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The key feature of a neural network is its ability to be "trained" to recognise patterns in data, allowing high efficiency algorithms to be developed with relative ease. This training is typically done with sample data which has been generated artificially, resulting in an algorithm that is very effective at recognising certain patterns in data sets. The only shortcoming is the danger of "over-training" an ANN, meaning that it becomes overly discriminating and searches across a narrower range of patterns than is desired (one countermeasure is to add extra noise to training data). | ||||||||
Changed: | ||||||||
< < | Computentp :- Simply running the code as above will result in less than optimal Neural Net training. The training procedure requires equal numbers of events from signal and from background (in this case it results in half of the signal events being used in training, half for testing). However, the above code will take events from the background signal samples in proportion to the file sizes - these result in proportions not quite in accordance with physical ratios. As the Neural Net weights results according to information about the cross-section of the process and so on stored in the tree, the final result is that while the outputs are weighted in a physical fashion, the Net is not trained to the same ratios, and so is not optimally trained. To solve this problem, Computentp is used to mix together all background and signal samples., and assign TrainWeights to them, so that the events are weighted correctly for the Net's training. | |||||||
> > | Computentp – Simply running the code as above will result in less than optimal Neural Net training. The training procedure requires equal numbers of events from signal and from background (in this case it results in half of the signal events being used in training, half for testing). However, the above code will take events from the background signal samples in proportion to the file sizes - these result in proportions not quite in accordance with physical ratios. As the Neural Net weights results according to information about the cross-section of the process and so on stored in the tree, the final result is that while the outputs are weighted in a physical fashion, the Net is not trained to the same ratios, and so is not optimally trained. To solve this problem, Computentp is used to mix together all background and signal samples and assign TrainWeights to them, so that the events are weighted correctly for the Net's training. | |||||||
Preparing samples for the Neural Net | ||||||||
Line: 45 to 45 | ||||||||
These cross-sections are for the overall process, at √s = 7 TeV. | ||||||||
Changed: | ||||||||
< < | The ttH sample cross-sections are provided for the overall process - the MC is divided into two samples with W+ and W- independent of one another. These two samples are merged before being put through the ANN. | |||||||
> > | The ttH sample cross-sections are provided for the overall process – the MC is divided into two samples with W+ and W- independent of one another. These two samples are merged before being put through the ANN. | |||||||
Changed: | ||||||||
< < | The tt samples were initially generated to produce the equivalent of 75 fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8 fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. | |||||||
> > | The tt samples were initially generated to produce the equivalent of 75 fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8 fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8 fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40 fb-1. Divide this by 40 to get the number of events in 1 fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 – so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy – simply divide by 40.8. You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. | |||||||
**IMPORTANT** The Filter Efficiency for these samples was calculated based on a no-pileup sample. The filter is generator level, and one of the things it will cut an event for is not enough jets. However, pileup adds jets, but these are added well after the filter. The net result is that a number of events that failed the filter would have passed, had the pileup been added earlier in the process. This means the filter efficiency (and thus the cross-sections) are incorrect, by a yet to determined amount.... |
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this will set up the environment for working in a v17 release of Athena, and it will make available the GlaNtp commands in the current working environment. | ||||||||
Changed: | ||||||||
< < | Note that an overview of the GlaNtp framework, made in doxygen, may be found here![]() | |||||||
> > | Note that an overview of the GlaNtp framework, made in doxygen, may be found here![]() | |||||||
Project Aims |
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The ttH sample cross-sections are provided for the overall process - the MC is divided into two samples with W+ and W- independent of one another. These two samples are merged before being put through the ANN. | ||||||||
Changed: | ||||||||
< < | The tt samples were initially generated to produce the equivalent of 75fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. | |||||||
> > | The tt samples were initially generated to produce the equivalent of 75 fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8 fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. | |||||||
**IMPORTANT** The Filter Efficiency for these samples was calculated based on a no-pileup sample. The filter is generator level, and one of the things it will cut an event for is not enough jets. However, pileup adds jets, but these are added well after the filter. The net result is that a number of events that failed the filter would have passed, had the pileup been added earlier in the process. This means the filter efficiency (and thus the cross-sections) are incorrect, by a yet to determined amount.... |
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source ./setup_glantp.sh -v 00-00-72 -b /afs/phas.gla.ac.uk/user/a/atlasmgr/physics/GlaNtp/ -s GlaNtp\ Packagev17 -a 17.0.5.5.2 | ||||||||
Changed: | ||||||||
< < | this will make available the GlaNtp commands in the current working environment. | |||||||
> > | this will set up the environment for working in a v17 release of Athena, and it will make available the GlaNtp commands in the current working environment. | |||||||
Note that an overview of the GlaNtp framework, made in doxygen, may be found here![]() |
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Computentp, Neural Nets and MCLIMITS | ||||||||
Changed: | ||||||||
< < | This page has been substantially rewritten (and remains a work in progress) to focus just on the information required for a successful run of the Computentp and Neural Net package, to deliver exclusions. For information on results obtained using inputs created in v12 of athena, please refer to the archive. This page also describes how to run on GlaNtp - the version of the code set up for use in Glasgow, with no CDF dependencies. To use the previous version of the code (there are some important differences) refer to r93 and earlier. | |||||||
> > | This page has been substantially rewritten (and remains a work in progress) to focus just on the information required for a successful run of the Computentp and Neural Net package, to deliver exclusions. For information on results obtained using inputs created in v12 of Athena, please refer to the archive. This page also describes how to run on GlaNtp – the version of the code set up for use in Glasgow, with no CDF dependencies. To use the previous version of the code (there are some important differences) refer to r93 and earlier. | |||||||
Changed: | ||||||||
< < | To set up the current version of GlaNtp, create a symbolic link to the setup script: | |||||||
> > | To set up the current version of GlaNtp on Glasgow AFS, create a symbolic link to the setup script: | |||||||
ln -s setup_glantp.sh /afs/phas.gla.ac.uk/user/a/atlasmgr/physics/GlaNtp/setup_glantp.sh | ||||||||
Line: 19 to 19 | ||||||||
this will make available the GlaNtp commands in the current working environment. | ||||||||
Added: | ||||||||
> > | Note that an overview of the GlaNtp framework, made in doxygen, may be found here![]() | |||||||
Project Aims |
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This page has been substantially rewritten (and remains a work in progress) to focus just on the information required for a successful run of the Computentp and Neural Net package, to deliver exclusions. For information on results obtained using inputs created in v12 of athena, please refer to the archive. This page also describes how to run on GlaNtp - the version of the code set up for use in Glasgow, with no CDF dependencies. To use the previous version of the code (there are some important differences) refer to r93 and earlier. | ||||||||
Added: | ||||||||
> > | To set up the current version of GlaNtp, create a symbolic link to the setup script:
ln -s setup_glantp.sh /afs/phas.gla.ac.uk/user/a/atlasmgr/physics/GlaNtp/setup_glantp.shthen set up the environment: source ./setup_glantp.sh -v 00-00-72 -b /afs/phas.gla.ac.uk/user/a/atlasmgr/physics/GlaNtp/ -s GlaNtp\ Packagev17 -a 17.0.5.5.2this will make available the GlaNtp commands in the current working environment. | |||||||
Project Aims |
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Getting a copy of GlaNtp | ||||||||
Changed: | ||||||||
< < |
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> > |
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source /data/ppe01/sl5x/x86_64/grid/glite-ui/latest/external/etc/profile.d/grid-env.sh
svn-grid-proxy-init
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Line: 207 to 207 | ||||||||
cd /home/ahgemmell/GlaNtp
svn co https://ppesvn.physics.gla.ac.uk/svn/atlas/GlaNtp/trunk/scripts/GlaNtpScript.sh![]() | ||||||||
Changed: | ||||||||
< < |
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> > |
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Variables used by the GlaNtp package | ||||||||
Changed: | ||||||||
< < | The variables used by the package can be divided into two sets. The first are those variables that are constant throughout the sample - the 'global' variables (e.g. cross-section of the sample). These can be specified in their own tree, where they will be recorded (and read by GlaNtp) once only. If desired, these variables can be defined within the main tree of the input file - however, then they will be recorded once per event, and read in once per event. This is obviously a bit wasteful, but for historical reasons it can be done. To determine which of these behaviours you use, set LoadGlobalOnEachEvent in FlatPlotter and FlatReader to 1 for the events to be read in on an event-by-event basis, or 0 to be read in once from the global tree (or from the first event only). For more information on this switch, refer to this![]() | |||||||
> > | The variables used by the package can be divided into two sets. The first are those variables that are constant throughout the sample - the 'global' variables (e.g. cross-section of the sample). These can be specified in their own tree, where they will be recorded (and read by GlaNtp) once only. If desired, these variables can be defined within the main tree of the input file - however, then they will be recorded once per event, and read in once per event. This is obviously a bit wasteful, but for historical reasons it can be done. To determine which of these behaviours you use, set LoadGlobalOnEachEvent in FlatPlotter and FlatReader to 1 for the events to be read in on an event-by-event basis, or 0 to be read in once from the global tree (or from the first event only). For more information on this switch, refer to this. The other variables are those that change on an event-by-event basis. These variables include both the variables we are going to train the Neural Net on (more information relevant to those variables is given in the relevant section of this TWiki), and other useful variables, such as filter flags (that tell GlaNtp whether an event is sensible or not). All of these variables are listed in the file VariableTreeToNTPATLASttHSemiLeptonic-v15.txt | |||||||
The file maps logical values to their branch/leaf. The tree can be the global tree or the event tree. | ||||||||
Line: 327 to 327 | ||||||||
Specifying files as Signal/Background or as real data | ||||||||
Changed: | ||||||||
< < | The input datasets need to be specified in a number of peripheral files, so that the ANN can distinguish between signal and background MC files or real data files. There is only one place where data files need to be specified differently to MC - FlatAtlastthPhysicsProc1.txt - and if you are running the fit with data and not pseudodata, this is determined through one single flag, set in genemflat - see here![]() | |||||||
> > | The input datasets need to be specified in a number of peripheral files, so that the ANN can distinguish between signal and background MC files or real data files. There is only one place where data files need to be specified differently to MC - FlatAtlastthPhysicsProc1.txt - and if you are running the fit with data and not pseudodata, this is determined through one single flag, set in genemflat - see here. Errors for each process also need to be specified - how this is done is detailed in that section. The relevant files for adding processes are atlastth_histlist_flat-v15.txt, AtlasttHRealTitles.txt, FlatAtlastthPhysicsProc1.txt and FlatSysSetAtlastth1.txt. There are also some files that are produced through the action of genemflat_batch_Complete2_SL5.sh. At several points in these files, there are common structures for inputting data, relating to ListParameter and ColumnParameter: | |||||||
ListParameter <tag> <onoff> <colon-separated-parameter-list> | ||||||||
Line: 691 to 691 | ||||||||
Computentp120.log | ||||||||
Changed: | ||||||||
< < | The log file from Computentp -- more information about the information contained within it is found here![]() | |||||||
> > | The log file from Computentp -- more information about the information contained within it is found here | |||||||
| ||||||||
Line: 756 to 756 | ||||||||
OnOff : 1 Process : 2.19001e-314 SorB : 0 | ||||||||
Changed: | ||||||||
< < | These reflect the parameters as set in TMVAsteer.txt (created via genemflat_batch_Complete2_SL5_sh![]() | |||||||
> > | These reflect the parameters as set in TMVAsteer.txt (created via genemflat_batch_Complete2_SL5_sh). Note that the number following 'Process' is nonsense (and in later releases of GlaNtp is not present) - that parameter is there in the steering file simply to make it more human-readable. However, the code still tries to read it in, but can only handle doubles - the net result varies from run to run, but can always be safely ignored. | |||||||
templates/out/FlatPlotter${prefix}.out |
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Line: 477 to 477 | ||||||||
This is just for the plotting scripts (but if you're training on variables, you should probably want them plotted as well...). The number after the SpecifyHist string (in this case 0) needs to be different for each entry. OnOff decides whether the variable is to be plotted or not, and must be specified. Min and Max specifiy the range of the x-axis (for energy / mass, this is in units of MeV), and unless specified defaults to 0 and 200 respectively. NBin specifies the number of bins in the histogram, with the default of 50. | ||||||||
Deleted: | ||||||||
< < | ATLAStthDiscrToLabel.txtListParameter DiscrToLabel:7 1 my_NN_BJet12_M:M^{BJet}_{12}\(MeV/c^{2}),MeV/c^{2}This is to make the axes titles in the plots easier on the eye. The ordering of variables and the numbering matches that in VariableTreeToNTPATLASttHSemiLeptonic-v16.txt. The final string is composed of two parts. The first is the variable name, and then after the semicolon comes the version you want on the plot axis, using root's LaTeX markup. | |||||||
TMVAvarset.txt (genemflat_batch_Complete2_SL5.sh)This is for the templating - a nice and simple list of all the variables you want to train on. Simples. | ||||||||
Line: 495 to 489 | ||||||||
Called by the FlatStack files. It allows for more instructive axes labels.
ListParameter DiscrToLabel:7 1 my_NN_BJet12_M:M^{BJet}_{12}\(MeV/c^{2}),MeV/c^{2} | ||||||||
Changed: | ||||||||
< < | The number number following DiscrToLabel must match that given in VariableTreeToNTPATLASttHSemiLeptonic-v16.txt. Then comes the real variable name - the name that the code deals with. Following the semicolon is the x-axis label, written in LaTeX style formatting. The backslash denotes a space in the axis label (the parameter must be one long continuous stream). The bit after the comma is optional, but if used specifies the units for the y-axis (e.g. # events per MeV). | |||||||
> > | The number number following DiscrToLabel must match that given in VariableTreeToNTPATLASttHSemiLeptonic-v16.txt. Then comes the real variable name - the name that the code deals with. Following the semicolon is the x-axis label, written in LaTeX style formatting. The backslash denotes a space in the axis label (the parameter must be one long continuous stream). The bit after the comma is optional, but if used specifies the units for the y-axis (e.g. # events per MeV). These labels are written using Root's LaTeX markup. | |||||||
Setting Systematic Uncertainties |
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teststeerFlatReaderATLAStthSemileptonic-v16.txtGeneralParameter string 1 ControlRegion=Higgs | ||||||||
Changed: | ||||||||
< < | Specifies which cutMask and invertWord are to be used from TreeSpecATLAStth_global.txt. This might be soon able to be altered from the command-line at runtime - check the section on running to see. | |||||||
> > | Specifies which cutMask and invertWord are to be used from TreeSpecATLAStth_global.txt. This is changed at runtime with one of the parameters | |||||||
RunningTo run the script, first log into the batch system (ppepbs). The genemflat_batch_Complete2_SL5.sh (NNFitter 00-00-21 version) script can be executed with the command (the last argument can be optional): | ||||||||
Changed: | ||||||||
< < | ./genemflat_batch_Complete2_SL5.sh 12 400 1.04 tth 120 120 6 00-00-45 /data/atlas07/stdenis/v16-r13/bjet2 agemmell@cern.ch srv001 ahgemmell ppepc23.physics.gla.ac.uk | |||||||
> > | ./genemflat_batch_Complete2_SL5.sh 12 400 1.04 tth 120 120 6 Higgs 00-00-45 /data/atlas07/stdenis/v16-r13/bjet2 agemmell@cern.ch srv001 ahgemmell ppepc23.physics.gla.ac.uk | |||||||
These options denote: | ||||||||
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teststeerFlatPlotterATLAStthSemileptonic-v16.txt | ||||||||
Changed: | ||||||||
< < | ColumnParameter SpecifyHist:my_NN_BJetWeight_Jet1 0 OnOff=1:Min=-5:Max=10 | |||||||
> > | ColumnParameter SpecifyHist:my_NN_BJetWeight_Jet1 0 OnOff=1:Min=-5:Max=10:NBin=25 | |||||||
Changed: | ||||||||
< < | This is just for the plotting scripts (but if you're training on variables, you should probably want them plotted as well...). The number after the SpecifyHist string (in this case 0) needs to be different for each entry. The following string is fairly self-explanatory. | |||||||
> > | This is just for the plotting scripts (but if you're training on variables, you should probably want them plotted as well...). The number after the SpecifyHist string (in this case 0) needs to be different for each entry. OnOff decides whether the variable is to be plotted or not, and must be specified. Min and Max specifiy the range of the x-axis (for energy / mass, this is in units of MeV), and unless specified defaults to 0 and 200 respectively. NBin specifies the number of bins in the histogram, with the default of 50. | |||||||
ATLAStthDiscrToLabel.txt | ||||||||
Line: 487 to 487 | ||||||||
This is for the templating - a nice and simple list of all the variables you want to train on. Simples. | ||||||||
Added: | ||||||||
> > | FlatStackInputSteer.txt / FlatStackInputSteerLog.txtParameters for the templating and the making of the stacked plots. Individual paramters are commented within the file itself.ATLAStthDiscrToLabel.txtCalled by the FlatStack files. It allows for more instructive axes labels.ListParameter DiscrToLabel:7 1 my_NN_BJet12_M:M^{BJet}_{12}\(MeV/c^{2}),MeV/c^{2}The number number following DiscrToLabel must match that given in VariableTreeToNTPATLASttHSemiLeptonic-v16.txt. Then comes the real variable name - the name that the code deals with. Following the semicolon is the x-axis label, written in LaTeX style formatting. The backslash denotes a space in the axis label (the parameter must be one long continuous stream). The bit after the comma is optional, but if used specifies the units for the y-axis (e.g. # events per MeV). | |||||||
Setting Systematic UncertaintiesThe fitting code can take into account two different types of systematic uncertainty - rate and shape. The basic method to obtain both these uncertainties is that you should make your input samples for both your nominal sample, and for the two bounds of a given error (e.g. Initial State Radiation, ISR). Repeat this for all of the errors you wish to consider. The rate systematic uncertainty is simply how the number of events change that pass your preselection cuts etc. (you can only consider this, if you like). To obtain the shape uncertainty, you should pass each of the resulting datasets through the ANN (up to and including the templating, so that you have ANN results for both the nominal results, and as a result of varying each background). These ANN outputs can then be used to produce the rate uncertainties based on their integrals, before being normalised to the nominal cross-section so as to find the shape uncertainty - a measure of the percentage change in the bin-by-bin distribution for each error. | ||||||||
Line: 576 to 586 | ||||||||
Filters | ||||||||
Changed: | ||||||||
< < | It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts - general cleaning cuts and the like. (There was a previous version of our inputs where we also required 'sensible states' - for each candidate event we required it to reconstruct tops and Ws with vaguely realistic masses. However - this is a Neural Net analysis, so it has been decided to remove these cuts - they will after all in effect be reintroduced by the net itself if they would have been useful, and by not applying them ourselves, we are passing more information to the net.) We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in TMVAsteer.txt (created in genemflat_batch_Complete2.sh) (not currently used, as explained below) and TreeSpecATLAStth.txt. | |||||||
> > | It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts - general cleaning cuts and the like. (There was a previous version of our inputs where we also required 'sensible states' - for each candidate event we required it to reconstruct tops and Ws with vaguely realistic masses. However - this is a Neural Net analysis, so it has been decided to remove these cuts - they will after all in effect be reintroduced by the net itself if they would have been useful, and by not applying them ourselves, we are passing more information to the net.) We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in TMVAsteer.txt (created in genemflat_batch_Complete2.sh) (not currently used, as explained below) and TreeSpecATLAStth_global.txt. | |||||||
VariableTreeToNTPATLASttHSemiLeptonic-v16.txt | ||||||||
Line: 584 to 594 | ||||||||
This sets the variable we wish to use in our filter - it interfaces with the cutMask and invertWord as specified in TreeSpecATLAStth.txt. Note that depending on the number of jets you wish to run your analysis on (set as a command line argument during the running of the script), this is edited with genemflat. | ||||||||
Changed: | ||||||||
< < | TreeSpecATLAStth.txt | |||||||
> > | TreeSpecATLAStth_global.txt | |||||||
In TreeSpecATLAStth.txt, we establish the filters which control what is used for the templating, and Computentp:
ListParameter SpecifyVariable:Higgs:cutMask 1 Type:int:Default:3 ListParameter SpecifyVariable:Higgs:invertWord 1 Type:int:Default:0 | ||||||||
Changed: | ||||||||
< < | InvertWord is used to invert the relevant bits (in this case no bits are inverted) before the cut from cutMask is applied. The cutMask tells the filter which bits we care about (we use a binary filter). So, for example, if cutMask is set to 6 (110 in binary), we are telling the filter that we wish the second and third bit to be equal to one in cutWord - we don't care about the first bit. | |||||||
> > | InvertWord is used to invert the relevant bits (in this case no bits are inverted) before the cut from cutMask is applied. The cutMask tells the filter which bits we care about (we use a binary filter). So, for example, if cutMask is set to 6 (110 in binary), we are telling the filter that we wish the second and third bit to be equal to one in cutWord - we don't care about the first bit. It is possible to specify multiple options of the cutMask and invertWord in the same file, distinguished by the word after SpecifyVariable (in this case Higgs). Which ones are used are determined by teststeerFlatReaderATLAStthSemileptonic-v16.txt. | |||||||
TMVAsteer.txt (genemflat_batch_Complete2.sh) | ||||||||
Line: 601 to 611 | ||||||||
**If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints.** | ||||||||
Added: | ||||||||
> > | teststeerFlatReaderATLAStthSemileptonic-v16.txtGeneralParameter string 1 ControlRegion=HiggsSpecifies which cutMask and invertWord are to be used from TreeSpecATLAStth_global.txt. This might be soon able to be altered from the command-line at runtime - check the section on running to see. | |||||||
RunningTo run the script, first log into the batch system (ppepbs). |
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Setting which variables to plot and train on | ||||||||
Changed: | ||||||||
< < | You need to let GlaNtp know where tha variables you are interested in are. It is also possible to merely plot some variables you're interested in without adding them to the training just yet. You need to provide GlaNtp with the location of the variables in all cases - this is done in VariableTreeToNTPATLASttHSemiLeptonic-v16.txt and TreeSpecATLAStth-v16_event.txt (or TreeSpecATLAStth-v16_global.txt as applicable). | |||||||
> > | You need to let GlaNtp know where the variables you are interested in are. It is also possible to merely plot some variables you're interested in without adding them to the training just yet. You need to provide GlaNtp with the location of the variables in all cases - this is done in VariableTreeToNTPATLASttHSemiLeptonic-v16.txt and TreeSpecATLAStth-v16_event.txt (or TreeSpecATLAStth-v16_global.txt as applicable). | |||||||
VariableTreeToNTPATLASttHSemiLeptonic-v16.txt | ||||||||
Line: 475 to 475 | ||||||||
ColumnParameter SpecifyHist:my_NN_BJetWeight_Jet1 0 OnOff=1:Min=-5:Max=10 | ||||||||
Changed: | ||||||||
< < | This is just for the plotting scripts (but if you're training on variables, you should probably want them plotted as well...). The number after the SpecifyHist string (in this case 0) needs to be different for each entry. The following string is fairly self-explanatory | |||||||
> > | This is just for the plotting scripts (but if you're training on variables, you should probably want them plotted as well...). The number after the SpecifyHist string (in this case 0) needs to be different for each entry. The following string is fairly self-explanatory.
ATLAStthDiscrToLabel.txtListParameter DiscrToLabel:7 1 my_NN_BJet12_M:M^{BJet}_{12}\(MeV/c^{2}),MeV/c^{2}This is to make the axes titles in the plots easier on the eye. The ordering of variables and the numbering matches that in VariableTreeToNTPATLASttHSemiLeptonic-v16.txt. The final string is composed of two parts. The first is the variable name, and then after the semicolon comes the version you want on the plot axis, using root's LaTeX markup. | |||||||
TMVAvarset.txt (genemflat_batch_Complete2_SL5.sh) |
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To run the script, first log into the batch system (ppepbs). | ||||||||
Changed: | ||||||||
< < | The genemflat_batch_Complete2_SL5.sh (NNFitter 00-00-21 version) script can be executed with the command:
./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 6 00-00-17 /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed | |||||||
> > | The genemflat_batch_Complete2_SL5.sh (NNFitter 00-00-21 version) script can be executed with the command (the last argument can be optional):
./genemflat_batch_Complete2_SL5.sh 12 400 1.04 tth 120 120 6 00-00-45 /data/atlas07/stdenis/v16-r13/bjet2 agemmell@cern.ch srv001 ahgemmell ppepc23.physics.gla.ac.uk | |||||||
These options denote:
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NLOAccep : -0.0627014 0.727165 pdf : -0.0106981 0.99073 xsec : 0.00620052 0.923289 | ||||||||
Changed: | ||||||||
< < | These values come from a Minuit fit, so should be taken with a pinch of salt. The 'Value' compares the results of the pseudoexperiment for all the various errors, and compares it to what you told it. E.g. if you said you had 1fb-1 for luminosity, but the pseudodata suggested a luminosity of 1.01, then Value would be 0.01 - you are 'out' by 1%. 'Error' says how much of your proposed error you have 'used' - if you say you have a 10% error on your luminosity, but the fit suggests at 1% error, then 'Error' would be 0.10 - you are using 10% of your 'allowed' error. | |||||||
> > | These values come from a Minuit fit, so should be taken with a pinch of salt. The 'Value' compares the results of the pseudoexperiment for all the various errors, and compares it to what you told it. E.g. if you said you had 1fb-1 for luminosity, but the pseudodata suggested a luminosity of 1.01, then Value would be 0.01 - you are 'out' by 1%. 'Error' says how much of your proposed error you have 'used' - if you say you have a 10% error on your luminosity, but the fit suggests at 1% error, then 'Error' would be 0.10 - you are using 10% of your 'allowed' error. These are calculated against data, seeing how much the various backgrounds are allowed to vary according to the systematics before they are no longer compatible with data. | |||||||
drivetestFlatFitAtlastth.rootUnscaledTemplates.root. |
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> > | Est_12_120.eps<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> | |||||||
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Overview of the process | ||||||||
Changed: | ||||||||
< < | ![]() | |||||||
> > | ![]() | |||||||
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Line: 209 to 209 | ||||||||
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HwwFlatFitATLAS Validation succeeded Done with core tests | ||||||||
Line: 247 to 247 | ||||||||
Result of FlatAscii validation: OK Result of FlatAscii_global validation: OK Result of FlatTRntp validation: OK | ||||||||
Changed: | ||||||||
< < | ||||||||
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Variables used by the GlaNtp package | ||||||||
Line: 268 to 268 | ||||||||
ListParameter EvInfoTree:1 1 NN_BJetWeight_Jet1:NN_BJetWeight_Jet1/NN_BJetWeight_Jet1 | ||||||||
Deleted: | ||||||||
< < | If you want a parameter to be found in the output, best to list it here.... | |||||||
Added: | ||||||||
> > | If you want a parameter to be found in the output, best to list it here.... | |||||||
Calculating my_integral (The Magic Formula) | ||||||||
Changed: | ||||||||
< < | 1. Check the critical formula: The most important forumla is the first thing to check: weight*= GetSF()*GetXsect()*GetBrFrac()*GetFilterEff()*GetLumiForType()/GetNGenForType(); my_brFrac my_filterEff my_xSect my_lumiForType my_nGenForType | |||||||
> > | 1. Check the critical formula: The most important forumla is the first thing to check: | |||||||
Added: | ||||||||
> > | weight*= GetSF()*GetXsect()*GetBrFrac()*GetFilterEff()*GetLumiForType()/GetNGenForType(); my_brFrac my_filterEff my_xSect my_lumiForType my_nGenForType | |||||||
You need a scale factor: my_sf? | ||||||||
Added: | ||||||||
> > | 2. Check the stuff that FlatReader uses. This is documented in GlaNtp/NtpAna/test/VariableTreeToNtp.txt. Variable Tree to Ntp is the one that maps logical values to their physical branch/leaf. Anything prefaced with FlatTupleVar needs to be specified or is useful to specify. | |||||||
Changed: | ||||||||
< < | 2. Check the stuff that FlatReader uses. This is documented in GlaNtp/NtpAna/test/VariableTreeToNtp.txt. Variable Tree to Ntp is the one that maps logical values to their physical branch/leaf. Anything prefaced with FlatTupleVar needs to be specified or is useful to specify. Values are divided in to those that can change on each event (kept in the "ev" tree) and those that are the same for a file (kept in the "global" tree). As you know you can set the tree names. You really should create a global tree for the global file values now. We have procrastinated on this a long time. | |||||||
> > | Values are divided in to those that can change on each event (kept in the "ev" tree) and those that are the same for a file (kept in the "global" tree). As you know you can set the tree names. You really should create a global tree for the global file values now. We have procrastinated on this a long time. | |||||||
Changed: | ||||||||
< < | There you see # # Values that are required from global # GeneralParameter string 1 FlatTupleVar/BrFrac=globalInfo/BrFrac GeneralParameter string 1 FlatTupleVar/FilterEff=globalInfo/FilterEff GeneralParameter string 1 FlatTupleVar/Fraction=Fraction/Fraction GeneralParameter string 1 FlatTupleVar/Integral=Integral/Integral GeneralParameter string 1 FlatTupleVar/XSect=globalInfo/Xsect | |||||||
> > | There you see # # Values that are required from global # GeneralParameter string 1 FlatTupleVar/BrFrac=globalInfo/BrFrac GeneralParameter string 1 FlatTupleVar/FilterEff=globalInfo/FilterEff GeneralParameter string 1 FlatTupleVar/Fraction=Fraction/Fraction GeneralParameter string 1 FlatTupleVar/Integral=Integral/Integral GeneralParameter string 1 FlatTupleVar/XSect=globalInfo/Xsect | |||||||
Changed: | ||||||||
< < | # This specifies the name of the leaf for the cutmask and invert word. #Again, these are global values for a file. GeneralParameter string 1 CutMaskString=cutMask GeneralParameter string 1 InvertWordString=invertWord | |||||||
> > | # This specifies the name of the leaf for the cutmask and invert word. #Again, these are global values for a file. GeneralParameter string 1 CutMaskString=cutMask GeneralParameter string 1 InvertWordString=invertWord | |||||||
Changed: | ||||||||
< < | This confirms that Fraction and Integral are needed. my_fraction my_integral | |||||||
> > | This confirms that Fraction and Integral are needed. my_fraction my_integral | |||||||
Changed: | ||||||||
< < | Here are the ones that are required for ev: # # Values that are required from ev # GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll GeneralParameter string 1 FlatTupleVar/Entry=evInfo/ientry GeneralParameter string 1 FlatTupleVar/Event=evInfo/eventNumber GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E GeneralParameter string 1 FlatTupleVar/LumiForType=evInfo/lumiForType GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW GeneralParameter string 1 FlatTupleVar/NGenForType=evInfo/nGenForType GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand GeneralParameter string 1 FlatTupleVar/Run=evInfo/runNumber GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight GeneralParameter string 1 FlatTupleVar/cutWord=evInfo/cutWord GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type GeneralParameter string 1 FlatTupleVar/sf=evInfo/sf Some can be used with the default values that FlatTuple gives: GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type Some are ok to leave if you dont want to use it: there are switches that turn on the use of these GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight | |||||||
> > | Here are the ones that are required for ev: # # Values that are required from ev # GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll GeneralParameter string 1 FlatTupleVar/Entry=evInfo/ientry GeneralParameter string 1 FlatTupleVar/Event=evInfo/eventNumber GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E GeneralParameter string 1 FlatTupleVar/LumiForType=evInfo/lumiForType GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW GeneralParameter string 1 FlatTupleVar/NGenForType=evInfo/nGenForType GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand GeneralParameter string 1 FlatTupleVar/Run=evInfo/runNumber GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight GeneralParameter string 1 FlatTupleVar/cutWord=evInfo/cutWord GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type GeneralParameter string 1 FlatTupleVar/sf=evInfo/sf | |||||||
Changed: | ||||||||
< < | Some are useful for plotting: GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets | |||||||
> > | Some can be used with the default values that FlatTuple gives: GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type | |||||||
Added: | ||||||||
> > | Some are ok to leave if you dont want to use it: there are switches that turn on the use of these GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight | |||||||
Changed: | ||||||||
< < | I think you have my_Eventtype as channel my_failEvent as cutword | |||||||
> > | Some are useful for plotting: GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets | |||||||
Added: | ||||||||
> > | I think you have my_Eventtype as channel my_failEvent as cutword | |||||||
Variables that must be listed in the event (not the global) tree | ||||||||
Line: 477 to 410 | ||||||||
ColumnParameter SignalList 1 ttH=1 ColumnParameter DataList 1 Data=11 | ||||||||
Changed: | ||||||||
< < | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList, SignalList or DataList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but it must be sequential, running from 0 to n-1 (where you have n samples) - apart from for DataList entries (as shown above). It also does not need to correspond to my_Eventtype, however, for completeness' sake within this file I have set it as such. The number at the end of this declaration (tt0j=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). It must match up with the numbers provided in atlastth_histlist_flat-v16.txt and AtlasttHRealTitles.txt so that processes and data can be matched to the various individual files. | |||||||
> > | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList, SignalList or DataList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but it must be sequential, running from 0 to n-1 (where you have n samples) - apart from for DataList entries (as shown above). It also does not need to correspond to my_Eventtype, however, for completeness' sake within this file I have set it as such. The number at the end of this declaration (tt0j=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). It must match up with the numbers provided in atlastth_histlist_flat-v16.txt and AtlasttHRealTitles.txt so that processes and data can be matched to the various individual files. | |||||||
ColumnParameter PseudoDataList 0 tt0j=0 | ||||||||
Line: 729 to 663 | ||||||||
teststeerFlatPlotterATLAStthSemileptonic-v16.txt and teststeerFlatReaderATLAStthSemileptonic-v16.txtGeneralParameter bool 1 LoadGlobalOnEachEvent=0 | ||||||||
Added: | ||||||||
> > | ||||||||
Determines if you have a separate global tree or not. If you do not, set this equal to one, and the relevant global values will be read out anew for each event from the event tree.
Important notes about running parts of the code (not a complete run - for debugging, replotting etc) | ||||||||
Changed: | ||||||||
< < |
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> > |
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Where the output is stored | ||||||||
Line: 805 to 738 | ||||||||
OnOff : 1 Process : 2.19001e-314 SorB : 0 | ||||||||
Changed: | ||||||||
< < |
These reflect the parameters as set in TMVAsteer.txt (created via genemflat_batch_Complete2_SL5_sh![]() | |||||||
> > | These reflect the parameters as set in TMVAsteer.txt (created via genemflat_batch_Complete2_SL5_sh![]() | |||||||
templates/out/FlatPlotter${prefix}.out | ||||||||
Line: 896 to 827 | ||||||||
Channel: SemiLeptonic(0) Process: eFake(10) ib= 1 0.88551 1.50073 wgt= 0.88551 wgtE= 1.50073 wgtEsum2= 2.2522 | ||||||||
Changed: | ||||||||
< < | The first number is the bin number being considered. wgt is the weighted integral of that bin, and all preceding bins (i.e. the total integral up to that point) | |||||||
> > | The first number is the bin number being considered. wgt is the weighted integral of that bin, and all preceding bins (i.e. the total integral up to that point) | |||||||
Immediately following this is the record of generating the first pseudoexperiment. It lists the weighted contents of each of the bins of a neural net histogram, assuming background only, with poisson fluctuations. It then gives the integral of this pseudoexperiment: | ||||||||
Changed: | ||||||||
< < | Pseudodata Integral: 11506For obvious reasons this should be similar to the projected background yield. | |||||||
> > | Pseudodata Integral: 11506For obvious reasons this should be similar to the projected background yield. | |||||||
Later on, at the start of the fitting we also have the following: | ||||||||
Line: 916 to 845 | ||||||||
NLOAccep : -0.0627014 0.727165 pdf : -0.0106981 0.99073 xsec : 0.00620052 0.923289 | ||||||||
Changed: | ||||||||
< < | These values come from a Minuit fit, so should be taken with a pinch of salt. The 'Value' compares the results of the pseudoexperiment for all the various errors, and compares it to what you told it. E.g. if you said you had 1fb-1 for luminosity, but the pseudodata suggested a luminosity of 1.01, then Value would be 0.01 - you are 'out' by 1%. 'Error' says how much of your proposed error you have 'used' - if you say you have a 10% error on your luminosity, but the fit suggests at 1% error, then 'Error' would be 0.10 - you are using 10% of your 'allowed' error. | |||||||
> > | These values come from a Minuit fit, so should be taken with a pinch of salt. The 'Value' compares the results of the pseudoexperiment for all the various errors, and compares it to what you told it. E.g. if you said you had 1fb-1 for luminosity, but the pseudodata suggested a luminosity of 1.01, then Value would be 0.01 - you are 'out' by 1%. 'Error' says how much of your proposed error you have 'used' - if you say you have a 10% error on your luminosity, but the fit suggests at 1% error, then 'Error' would be 0.10 - you are using 10% of your 'allowed' error. | |||||||
drivetestFlatFitAtlastth.rootUnscaledTemplates.root. | ||||||||
Line: 949 to 876 | ||||||||
ttH /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/ttH-v16.root 120 1 556 556 1 1 | ||||||||
Changed: | ||||||||
< < | Some of the values are established through steerComputentp.txt in the line ListParameter Process:ttH 1 Filename:/data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/ttH-v16.root:File:120:IntLumi:1.0 | |||||||
> > | Some of the values are established through steerComputentp.txt in the line
ListParameter Process:ttH 1 Filename:/data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/ttH-v16.root:File:120:IntLumi:1.0 | |||||||
Changed: | ||||||||
< < |
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> > |
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Limitations | ||||||||
Line: 1108 to 1024 | ||||||||
WtEvents:01 Passing Mask Selection for Higgs : 2.59829 | ||||||||
Changed: | ||||||||
< < | These entries correspond to the yields - the numbers of events expected in our specified luminosity. | |||||||
> > | These entries correspond to the yields - the numbers of events expected in our specified luminosity. | |||||||
If you want to get more debugging from Computentp, then run it with another argument (doesn't matter what the argument is - in the example below it's simply 1): | ||||||||
Line: 1146 to 1062 | ||||||||
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Added: | ||||||||
> > |
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Line: 1 to 1 | ||||||||
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Line: 121 to 121 | ||||||||
In the script used to make the webpage showing the results, the reference to H6AONN5MEMLP is hardwired. It should become a argument. It is the name of the method you give TMVA in the training, and so if it changes in one you should be able to change in the other
Overview of the process | ||||||||
Added: | ||||||||
> > |
![]() | |||||||
|
Line: 1 to 1 | ||||||||
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Line: 969 to 969 | ||||||||
Limitations
| ||||||||
Deleted: | ||||||||
< < |
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Line: 1 to 1 | ||||||||
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Line: 1033 to 1033 | ||||||||
At any point you can check that the a given steering file can be read by GlaNtp by using testSteerrv5.exe - found inside your GlaNtp package: | ||||||||
Changed: | ||||||||
< < | ./${GLANTPDIR}/bin/Linux2.6-GCC_4_1/testSteerrv5.exe | |||||||
> > | testSteerrv5.exe | |||||||
Deleted: | ||||||||
< < | Please note that the Linux2.6-GCC_4_1may change depending on your system architecture. It is set during the installation of GlaNtp, and can be checked with the command echo $BFARCH | |||||||
Another debugging script checks you have defined the processes correctly: |
Line: 1 to 1 | ||||||||
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Line: 519 to 519 | ||||||||
FirstEvent and LastEvent allow you to specify a range of events to run over - this is liable only to be useful during debugging. (Note that these parameters are currently turned off). NEvent gives the maximum number of events processed for any given sample - take care with this, if you are running a particularly large sample through the code.... | ||||||||
Added: | ||||||||
> > | Setting which variables to plot and train onYou need to let GlaNtp know where tha variables you are interested in are. It is also possible to merely plot some variables you're interested in without adding them to the training just yet. You need to provide GlaNtp with the location of the variables in all cases - this is done in VariableTreeToNTPATLASttHSemiLeptonic-v16.txt and TreeSpecATLAStth-v16_event.txt (or TreeSpecATLAStth-v16_global.txt as applicable).VariableTreeToNTPATLASttHSemiLeptonic-v16.txtListParameter EvInfoTree:1 1 my_NN_BJetWeight_Jet1:my_NN_BJetWeight_Jet1/my_NN_BJetWeight_Jet1This information must be provided for every variable you're interested in in any way. It provides the variable name, and a map to that variable name from the input tree. Note that the number after EvInfoTree must be unique for each entry (EvInfoTree:2, EvInfoTree:3, etc) TreeSpecATLAStth-v16_event.txtListParameter SpecifyVariable:my_NN_BJetWeight_Jet1 1 Type:doubleThis is another compulsory piece of information for GlaNtp - telling it which tree the information is in (event or global) and the event type. teststeerFlatPlotterATLAStthSemileptonic-v16.txtColumnParameter SpecifyHist:my_NN_BJetWeight_Jet1 0 OnOff=1:Min=-5:Max=10This is just for the plotting scripts (but if you're training on variables, you should probably want them plotted as well...). The number after the SpecifyHist string (in this case 0) needs to be different for each entry. The following string is fairly self-explanatory TMVAvarset.txt (genemflat_batch_Complete2_SL5.sh)This is for the templating - a nice and simple list of all the variables you want to train on. Simples. | |||||||
Setting Systematic UncertaintiesThe fitting code can take into account two different types of systematic uncertainty - rate and shape. The basic method to obtain both these uncertainties is that you should make your input samples for both your nominal sample, and for the two bounds of a given error (e.g. Initial State Radiation, ISR). Repeat this for all of the errors you wish to consider. The rate systematic uncertainty is simply how the number of events change that pass your preselection cuts etc. (you can only consider this, if you like). To obtain the shape uncertainty, you should pass each of the resulting datasets through the ANN (up to and including the templating, so that you have ANN results for both the nominal results, and as a result of varying each background). These ANN outputs can then be used to produce the rate uncertainties based on their integrals, before being normalised to the nominal cross-section so as to find the shape uncertainty - a measure of the percentage change in the bin-by-bin distribution for each error. |
Line: 1 to 1 | ||||||||
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Line: 410 to 410 | ||||||||
atlastth_hislist_flat-v15.txt | ||||||||
Changed: | ||||||||
< < | This file provides a map for the ANN, giving it the output file names (and in which directory they are to be stored, relative to ${template_area} - set in genemflat) and the tree structure where the final result of the ANN will be stored in the output (in the example below, the output file is ${template_area}/11602-filter.root, and the result graph will be FlatPlotter/NNScoreAny_0_0_0 0). The number to the left of the file name indicates which process it is - this is established using a file called TMVAsteer.txt, which is created through the running of genemflat_batch_Complete2_SL5.sh), and corresponds to the variable my_Eventtype in the input files (this can also be influenced in genemflat). Thus it is possible to assign multiple files to the same process (e.g. a file for the electron stream and muon stream are both assigned to Data), by giving them a common number at the start of the line. | |||||||
> > | This file provides a map for the ANN, giving it the output file names (and in which directory they are to be stored, relative to ${template_area} - set in genemflat) and the tree structure where the final result of the ANN will be stored in the output (in the example below, the output file is ${template_area}/11602-filter.root, and the result graph will be FlatPlotter/NNScoreAny_0_0_0 0). The number to the left of the file name indicates which process it is - this is also used in the files AtlasttHRealTitles.txt and FlatAtlastthPhysicsProc1.txt, and needs to be consistent with them), and corresponds to the variable my_Eventtype in the input files (this can also be influenced in genemflat). Thus it is possible to assign multiple files to the same process (e.g. a file for the electron stream and muon stream are both assigned to Data), by giving them a common number at the start of the line. | |||||||
0 116102-filter.root FlatPlotter/NNScoreAny_0_0_0 0 | ||||||||
Line: 424 to 424 | ||||||||
AtlasttHRealTitles.txt | ||||||||
Changed: | ||||||||
< < | The list of signal/background processes can be found in AtlasttHRealTitles.txt (where the names are specified and associated with numbers). At present these are: | |||||||
> > | The list of signal/background processes can be found in AtlasttHRealTitles.txt (where the names are specified and associated with process numbers - these process/number associations need to be the same as in atlastth_hislist_flat-v15.txt and FlatAtlastthPhysicsProc1.txt). At present these are: | |||||||
Process_0_0 TTjj:Semileptonic | ||||||||
Line: 443 to 443 | ||||||||
GeneralParameter string 1 FileString=my_Eventtype | ||||||||
Changed: | ||||||||
< < | Indicates the leaf in the input file which shows which process the event belongs to - this is the same number as we've specified as in atlastth_hislist_flat-v15.txt and AtlasttHRealTitles.txt. | |||||||
> > | Indicates the leaf in the input file which shows which process the event belongs to - this is the same number as we specify later in genemflat for steerComputentp.txt - it does not have to be consistent with the process numbers as defined in atlastth_hislist_flat-v15.txt, AtlasttHRealTitles.txt and FlatAtlastthPhysicsProc1.txt. | |||||||
ColumnParameter File 1 OnOff=1:SorB=1:Process=tth | ||||||||
Line: 474 to 474 | ||||||||
ColumnParameter SignalList 1 ttH=1 ColumnParameter DataList 1 Data=11 | ||||||||
Changed: | ||||||||
< < | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList, SignalList or DataList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but it must be sequential, running from 0 to n-1 (where you have n samples) - apart from for DataList entries (as shown above). It also does not need to correspond to my_Eventtype, however, for completeness' sake within this file I have set it as such. The number at the end of this declaration (tt0j=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). It must match up with the numbers provided in atlastth_histlist_flat-v16.txt so that processes and data can be matched to the various individual files. | |||||||
> > | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList, SignalList or DataList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but it must be sequential, running from 0 to n-1 (where you have n samples) - apart from for DataList entries (as shown above). It also does not need to correspond to my_Eventtype, however, for completeness' sake within this file I have set it as such. The number at the end of this declaration (tt0j=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). It must match up with the numbers provided in atlastth_histlist_flat-v16.txt and AtlasttHRealTitles.txt so that processes and data can be matched to the various individual files. | |||||||
ColumnParameter PseudoDataList 0 tt0j=0 | ||||||||
Line: 1001 to 1001 | ||||||||
Before trying debugging, you should set up the environment in your terminal (when running the code normally, this is done automatically by tr${run}.job). This can be done by sourcing setup_glantp.sh, which automates setting of the relevant paths - remember to specify the release number of GlaNtp that you have in your area: | ||||||||
Changed: | ||||||||
< < | source setup_glantp.sh 00-00-17 | |||||||
> > | source setup_glantp.sh 00-00-32 | |||||||
At any point you can check that the a given steering file can be read by GlaNtp by using testSteerrv5.exe - found inside your GlaNtp package: | ||||||||
Line: 1012 to 1012 | ||||||||
Please note that the Linux2.6-GCC_4_1may change depending on your system architecture. It is set during the installation of GlaNtp, and can be checked with the command echo $BFARCH | ||||||||
Added: | ||||||||
> > | Another debugging script checks you have defined the processes correctly:
testFlatProcessInforv5.exe FlatAtlastthPhysicsProc1.txtThe output from the near the end of this is the important bit: Table of what category each process is falling under IP : PN : LB : B : D : S : P : O 0 : tt : t#bar{t} : 1 : 0 : 0 : 1 : 0IP is something or other (need to ask Rick to remind me), PN is process name, LB is the label of the process. B, D and S are whether or not the process is Background, Data or Signal respectively. P is whether or not that process is included in the manufacture of Pseudoexperiments, and O is the order in which that process is plotted. | |||||||
To debug the code further, two things need to be done - first, all the debug switches need to be turned on, and then you need to restrict the number of events to ~10 (for a Computentp run this will still manage to generate a 2 GB log file!). All of these switches are found in teststeerFlatReaderATLAStthSemileptonic.txt (the progenitor for all FlatReader files) and steerComputentp.txt (created by genemflat). The debug switches are:
GeneralParameter bool 1 Debug=0 GeneralParameter bool 1 DebugGlobalInfo=0 |
Line: 1 to 1 | ||||||||
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Line: 778 to 778 | ||||||||
templates/out/FlatPlotter${prefix}.out | ||||||||
Changed: | ||||||||
< < | The output from the screen from the templating stage, where the Neural Net scores are actually produced, detailing errors etc. | |||||||
> > | (Where ${prefix} is the process name.) The output from the screen from the templating stage, where the Neural Net scores are actually produced, detailing errors etc. It also contains a more detailed breakdown of the event numbers and the yields (event numbers in the specified luminosity) at various points:
****** FlatReader Info Start ****** Entries examined : 2884 Events:00 Seen : 2884 Events:00 Seen Any : 2884 Events:01 Passing Mask Selection for Higgs : 1800 Events:01 Passing Mask Selection for Higgs Any : 1800 Events:02 Passing DilPairType Selection for ALL : 1800 Events:02 Passing DilPairType Selection for ALL Any : 1800 Events:30 Passing Selection : 1800 Events:30 Passing Selection Any : 1800 Events:40 Passing Bad Lepton Energy Filter : 1800 Events:40 Passing Bad Lepton Energy Filter Any : 1800 Events:9999 Final Selection : 1800 Events:9999 Final Selection Any : 1800 No precomputed weight: Weight Difference not checked : 148336 UPEvents: 00 Seen : 2884 UPEvents: 00 SeenAny : 2884 UPEvents:30 Passing Selection : 1800 UPEvents:30 Passing SelectionAny : 1800 UPWtEvents: 00 Seen : -2115.31 UPWtEvents: 00 SeenAny : -2115.31 UPWtEvents:30 Passing Selection : -1318.36 UPWtEvents:30 Passing SelectionAny : -1318.36 WtEvents:00 Seen : -2115.31 WtEvents:00 Seen Any : -2115.31 WtEvents:01 Passing Mask Selection for Higgs : -1318.36 WtEvents:01 Passing Mask Selection for Higgs Any : -1318.36 WtEvents:02 Passing DilPairType Selection for ALL : -1318.36 WtEvents:02 Passing DilPairType Selection for ALL Any : -1318.36 WtEvents:30 Passing Selection : -1318.36 WtEvents:30 Passing Selection Any : -1318.36 WtEvents:40 Passing Bad Lepton Energy Filter : -1318.36 WtEvents:40 Passing Bad Lepton Energy Filter Any : -1318.36 WtEvents:9999 Final Selection : -1318.36 WtEvents:9999 Final Selection Any : -1318.36 ****** FlatReader Info End ******In 'Events', the first number, the entries examined, is the number of entries in the MC sample being passed to the Neural Net. The Final Selection is the number of entries that make it past your cutmask etc to actually be passed to the Neural Net. The intervening numbers are at this stage rather meaningless. 'UPWtEvents' can be safely ignored in its entirety. 'WtEvents' provides the same numbers as in 'Events', but this time with Scale Weights etc applied - these are now the yields. | |||||||
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To set up a neural net for the analysis of a particular kind of data it is necessary to train it with sample data; this process will adjust the "weights" on each variable that the neural net analyses in the ntuple, in order to optimise performance. These weights can then be viewed as a scatter plot in ROOT. | ||||||||
Changed: | ||||||||
< < | Specifying files as Signal/Background | |||||||
> > | Specifying files as Signal/Background or as real data | |||||||
Changed: | ||||||||
< < | The input datasets need to be specified in a number of peripheral files, so that the ANN can distinguish between signal and background files. Errors for each process also need to be specified - how this is done is detailed in that section. The relevant files for adding processes are atlastth_histlist_flat-v15.txt, AtlasttHRealTitles.txt, FlatAtlastthPhysicsProc1.txt and FlatSysSetAtlastth1.txt. There are also some files that are produced through the action of genemflat_batch_Complete2_SL5.sh. At several points in these files, there are common structures for inputting data, relating to ListParameter and ColumnParameter: | |||||||
> > | The input datasets need to be specified in a number of peripheral files, so that the ANN can distinguish between signal and background MC files or real data files. There is only one place where data files need to be specified differently to MC - FlatAtlastthPhysicsProc1.txt - and if you are running the fit with data and not pseudodata, this is determined through one single flag, set in genemflat - see here![]() | |||||||
ListParameter <tag> <onoff> <colon-separated-parameter-list> | ||||||||
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atlastth_hislist_flat-v15.txt | ||||||||
Changed: | ||||||||
< < | This file provides a map for the ANN, giving it the output file names (and in which directory they are to be stored, relative to ${template_area} - set in genemflat) and the tree structure where the final result of the ANN will be stored in the output (in the example below, the output file is ${template_area}/11602-filter.root, and the result graph will be FlatPlotter/NNScoreAny_0_0_0 0). The number to the left of the file name indicates which process it is - this is established using a file called TMVAsteer.txt, which is created through the running of genemflat_batch_Complete2_SL5.sh), and corresponds to the variable my_Eventtype in the input files (this can also be influenced in genemflat). | |||||||
> > | This file provides a map for the ANN, giving it the output file names (and in which directory they are to be stored, relative to ${template_area} - set in genemflat) and the tree structure where the final result of the ANN will be stored in the output (in the example below, the output file is ${template_area}/11602-filter.root, and the result graph will be FlatPlotter/NNScoreAny_0_0_0 0). The number to the left of the file name indicates which process it is - this is established using a file called TMVAsteer.txt, which is created through the running of genemflat_batch_Complete2_SL5.sh), and corresponds to the variable my_Eventtype in the input files (this can also be influenced in genemflat). Thus it is possible to assign multiple files to the same process (e.g. a file for the electron stream and muon stream are both assigned to Data), by giving them a common number at the start of the line. | |||||||
0 116102-filter.root FlatPlotter/NNScoreAny_0_0_0 0 | ||||||||
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This file contains various parameters:
ColumnParameter BackgroundList 0 tt0j=0 | ||||||||
Changed: | ||||||||
< < | ColumnParameter SignalList 1 ttH=1 | |||||||
> > | ColumnParameter SignalList 1 ttH=1 ColumnParameter DataList 1 Data=11 | |||||||
Changed: | ||||||||
< < | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList or SignalList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but it must be sequential, running from 0 to n-1 (where you have n samples). It also does not need to correspond to my_Eventtype, however, for completeness' sake within this file I have set it as such. The number at the end of this declaration (ttjj=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). | |||||||
> > | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList, SignalList or DataList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but it must be sequential, running from 0 to n-1 (where you have n samples) - apart from for DataList entries (as shown above). It also does not need to correspond to my_Eventtype, however, for completeness' sake within this file I have set it as such. The number at the end of this declaration (tt0j=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). It must match up with the numbers provided in atlastth_histlist_flat-v16.txt so that processes and data can be matched to the various individual files. | |||||||
ColumnParameter PseudoDataList 0 tt0j=0 | ||||||||
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***NOTE*** The flags DoTraining and DoTemplates had previously (until release 00-00-21) been set on the command line. They were moved from the command line when the other flags were introduced. | ||||||||
Added: | ||||||||
> > | If you wish the fit to be run using data and not pseudodata, then the flag is set in FlatFitSteer.txt, which is created in genemflat:
GeneralParameter bool 1 PseudoData=1If this flag is set to 1 then pseudodata is used, 0 causes data to be used. | |||||||
teststeerFlatPlotterATLAStthSemileptonic-v16.txt and teststeerFlatReaderATLAStthSemileptonic-v16.txtGeneralParameter bool 1 LoadGlobalOnEachEvent=0 |
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templates/fit/out_120.log This is the paydirt - the output to screen from the fitting stage. Look at the end of this file, and you shall see the exclusions generated by all the pseudoexperiments performed on the ANN output, in a little table, also giving +/- 1/2 sigma results. | ||||||||
Added: | ||||||||
> > |
From here you can also get a record of the yields of the various processes considered by the Neural Net (the number of events expected for each process after applying all our preselection cuts, cutmasks, etc in our specified luminosity):
Print out Yield ================ Channel & tt & ttH & ttbb & Wlnu & Wbb & Wc & Wcc & st_Wt & st_schan & st_tchan & eFake & Data\\ SemiLeptonic& 2154.475 & 2.598 & 49.313 & 4140.901 & 160.634 & 498.841 & 322.464 & 83.942 & 3.265 & 18.288 & 4069.197 & 10616.000 sum & 2154.475 & 2.598 & 49.313 & 4140.901 & 160.634 & 498.841 & 322.464 & 83.942 & 3.265 & 18.288 & 4069.197 & 10616.000 ====================================================== NSig 2.59829 NBkg 11501.3 NData 10616 ====================================================== End Print outJust above this is information of the weighted histograms of the various processes (weighted such that the integral equals the yield). Channel: SemiLeptonic(0) Process: eFake(10) ib= 1 0.88551 1.50073 wgt= 0.88551 wgtE= 1.50073 wgtEsum2= 2.2522The first number is the bin number being considered. wgt is the weighted integral of that bin, and all preceding bins (i.e. the total integral up to that point) Immediately following this is the record of generating the first pseudoexperiment. It lists the weighted contents of each of the bins of a neural net histogram, assuming background only, with poisson fluctuations. It then gives the integral of this pseudoexperiment: Pseudodata Integral: 11506For obvious reasons this should be similar to the projected background yield. Later on, at the start of the fitting we also have the following: = After fit ========================================== Parameters fit: 7 Name Value Error =========== ============ ========= LumiTrigLepID : 0.0140496 0.903865 JES : 0.00324431 0.980996 Met : 0.00131664 0.99884 btag : 0.0197434 0.649849 NLOAccep : -0.0627014 0.727165 pdf : -0.0106981 0.99073 xsec : 0.00620052 0.923289These values come from a Minuit fit, so should be taken with a pinch of salt. The 'Value' compares the results of the pseudoexperiment for all the various errors, and compares it to what you told it. E.g. if you said you had 1fb-1 for luminosity, but the pseudodata suggested a luminosity of 1.01, then Value would be 0.01 - you are 'out' by 1%. 'Error' says how much of your proposed error you have 'used' - if you say you have a 10% error on your luminosity, but the fit suggests at 1% error, then 'Error' would be 0.10 - you are using 10% of your 'allowed' error. | |||||||
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Process_3_0 QCD:Semileptonic | ||||||||
Added: | ||||||||
> > | FlatStackInputSteer.txt / FlatStackInputSteerLog.txtThese files create the stacked input plots. FlatStackInputSteer.txt creates plots with a linear y-scale, FlatStackInputSteerLog.txt is with a log y-scale. Comments are within the file to explain the meaning of a few parameters. | |||||||
genemflat_batch_Complete2_SL5.shgenemflat creates the file TMVAsteer.txt, which sets a number of parameters for the running of the ANN - the constraints on the events, the precise Neural Net structure and so on - for establishing the input files, we are interested in only a couple of these parameters: | ||||||||
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The log file from Computentp -- more information about the information contained within it is found here![]() | ||||||||
Added: | ||||||||
> > | stackedinput/StackInput/tth120 A collection of root files and eps files showing pretty stacked plots of all the input variables. | |||||||
trees/NNInputs_120.root The output from Computentp - it is a copy of all of the input datasets, with the addition of the variables TrainWeight and weight. |
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Process : 2.19001e-314 SorB : 0 | ||||||||
Changed: | ||||||||
< < | These reflect the parameters as set in TMVAsteer.txt (created via genemflat_batch_Complete2_SL5_sh![]() | |||||||
> > | These reflect the parameters as set in TMVAsteer.txt (created via genemflat_batch_Complete2_SL5_sh![]() | |||||||
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0 116102-filter.root FlatPlotter/NNScoreAny_0_0_0 0 | ||||||||
Added: | ||||||||
> > | The file in general expects each line to contain an integer, 2 strings and another integer, separated by spaces. If the integers are less than one, then that line is ignored. Therefore, so long as you are careful to exclude spaces from your strings, and stick to the string/integer formula, it is possible to place comments in this file:
-1 -------------------------------------------------------- x -1 -1 ttH x -1 -1 -------------------------------------------------------- x -1 | |||||||
AtlasttHRealTitles.txtThe list of signal/background processes can be found in AtlasttHRealTitles.txt (where the names are specified and associated with numbers). At present these are: | ||||||||
Line: 937 to 945 | ||||||||
runFlatReader FlatReaderATLAStthNoNN.txt /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/ttH-v16.root | ||||||||
Changed: | ||||||||
< < | This produces a lot of printout, so be sure to restrict the number of events as described above! | |||||||
> > | This produces a lot of printout, so be sure to restrict the number of events as described above! An example of part of the output is
****** FlatReader Info Start ****** Entries examined : 13307 Events:00 Seen : 13307 Events:00 Seen Any : 13307 Events:01 Passing Mask Selection for Higgs : 5049The numbers correspond to first of all the number of entries in the input MC passing into the FlatReader (e.g. in our preselection we require at least 6 jets). The second number is in reference to the number of entries passing the cutMask (e.g. requiring exactly 6 jets). This is shortly followed by WtEvents:00 Seen : 6.17741 WtEvents:00 Seen Any : 6.17741 WtEvents:01 Passing Mask Selection for Higgs : 2.59829These entries correspond to the yields - the numbers of events expected in our specified luminosity. | |||||||
If you want to get more debugging from Computentp, then run it with another argument (doesn't matter what the argument is - in the example below it's simply 1): |
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ListParameter ProcessLabels:1 1 tt0j:t#bar{t}0j | ||||||||
Changed: | ||||||||
< < | The number after ProcessLabels again doesn't correspond to my_Eventtype - I have made it the same as the number after BackgroundList/SignalList and PseudoDataList. The important feature from this is that it tells the ANN what to label each of the various processes as in the results plots. Again, the numbers must run from 0 to n-1. | |||||||
> > | The number after ProcessLabels again doesn't correspond to my_Eventtype - I have made it the same as the number after BackgroundList/SignalList and PseudoDataList. The important feature from this is that it tells the ANN what to label each of the various processes as in the results plots. The numbers must run from 1 to n. | |||||||
ColumnParameter UCSDPalette 0 tt0j=19 |
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Changed: | ||||||||
< < |
./GlaNtpScript.sh SVN 00-00-10* This will check out everything, and run a few simple validations - the final output should look like this (i.e. don't be worried that not everything seems to have passed validation!): | |||||||
> > |
| |||||||
HwwFlatFitATLAS Validation succeeded Done with core tests | ||||||||
Line: 248 to 244 | ||||||||
Result of FlatAscii validation: OK Result of FlatAscii_global validation: OK Result of FlatTRntp validation: OK | ||||||||
Changed: | ||||||||
< < | ||||||||
> > | ||||||||
Variables used by the GlaNtp package |
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ColumnParameter BackgroundList 0 tt0j=0 ColumnParameter SignalList 1 ttH=1 | ||||||||
Changed: | ||||||||
< < | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList or SignalList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but does not need to correspond to my_Eventtype. However, for completeness' sake within this file I have set it as such. The number at the end of this declaration (ttjj=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). | |||||||
> > | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList or SignalList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but it must be sequential, running from 0 to n-1 (where you have n samples). It also does not need to correspond to my_Eventtype, however, for completeness' sake within this file I have set it as such. The number at the end of this declaration (ttjj=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). | |||||||
ColumnParameter PseudoDataList 0 tt0j=0 |
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Changed: | ||||||||
< < |
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> > |
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To run the script, first log into the batch system (ppepbs). | ||||||||
Changed: | ||||||||
< < | The genemflat_batch_Complete2_SL5.sh script can be executed with the command: | |||||||
> > | The genemflat_batch_Complete2_SL5.sh (NNFitter 00-00-21 version) script can be executed with the command: | |||||||
./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 6 00-00-17 /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessedThese options denote: |
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ListParameter <tag> <onoff> <colon-separated-parameter-list> | ||||||||
Changed: | ||||||||
< < | <onoff> - specifies whether this parameter will be taken into consideration (1) or ignored (0) - generally this should be set to 1. <tag> and <colon-seperated-parameter-list> - varies from process to process, will be explained for individual cases below. There can only be one instance of a <tag> active at any one time (i.e. you can write more than one version, but only one can be taken into consideration. | |||||||
> > | <onoff> - specifies whether this parameter will be taken into consideration (1) or ignored (0) - generally this should be set to 1. <tag> and <colon-seperated-parameter-list> - varies from process to process, will be explained for individual cases below. There can only be one instance of a <tag> active at any one time (i.e. you can write more than one version, but only one can be taken into consideration). | |||||||
ColumnParameter <tag> <sequence> <keyword=doubleValue:keyword=doubleValue...> |
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train2.log | ||||||||
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< < | A text summary of the training process. Mentions files used (how many files, numbers of events, if they signal/background, etc). Will refer to file 0, 1... - refer to Atlas ttHRealTitles.txt to work out which file is which. Use this to make sure the right numbers of events are getting though your filters. | |||||||
> > | A text summary of the training process. Mentions files used (how many files, numbers of events, if they signal/background, etc). Will refer to file 0, 1... - refer to Atlas ttHRealTitles.txt to work out which file is which. Use this to make sure the right numbers of events are getting though your filters. At the start of the file is a summary listing some relevant information about the input files:
ParamInfo: File:0 OnOff : 1 Process : 2.19001e-314 SorB : 0These reflect the parameters as set in TMVAsteer.txt (created via genemflat_batch_Complete2_SL5_sh ![]() | |||||||
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For comparison to these null hypothesis datasets, for both signal and background an array of 10,000 templates is created, each of these templates being subjected to systematic and Poisson variation. | ||||||||
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< < | The pseudodata and the array are passed to the function cslimit, which uses them to calculate an exclusion for each pseudoexperimen. | |||||||
> > | The pseudodata and the array are passed to the function cslimit, which uses them to calculate an exclusion for each pseudoexperiment. | |||||||
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Calculating my_integral (The Magic Formula)1. Check the critical formula: | ||||||||
Changed: | ||||||||
< < | > The most important forumla is the first thing to check: > > weight*= > GetSF()*GetXsect()*GetBrFrac()*GetFilterEff()*GetLumiForType()/GetNGenForType(); >> my_brFrac >> my_filterEff >> my_xSect >> my_lumiForType >> my_nGenForType > > > You need a scale factor: my_sf? > > > 2. Check the stuff that FlatReader uses. This is documented in > GlaNtp/NtpAna/test/VariableTreeToNtp.txt. > Variable Tree to Ntp is the one that maps logical values to their > physical branch/leaf. Anything prefaced with FlatTupleVar needs to > be specified or is useful to specify. > > > Values are divided in to those that can change on each event (kept > in the "ev" tree) and those that are the same for a file (kept in the > "global" tree). As you know you can set the tree names. You really > should create a global tree for the global file values now. We have > procrastinated on this a long time. > > There you see > # > # Values that are required from global > # > GeneralParameter string 1 FlatTupleVar/BrFrac=globalInfo/BrFrac > GeneralParameter string 1 FlatTupleVar/FilterEff=globalInfo/FilterEff > GeneralParameter string 1 FlatTupleVar/Fraction=Fraction/Fraction > GeneralParameter string 1 FlatTupleVar/Integral=Integral/Integral > GeneralParameter string 1 FlatTupleVar/XSect=globalInfo/Xsect > > # This specifies the name of the leaf for the cutmask and invert > word. #Again, these are global values for a file. > GeneralParameter string 1 CutMaskString=cutMask > GeneralParameter string 1 InvertWordString=invertWord > > This confirms that Fraction and Integral are needed. >> my_fraction >> my_integral > > Here are the ones that are required for ev: > # > # Values that are required from ev > # > GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel > GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll > GeneralParameter string 1 FlatTupleVar/Entry=evInfo/ientry > GeneralParameter string 1 FlatTupleVar/Event=evInfo/eventNumber > GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E > GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E > GeneralParameter string 1 FlatTupleVar/LumiForType=evInfo/lumiForType > GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW > GeneralParameter string 1 FlatTupleVar/NGenForType=evInfo/nGenForType > GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets > GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand > GeneralParameter string 1 FlatTupleVar/Run=evInfo/runNumber > GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight > GeneralParameter string 1 FlatTupleVar/cutWord=evInfo/cutWord > GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type > GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type > GeneralParameter string 1 FlatTupleVar/sf=evInfo/sf > > > Some can be used with the default values that FlatTuple gives: > GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel > GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll > GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW > GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand > GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type > GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type > > Some are ok to leave if you dont want to use it: there are switches > that turn on the use of these > GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E > GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E > GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight > > Some are useful for plotting: > GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets > > > I think you have my_Eventtype as channel > my_failEvent as cutword | |||||||
> > | The most important forumla is the first thing to check: weight*= GetSF()*GetXsect()*GetBrFrac()*GetFilterEff()*GetLumiForType()/GetNGenForType(); my_brFrac my_filterEff my_xSect my_lumiForType my_nGenForType You need a scale factor: my_sf? 2. Check the stuff that FlatReader uses. This is documented in GlaNtp/NtpAna/test/VariableTreeToNtp.txt. Variable Tree to Ntp is the one that maps logical values to their physical branch/leaf. Anything prefaced with FlatTupleVar needs to be specified or is useful to specify. Values are divided in to those that can change on each event (kept in the "ev" tree) and those that are the same for a file (kept in the "global" tree). As you know you can set the tree names. You really should create a global tree for the global file values now. We have procrastinated on this a long time. There you see # # Values that are required from global # GeneralParameter string 1 FlatTupleVar/BrFrac=globalInfo/BrFrac GeneralParameter string 1 FlatTupleVar/FilterEff=globalInfo/FilterEff GeneralParameter string 1 FlatTupleVar/Fraction=Fraction/Fraction GeneralParameter string 1 FlatTupleVar/Integral=Integral/Integral GeneralParameter string 1 FlatTupleVar/XSect=globalInfo/Xsect # This specifies the name of the leaf for the cutmask and invert word. #Again, these are global values for a file. GeneralParameter string 1 CutMaskString=cutMask GeneralParameter string 1 InvertWordString=invertWord This confirms that Fraction and Integral are needed. my_fraction my_integral Here are the ones that are required for ev: # # Values that are required from ev # GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll GeneralParameter string 1 FlatTupleVar/Entry=evInfo/ientry GeneralParameter string 1 FlatTupleVar/Event=evInfo/eventNumber GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E GeneralParameter string 1 FlatTupleVar/LumiForType=evInfo/lumiForType GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW GeneralParameter string 1 FlatTupleVar/NGenForType=evInfo/nGenForType GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand GeneralParameter string 1 FlatTupleVar/Run=evInfo/runNumber GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight GeneralParameter string 1 FlatTupleVar/cutWord=evInfo/cutWord GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type GeneralParameter string 1 FlatTupleVar/sf=evInfo/sf Some can be used with the default values that FlatTuple gives: GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type Some are ok to leave if you dont want to use it: there are switches that turn on the use of these GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight Some are useful for plotting: GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets I think you have my_Eventtype as channel my_failEvent as cutword | |||||||
Variables that must be listed in the event (not the global) tree |
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If you want a parameter to be found in the output, best to list it here.... | ||||||||
Added: | ||||||||
> > |
Calculating my_integral (The Magic Formula)1. Check the critical formula: > The most important forumla is the first thing to check:> > weight*= > GetSF()*GetXsect()*GetBrFrac()*GetFilterEff()*GetLumiForType()/GetNGenForType(); >> my_brFrac >> my_filterEff >> my_xSect >> my_lumiForType >> my_nGenForType > > > You need a scale factor: my_sf? > > > 2. Check the stuff that FlatReader uses. This is documented in > GlaNtp/NtpAna/test/VariableTreeToNtp.txt. > Variable Tree to Ntp is the one that maps logical values to their > physical branch/leaf. Anything prefaced with FlatTupleVar needs to > be specified or is useful to specify. > > > Values are divided in to those that can change on each event (kept > in the "ev" tree) and those that are the same for a file (kept in the > "global" tree). As you know you can set the tree names. You really > should create a global tree for the global file values now. We have > procrastinated on this a long time. > > There you see > # > # Values that are required from global > # > GeneralParameter string 1 FlatTupleVar/BrFrac=globalInfo/BrFrac > GeneralParameter string 1 FlatTupleVar/FilterEff=globalInfo/FilterEff > GeneralParameter string 1 FlatTupleVar/Fraction=Fraction/Fraction > GeneralParameter string 1 FlatTupleVar/Integral=Integral/Integral > GeneralParameter string 1 FlatTupleVar/XSect=globalInfo/Xsect > > # This specifies the name of the leaf for the cutmask and invert > word. #Again, these are global values for a file. > GeneralParameter string 1 CutMaskString=cutMask > GeneralParameter string 1 InvertWordString=invertWord > > This confirms that Fraction and Integral are needed. >> my_fraction >> my_integral > > Here are the ones that are required for ev: > # > # Values that are required from ev > # > GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel > GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll > GeneralParameter string 1 FlatTupleVar/Entry=evInfo/ientry > GeneralParameter string 1 FlatTupleVar/Event=evInfo/eventNumber > GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E > GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E > GeneralParameter string 1 FlatTupleVar/LumiForType=evInfo/lumiForType > GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW > GeneralParameter string 1 FlatTupleVar/NGenForType=evInfo/nGenForType > GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets > GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand > GeneralParameter string 1 FlatTupleVar/Run=evInfo/runNumber > GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight > GeneralParameter string 1 FlatTupleVar/cutWord=evInfo/cutWord > GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type > GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type > GeneralParameter string 1 FlatTupleVar/sf=evInfo/sf > > > Some can be used with the default values that FlatTuple gives: > GeneralParameter string 1 FlatTupleVar/Channel=evInfo/Channel > GeneralParameter string 1 FlatTupleVar/DilMass=evInfo/Mll > GeneralParameter string 1 FlatTupleVar/MEVal=LRInfo/LRHWW > GeneralParameter string 1 FlatTupleVar/Rand=evInfo/Rand > GeneralParameter string 1 FlatTupleVar/lep1_Type=evInfo/lep1_Type > GeneralParameter string 1 FlatTupleVar/lep2_Type=evInfo/lep2_Type > > Some are ok to leave if you dont want to use it: there are switches > that turn on the use of these > GeneralParameter string 1 FlatTupleVar/Lep1En=evInfo/lep1_E > GeneralParameter string 1 FlatTupleVar/Lep2En=evInfo/lep2_E > GeneralParameter string 1 FlatTupleVar/Weight=LRInfo/weight > > Some are useful for plotting: > GeneralParameter string 1 FlatTupleVar/Njets=evInfo/Njets > > > I think you have my_Eventtype as channel > my_failEvent as cutword | |||||||
Variables that must be listed in the event (not the global) treenGenForType, LumiForType, Eventtype |
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To set up the neural net,
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< < |
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> > |
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Getting a copy of GlaNtp
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< < |
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> > |
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mkdir /home/ahgemmell/GlaNtp cd /home/ahgemmell/GlaNtp | ||||||||
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< < | cp /home/stdenis/GlaNtpScript.sh . | |||||||
> > | svn co https://ppesvn.physics.gla.ac.uk/svn/atlas/GlaNtp/trunk/scripts/GlaNtpScript.sh![]() | |||||||
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< < |
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< < |
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> > |
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./GlaNtpScript.sh SVN 00-00-10* |
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Debugging the code | ||||||||
Changed: | ||||||||
< < | Before trying debugging, you should set up the environment in your terminal (when running the code normally, this is done automatically by tr${run}.job). This can be done by sourcing setup.sh, which automates the following lines of code: | |||||||
> > | Before trying debugging, you should set up the environment in your terminal (when running the code normally, this is done automatically by tr${run}.job). This can be done by sourcing setup_glantp.sh, which automates setting of the relevant paths - remember to specify the release number of GlaNtp that you have in your area: | |||||||
Changed: | ||||||||
< < | # Set where your GlaNtp installation is GLANTPDIR=/home/ahgemmell/GlaNtp/GlaNtpPackage/GlaNtpSVN00-00-10 | |||||||
> > | source setup_glantp.sh 00-00-17 At any point you can check that the a given steering file can be read by GlaNtp by using testSteerrv5.exe - found inside your GlaNtp package: | |||||||
Changed: | ||||||||
< < | source ~/GlaNtp/cleanpath3.sh export PATH=${PATH}:${GLANTPDIR}/bin/Linux2.6-GCC_4_1 export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${GLANTPDIR}/shlib/Linux2.6-GCC_4_1 export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${GLANTPDIR}/lib/Linux2.6-GCC_4_1 | |||||||
> > | ./${GLANTPDIR}/bin/Linux2.6-GCC_4_1/testSteerrv5.exe <file to be tested> | |||||||
Changed: | ||||||||
< < | To debug the code, two things need to be done - first, all the debug switches need to be turned on, and then you need to restrict the number of events to ~10 (for a Computentp run this will still manage to generate a 2 GB log file!). All of these switches are found in teststeerFlatReaderATLAStthSemileptonic.txt (the progenitor for all FlatReader files) and steerComputentp.txt (created by genemflat). The debug switches are: | |||||||
> > | Please note that the Linux2.6-GCC_4_1may change depending on your system architecture. It is set during the installation of GlaNtp, and can be checked with the command echo $BFARCHTo debug the code further, two things need to be done - first, all the debug switches need to be turned on, and then you need to restrict the number of events to ~10 (for a Computentp run this will still manage to generate a 2 GB log file!). All of these switches are found in teststeerFlatReaderATLAStthSemileptonic.txt (the progenitor for all FlatReader files) and steerComputentp.txt (created by genemflat). The debug switches are: | |||||||
GeneralParameter bool 1 Debug=0 GeneralParameter bool 1 DebugGlobalInfo=0 GeneralParameter bool 1 DebugEvInfo=0 |
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Variables used by the GlaNtp package | ||||||||
Changed: | ||||||||
< < | The variables used by the package can be divided into two sets. The first are those variables that are constant throughout the sample - the 'global' variables (e.g. cross-section of the sample). These can be specified in their own tree, where they will be recorded (and read by GlaNtp) once only. If desired, these variables can be defined within the main tree of the input file - however, then they will be recorded once per event, and read in once per event. This is obviously a bit wasteful, but for historical reasons it can be done. To determine which of these behaviours you use, set LoadGlobalOnEachEvent in FlatPlotter and FlatReader to 1 for the events to be read in on an event-by-event basis, or 0 to be read in once from the global tree (or from the first event only). For more information on this switch, refer to this![]() | |||||||
> > | The variables used by the package can be divided into two sets. The first are those variables that are constant throughout the sample - the 'global' variables (e.g. cross-section of the sample). These can be specified in their own tree, where they will be recorded (and read by GlaNtp) once only. If desired, these variables can be defined within the main tree of the input file - however, then they will be recorded once per event, and read in once per event. This is obviously a bit wasteful, but for historical reasons it can be done. To determine which of these behaviours you use, set LoadGlobalOnEachEvent in FlatPlotter and FlatReader to 1 for the events to be read in on an event-by-event basis, or 0 to be read in once from the global tree (or from the first event only). For more information on this switch, refer to this![]() | |||||||
The file maps logical values to their branch/leaf. The tree can be the global tree or the event tree. | ||||||||
Line: 271 to 271 | ||||||||
If you want a parameter to be found in the output, best to list it here.... | ||||||||
Added: | ||||||||
> > | Variables that must be listed in the event (not the global) treenGenForType, LumiForType, Eventtype | |||||||
Variables used for training the Neural NetThe list of variables on which the neural net is to train is set in the shell script, under TMVAvarset.txt (this file is created when the script runs). At present, these variables are: | ||||||||
Line: 564 to 568 | ||||||||
Other switches to influence the running | ||||||||
Changed: | ||||||||
< < | In genemflat_batch_Complete2_SL5.sh, at the start of the file there are a number of switches established: | |||||||
> > | genemflat_batch_Complete2_SL5.shAt the start of the file there are a number of switches established: | |||||||
# Flags to limit the scope of the run if desired | ||||||||
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***NOTE*** The flags DoTraining and DoTemplates had previously (until release 00-00-21) been set on the command line. They were moved from the command line when the other flags were introduced. | ||||||||
Added: | ||||||||
> > | teststeerFlatPlotterATLAStthSemileptonic-v16.txt and teststeerFlatReaderATLAStthSemileptonic-v16.txtGeneralParameter bool 1 LoadGlobalOnEachEvent=0Determines if you have a separate global tree or not. If you do not, set this equal to one, and the relevant global values will be read out anew for each event from the event tree. | |||||||
Where the output is stored | ||||||||
Added: | ||||||||
> > | Computentp120.log
The log file from Computentp -- more information about the information contained within it is found here![]() | |||||||
trees/NNInputs_120.root The output from Computentp - it is a copy of all of the input datasets, with the addition of the variables TrainWeight and weight. | ||||||||
Line: 682 to 698 | ||||||||
A useful little html page that one of Rick's scripts creates, showing a number of useful plots - the signal and background Net scores, distributions of input variables and their correlations, and so on. | ||||||||
Added: | ||||||||
> > | Information found in log filesComputentp120.logAfter looping over all the events, you will see a table like the one below:Process Name File Name File Scale Events Integral IntLumi Alpha ttjj /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/105200-29Aug.root 0 1 613 613 1 0.907015 ttH /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/ttH-v16.root 120 1 556 556 1 1Some of the values are established through steerComputentp.txt in the line ListParameter Process:ttH 1 Filename:/data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/ttH-v16.root:File:120:IntLumi:1.0
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Limitations
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To enable you to specify the range and number of bins in the histogram showing the distribution of the pseudoexperiment exclusions. (Found in drivetestFlatFitAtlastth.rootUnscaledTemplates.root) | ||||||||
Deleted: | ||||||||
< < | In both FlatReader and FlatPlotter: GeneralParameter bool 1 LoadGlobalOnEachEvent=1 This needs to be set to one if you wish to load the global variables anew for each event. Otherwise the global variables will be loaded once only - from the Global tree if you have specified it, or from the first event if you haven't. Therefore, if your input datasets have non-sensible states and no global tree, this must be set to one. Otherwise, if the first entry is not sensible, or for some other reason has an unreasonable answer for this global value, a problem will develop. With this switch on, the values will be loaded each and every time – obviously this slows the code down – if the global values are safely stored in every entry, it might be best to set this to false. | |||||||
TMVAsteer.txt (genemflat_batch_Complete2_SL5.sh)H6AONN5MEMLP MLP 1 !H:!V:NCycles=1000:HiddenLayers=N+1,N:RandomSeed=9876543 |
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Line: 529 to 529 | ||||||||
To run the script, first log into the batch system (ppepbs). The genemflat_batch_Complete2_SL5.sh script can be executed with the command: | ||||||||
Changed: | ||||||||
< < | ./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 6 00-00-17 /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed | |||||||
> > | ./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 6 00-00-17 /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed | |||||||
These options denote: | ||||||||
Line: 540 to 540 | ||||||||
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> > |
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Line: 529 to 529 | ||||||||
To run the script, first log into the batch system (ppepbs). The genemflat_batch_Complete2_SL5.sh script can be executed with the command: | ||||||||
Changed: | ||||||||
< < | ./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 6 /data/atlas07/ahgemmell/NTuple-v15-30Aug | |||||||
> > | ./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 6 00-00-17 /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed | |||||||
These options denote: |
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Getting a copy of GlaNtp | ||||||||
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Getting a copy of GlaNtp | ||||||||
Changed: | ||||||||
< < |
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> > |
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mkdir /home/ahgemmell/GlaNtp cd /home/ahgemmell/GlaNtp cp /home/stdenis/GlaNtpScript.sh . | ||||||||
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< < |
cp /home/stdenis/atlas/testGlaNtp/cleanpath3.sh source cleanpath3.sh | |||||||
> > |
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Changed: | ||||||||
< < |
export GLANTP_DATA=/data/cdf01/stdenis/GlaNtpData | |||||||
> > |
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./GlaNtpScript.sh SVN 00-00-10 |
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GeneralParameter string 1 InvertWordString=invertWord | ||||||||
Changed: | ||||||||
< < | There's also something about | |||||||
> > | The structure of Computentp's output is specified by | |||||||
ListParameter EvInfoTree:1 1 NN_BJetWeight_Jet1:NN_BJetWeight_Jet1/NN_BJetWeight_Jet1 | ||||||||
Changed: | ||||||||
< < | that I need to ask Rick about... | |||||||
> > | If you want a parameter to be found in the output, best to list it here.... | |||||||
Variables used for training the Neural Net | ||||||||
Line: 761 to 760 | ||||||||
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${GLANTPDIR}/lib/Linux2.6-GCC_4_1 | ||||||||
Changed: | ||||||||
< < | To debug the code, two things need to be done - first, all the debug switches need to be turned on, and then you need to restrict the number of events to ~10 (for a Computentp run this will still manage to generate a 2 GB log file!). All of these switches are found in teststeerFlatReaderATLAStthSemileptonic.txt (the progenitor for all FlatReader files). The debug switches are: | |||||||
> > | To debug the code, two things need to be done - first, all the debug switches need to be turned on, and then you need to restrict the number of events to ~10 (for a Computentp run this will still manage to generate a 2 GB log file!). All of these switches are found in teststeerFlatReaderATLAStthSemileptonic.txt (the progenitor for all FlatReader files) and steerComputentp.txt (created by genemflat). The debug switches are: | |||||||
GeneralParameter bool 1 Debug=0 GeneralParameter bool 1 DebugGlobalInfo=0 GeneralParameter bool 1 DebugEvInfo=0 GeneralParameter int 1 ReportInterval=100 | ||||||||
Added: | ||||||||
> > | In steerComputentp.txt there is also one additional debug option:
GeneralParameter bool 1 DebugFlatTRntp=1 | |||||||
All the debug switches can be set to one (I'm not sure of the exact effect of each individual switch) - the report interval can be adapted depending on how many events are present in your input files and on how large you want your log files to be. To restrict the events you use
# # Loop Control | ||||||||
Line: 795 to 798 | ||||||||
This produces a lot of printout, so be sure to restrict the number of events as described above! | ||||||||
Added: | ||||||||
> > | If you want to get more debugging from Computentp, then run it with another argument (doesn't matter what the argument is - in the example below it's simply 1):
Computentp steerComputentp.txt 1 Some error messages and how to fix themDouble Variable: my_NN_BJet12_M not valid and hence saved : 1Look at VariableTreeToNTPATLASttHSemiLeptonic-v16.txt - are the names of the variables really consistent? | |||||||
Various other switches of interestIn FlatReader: |
Line: 1 to 1 | |||||||||||||||||||||||||
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> > | |||||||||||||||||||||||||
Computentp, Neural Nets and MCLIMITSThis page has been substantially rewritten (and remains a work in progress) to focus just on the information required for a successful run of the Computentp and Neural Net package, to deliver exclusions. For information on results obtained using inputs created in v12 of athena, please refer to the archive. This page also describes how to run on GlaNtp - the version of the code set up for use in Glasgow, with no CDF dependencies. To use the previous version of the code (there are some important differences) refer to r93 and earlier. | |||||||||||||||||||||||||
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Current samples in use | |||||||||||||||||||||||||
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< < | Input data and cross-sections | ||||||||||||||||||||||||
> > | Input data and cross-sections | ||||||||||||||||||||||||
These cross-sections are for the overall process, at √s = 7 TeV. | |||||||||||||||||||||||||
Line: 33 to 36 | |||||||||||||||||||||||||
The tt samples were initially generated to produce the equivalent of 75fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | **IMPORTANT** The Filter Efficiency for these samples was calculated based on a no-pileup sample. The filter is generator level, and one of the things it will cut an event for is not enough jets. However, pileup adds jets, but these are added well after the filter. The net result is that a number of events that failed the filter would have passed, had the pileup been added earlier in the process. This means the filter efficiency (and thus the cross-sections) are incorrect, by a yet to determined amount.... | ||||||||||||||||||||||||
> > | **IMPORTANT** The Filter Efficiency for these samples was calculated based on a no-pileup sample. The filter is generator level, and one of the things it will cut an event for is not enough jets. However, pileup adds jets, but these are added well after the filter. The net result is that a number of events that failed the filter would have passed, had the pileup been added earlier in the process. This means the filter efficiency (and thus the cross-sections) are incorrect, by a yet to determined amount.... | ||||||||||||||||||||||||
For the other samples, however, we do need to worry about branching ratios - the quoted initial cross-section includes all final states, so we need to apply branching ratios to the cross-section to reduce it down, so that it reflects the sample we've generated. We then subsequently need to reduce the cross-section further so that it reflects the number of sensible states. | |||||||||||||||||||||||||
Line: 77 to 80 | |||||||||||||||||||||||||
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< < | |||||||||||||||||||||||||
These cross-sections and branching ratios are correct as of 8 Feb 2011. qq→ttbb (EWK) is currently not being used, thanks to a bug in the production of the MC | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | Number of events surviving preselection, weights and TrainWeights | ||||||||||||||||||||||||
> > | Number of events surviving preselection, weights and TrainWeights | ||||||||||||||||||||||||
(See later in the TWiki for an explanation of weights and TrainWeights.) This table will be completed with all the relevant weights and TrainWeights at a later date - these values are to be compared to the output from Computentp to ensure everything is working as intended, and are calculated for the sensible cross-sections/events. (A quick check of the TrainWeight is to multiply the number so events of each background by their TrainWeight and sum them - by design, this should equal the number of entries in the ttH sample.) | |||||||||||||||||||||||||
Line: 115 to 116 | |||||||||||||||||||||||||
Things to do | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | * In the script used to make the webpage showing the results, the reference to H6AONN5MEMLP is hardwired. It should become a argument. It is the name of the method you give TMVA in the training, and so if it changes in one you should be able to change in the other | ||||||||||||||||||||||||
> > | * | ||||||||||||||||||||||||
Added: | |||||||||||||||||||||||||
> > | In the script used to make the webpage showing the results, the reference to H6AONN5MEMLP is hardwired. It should become a argument. It is the name of the method you give TMVA in the training, and so if it changes in one you should be able to change in the other | ||||||||||||||||||||||||
Overview of the process | |||||||||||||||||||||||||
Line: 194 to 196 | |||||||||||||||||||||||||
Getting a copy of GlaNtp | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < |
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> > |
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mkdir /home/ahgemmell/GlaNtp cd /home/ahgemmell/GlaNtp cp /home/stdenis/GlaNtpScript.sh . | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < |
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> > |
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cp /home/stdenis/atlas/testGlaNtp/cleanpath3.sh source cleanpath3.sh | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < |
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> > |
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export GLANTP_DATA=/data/cdf01/stdenis/GlaNtpData | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < |
./GlaNtpScript.sh SVN 00-00-10* This will check out everything, and run a few simple validations - the final output should look like this (i.e. don't be worried that not everything seems to have passed validation!): | ||||||||||||||||||||||||
> > |
./GlaNtpScript.sh SVN 00-00-10* This will check out everything, and run a few simple validations - the final output should look like this (i.e. don't be worried that not everything seems to have passed validation!): | ||||||||||||||||||||||||
HwwFlatFitATLAS Validation succeeded Done with core tests | |||||||||||||||||||||||||
Line: 249 to 257 | |||||||||||||||||||||||||
Result of FlatAscii validation: OK Result of FlatAscii_global validation: OK Result of FlatTRntp validation: OK | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | |||||||||||||||||||||||||
> > | |||||||||||||||||||||||||
Variables used by the GlaNtp package | |||||||||||||||||||||||||
Added: | |||||||||||||||||||||||||
> > | |||||||||||||||||||||||||
The variables used by the package can be divided into two sets. The first are those variables that are constant throughout the sample - the 'global' variables (e.g. cross-section of the sample). These can be specified in their own tree, where they will be recorded (and read by GlaNtp) once only. If desired, these variables can be defined within the main tree of the input file - however, then they will be recorded once per event, and read in once per event. This is obviously a bit wasteful, but for historical reasons it can be done. To determine which of these behaviours you use, set LoadGlobalOnEachEvent in FlatPlotter and FlatReader to 1 for the events to be read in on an event-by-event basis, or 0 to be read in once from the global tree (or from the first event only). For more information on this switch, refer to this![]() | |||||||||||||||||||||||||
Line: 269 to 278 | |||||||||||||||||||||||||
ListParameter EvInfoTree:1 1 NN_BJetWeight_Jet1:NN_BJetWeight_Jet1/NN_BJetWeight_Jet1 | |||||||||||||||||||||||||
Added: | |||||||||||||||||||||||||
> > | |||||||||||||||||||||||||
that I need to ask Rick about...
Variables used for training the Neural NetThe list of variables on which the neural net is to train is set in the shell script, under TMVAvarset.txt (this file is created when the script runs). At present, these variables are: | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | The b-weights for the six 'leading' jets - currently the jets are ranked according to their b-weights, but it is possible to rank them according to pT and energy. The decision about how to rank them is done in the AOD -> NTuple stage: NN_BJetWeight_Jet1 NN_BJetWeight_Jet2 NN_BJetWeight_Jet3 NN_BJetWeight_Jet4 NN_BJetWeight_Jet5 NN_BJetWeight_Jet6 | ||||||||||||||||||||||||
> > | The b-weights for the six 'leading' jets - currently the jets are ranked according to their b-weights, but it is possible to rank them according to pT and energy. The decision about how to rank them is done in the AOD -> NTuple stage: NN_BJetWeight_Jet1 NN_BJetWeight_Jet2 NN_BJetWeight_Jet3 NN_BJetWeight_Jet4 NN_BJetWeight_Jet5 NN_BJetWeight_Jet6 | ||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | The masses and pT of the various jet combinations (only considering the four 'top' jets - i.e. if ranked by b-weights, the jets that we expect to really be b-jets in our signal: NN_BJet12_M NN_BJet13_M NN_BJet14_M NN_BJet23_M NN_BJet24_M NN_BJet34_M NN_BJet12_Pt NN_BJet13_Pt NN_BJet14_Pt NN_BJet23_Pt NN_BJet24_Pt NN_BJet34_Pt | ||||||||||||||||||||||||
> > | The masses and pT of the various jet combinations (only considering the four 'top' jets - i.e. if ranked by b-weights, the jets that we expect to really be b-jets in our signal: NN_BJet12_M NN_BJet13_M NN_BJet14_M NN_BJet23_M NN_BJet24_M NN_BJet34_M NN_BJet12_Pt NN_BJet13_Pt NN_BJet14_Pt NN_BJet23_Pt NN_BJet24_Pt NN_BJet34_Pt | ||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | The sums of the eT of the two reconstructed tops, for each of the top three states: NN_State1_SumTopEt NN_State2_SumTopEt NN_State3_SumTopEt | ||||||||||||||||||||||||
> > | The sums of the eT of the two reconstructed tops, for each of the top three states: NN_State1_SumTopEt NN_State2_SumTopEt NN_State3_SumTopEt | ||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | And the differences between the eta and phi of the two reconstructed tops, again from the top three states: NN_State1_DiffTopEta NN_State2_DiffTopEta NN_State3_DiffTopEta NN_State1_DiffTopPhi NN_State2_DiffTopPhi NN_State3_DiffTopPhi | ||||||||||||||||||||||||
> > | And the differences between the eta and phi of the two reconstructed tops, again from the top three states: NN_State1_DiffTopEta NN_State2_DiffTopEta NN_State3_DiffTopEta NN_State1_DiffTopPhi NN_State2_DiffTopPhi NN_State3_DiffTopPhi | ||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | You also need to provide addresses to the Neural Net so that it can find the variables in the input trees. This is done inside VariableTreeToNTPATLASttHSemiLeptonic-v15.txt | ||||||||||||||||||||||||
> > | You also need to provide addresses to the Neural Net so that it can find the variables in the input trees. This is done inside VariableTreeToNTPATLASttHSemiLeptonic-v15.txt : | ||||||||||||||||||||||||
ListParameter EvInfoTree:1 1 NN_BJetWeight_Jet1:NN_BJetWeight_Jet1/NN_BJetWeight_Jet1 | |||||||||||||||||||||||||
Line: 526 to 532 | |||||||||||||||||||||||||
This controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first two bits are set, and not equal to zero), then the event is used for training. This filter is not used currently, as training of the net takes place based on the Computentp output - this Computentp output only contains sensible states (as specified in the TreeSpecATLAStth.txt file's filter). If further filtering is required, then care must be taken to ensure that my_failEvent (or whatever you wish to base your filter on) is specified in the VariableTreeToNTP file, so that Computentp will copy it into its output. | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | **If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints.** | ||||||||||||||||||||||||
> > | **If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints.** | ||||||||||||||||||||||||
Running | |||||||||||||||||||||||||
Line: 543 to 549 | |||||||||||||||||||||||||
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Changed: | |||||||||||||||||||||||||
< < |
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> > |
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Line: 581 to 587 | |||||||||||||||||||||||||
These control whether or not various parts of the code are run - the names of the flags are pretty self-explanatory about what parts of the code they control. For example, it is possible to omit the training in subsequent (templating) runs, if it has previously been done. This shortens the run time significantly. | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | ***NOTE*** The flags DoTraining and DoTemplates had previously (until release 00-00-21) been set on the command line. They were moved from the command line when the other flags were introduced. | ||||||||||||||||||||||||
> > | ***NOTE*** The flags DoTraining and DoTemplates had previously (until release 00-00-21) been set on the command line. They were moved from the command line when the other flags were introduced. | ||||||||||||||||||||||||
Where the output is stored | |||||||||||||||||||||||||
Line: 787 to 793 | |||||||||||||||||||||||||
runFlatReader FlatReaderATLAStthNoNN.txt /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/ttH-v16.root | |||||||||||||||||||||||||
Changed: | |||||||||||||||||||||||||
< < | This produces a lot of printout, so be sure to restrict the number of events as described above! | ||||||||||||||||||||||||
> > | This produces a lot of printout, so be sure to restrict the number of events as described above! | ||||||||||||||||||||||||
Various other switches of interest | |||||||||||||||||||||||||
Line: 812 to 818 | |||||||||||||||||||||||||
If the phrase 'H6AONN5MEMLP' is changed, then this change must also be propogated to the webpage plotter (e-mail from Rick 1 Mar 2011) | |||||||||||||||||||||||||
Added: | |||||||||||||||||||||||||
> > | |||||||||||||||||||||||||
|
Line: 1 to 1 | ||||||||
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 414 to 414 | ||||||||
FirstEvent and LastEvent allow you to specify a range of events to run over - this is liable only to be useful during debugging. (Note that these parameters are currently turned off). NEvent gives the maximum number of events processed for any given sample - take care with this, if you are running a particularly large sample through the code.... | ||||||||
Deleted: | ||||||||
< < | Passing preselection and sensible statesMany of the generated events in our samples will not pass the preselection cuts we would use in our final analysis. Sometimes to pass preselection requires some mistakes on the part of the reconstruction (e.g. tt + 0j), othertimes to fail preselection requires either the final state particles to be inherently unsuitable for our reconstruction, or to be mis-reconstructed. However, even if an event passes preselection it is possible that the events as reconstructed give a nonsensical final state - for example, the the light jets might not be able to be combined in such a way as to give a reasonable value of the W mass. Based on a few simple mass cuts, an event passing preselection can be determined to have a sensible state or not. Currently, the type of event you are looking at is determined by looking at my_failEvent. States failing preselection have this equal to 0, passing preselection but not having a sensible final state equal 1 and passing preselection and having a sensible final state equal 3. These numbers are the basis of a number of bitwise tests - thus when setting your own my_failEvents, consider which bits in a binary string you want to represent various things, and then convert those to decimal. | |||||||
Setting Systematic UncertaintiesThe fitting code can take into account two different types of systematic uncertainty - rate and shape. The basic method to obtain both these uncertainties is that you should make your input samples for both your nominal sample, and for the two bounds of a given error (e.g. Initial State Radiation, ISR). Repeat this for all of the errors you wish to consider. The rate systematic uncertainty is simply how the number of events change that pass your preselection cuts etc. (you can only consider this, if you like). To obtain the shape uncertainty, you should pass each of the resulting datasets through the ANN (up to and including the templating, so that you have ANN results for both the nominal results, and as a result of varying each background). These ANN outputs can then be used to produce the rate uncertainties based on their integrals, before being normalised to the nominal cross-section so as to find the shape uncertainty - a measure of the percentage change in the bin-by-bin distribution for each error. | ||||||||
Line: 511 to 505 | ||||||||
It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts - general cleaning cuts and the like. (There was a previous version of our inputs where we also required 'sensible states' - for each candidate event we required it to reconstruct tops and Ws with vaguely realistic masses. However - this is a Neural Net analysis, so it has been decided to remove these cuts - they will after all in effect be reintroduced by the net itself if they would have been useful, and by not applying them ourselves, we are passing more information to the net.) We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in TMVAsteer.txt (created in genemflat_batch_Complete2.sh) (not currently used, as explained below) and TreeSpecATLAStth.txt. | ||||||||
Added: | ||||||||
> > | VariableTreeToNTPATLASttHSemiLeptonic-v16.txtGeneralParameter string 1 FlatTupleVar/cutWord=my_GoodJets_N/my_GoodJets_NThis sets the variable we wish to use in our filter - it interfaces with the cutMask and invertWord as specified in TreeSpecATLAStth.txt. Note that depending on the number of jets you wish to run your analysis on (set as a command line argument during the running of the script), this is edited with genemflat. | |||||||
TreeSpecATLAStth.txtIn TreeSpecATLAStth.txt, we establish the filters which control what is used for the templating, and Computentp: | ||||||||
Line: 518 to 518 | ||||||||
ListParameter SpecifyVariable:Higgs:cutMask 1 Type:int:Default:3 ListParameter SpecifyVariable:Higgs:invertWord 1 Type:int:Default:0 | ||||||||
Changed: | ||||||||
< < | InvertWord is used to invert the relevant bits (in this case no bits are inverted) before the cut from cutMask is applied. The cutMask will exclude from templating those events where the matching bits are equal to zero AFTER the inversion. So here, with no inversion applied, those events with my_failEvent == 3 will be used for templating. | |||||||
> > | InvertWord is used to invert the relevant bits (in this case no bits are inverted) before the cut from cutMask is applied. The cutMask tells the filter which bits we care about (we use a binary filter). So, for example, if cutMask is set to 6 (110 in binary), we are telling the filter that we wish the second and third bit to be equal to one in cutWord - we don't care about the first bit. | |||||||
TMVAsteer.txt (genemflat_batch_Complete2.sh) |
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 275 to 275 | ||||||||
The list of variables on which the neural net is to train is set in the shell script, under TMVAvarset.txt (this file is created when the script runs). At present, these variables are: | ||||||||
Changed: | ||||||||
< < | The b-weights for the six 'leading' jets - currently the jets are ranked according to their b-weights, but it is possible to rank them according to pT and energy. The decision about how to rank them is done in the AOD -> NTuple stage: | |||||||
> > | The b-weights for the six 'leading' jets - currently the jets are ranked according to their b-weights, but it is possible to rank them according to pT and energy. The decision about how to rank them is done in the AOD -> NTuple stage: | |||||||
NN_BJetWeight_Jet1 NN_BJetWeight_Jet2 NN_BJetWeight_Jet3 NN_BJetWeight_Jet4 NN_BJetWeight_Jet5 NN_BJetWeight_Jet6 | ||||||||
Changed: | ||||||||
< < | The masses and pT of the various jet combinations (only considering the four 'top' jets - i.e. if ranked by b-weights, the jets that we expect to really be b-jets in our signal: | |||||||
> > | The masses and pT of the various jet combinations (only considering the four 'top' jets - i.e. if ranked by b-weights, the jets that we expect to really be b-jets in our signal: | |||||||
NN_BJet12_M NN_BJet13_M NN_BJet14_M NN_BJet23_M NN_BJet24_M NN_BJet34_M NN_BJet12_Pt NN_BJet13_Pt NN_BJet14_Pt NN_BJet23_Pt NN_BJet24_Pt NN_BJet34_Pt | ||||||||
Changed: | ||||||||
< < | The sums of the eT of the two reconstructed tops, for each of the top three states: | |||||||
> > | The sums of the eT of the two reconstructed tops, for each of the top three states: | |||||||
NN_State1_SumTopEt NN_State2_SumTopEt NN_State3_SumTopEt | ||||||||
Changed: | ||||||||
< < | And the differences between the eta and phi of the two reconstructed tops, again from the top three states: | |||||||
> > | And the differences between the eta and phi of the two reconstructed tops, again from the top three states: | |||||||
NN_State1_DiffTopEta NN_State2_DiffTopEta NN_State3_DiffTopEta NN_State1_DiffTopPhi NN_State2_DiffTopPhi NN_State3_DiffTopPhi You also need to provide addresses to the Neural Net so that it can find the variables in the input trees. This is done inside VariableTreeToNTPATLASttHSemiLeptonic-v15.txt |
Line: 1 to 1 | ||||||||
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 750 to 750 | ||||||||
GLANTPDIR=/home/ahgemmell/GlaNtp/GlaNtpPackage/GlaNtpSVN00-00-10 source ~/GlaNtp/cleanpath3.sh | ||||||||
Changed: | ||||||||
< < | export PATH=\${PATH}:${GLANTPDIR}/bin/Linux2.6-GCC_4_1 export LD_LIBRARY_PATH=\${LD_LIBRARY_PATH}:${GLANTPDIR}/shlib/Linux2.6-GCC_4_1 export LD_LIBRARY_PATH=\${LD_LIBRARY_PATH}:${GLANTPDIR}/lib/Linux2.6-GCC_4_1 | |||||||
> > | export PATH=${PATH}:${GLANTPDIR}/bin/Linux2.6-GCC_4_1 export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${GLANTPDIR}/shlib/Linux2.6-GCC_4_1 export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${GLANTPDIR}/lib/Linux2.6-GCC_4_1 | |||||||
To debug the code, two things need to be done - first, all the debug switches need to be turned on, and then you need to restrict the number of events to ~10 (for a Computentp run this will still manage to generate a 2 GB log file!). All of these switches are found in teststeerFlatReaderATLAStthSemileptonic.txt (the progenitor for all FlatReader files). The debug switches are: |
Line: 1 to 1 | ||||||||
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 743 to 743 | ||||||||
Debugging the code | ||||||||
Changed: | ||||||||
< < | Before trying debugging, you should set up the environment in your terminal (when running the code normally, this is done automatically by tr${run}.job) | |||||||
> > | Before trying debugging, you should set up the environment in your terminal (when running the code normally, this is done automatically by tr${run}.job). This can be done by sourcing setup.sh, which automates the following lines of code: | |||||||
# Set where your GlaNtp installation is | ||||||||
Line: 761 to 761 | ||||||||
GeneralParameter bool 1 DebugEvInfo=0 GeneralParameter int 1 ReportInterval=100 | ||||||||
Changed: | ||||||||
< < | All the debug switches can be set to one (I'm not sure of the exact effect of each individual switch) - the report interval is probably best left at 100. To restrict the events you use | |||||||
> > | All the debug switches can be set to one (I'm not sure of the exact effect of each individual switch) - the report interval can be adapted depending on how many events are present in your input files and on how large you want your log files to be. To restrict the events you use | |||||||
# # Loop Control # |
Line: 1 to 1 | ||||||||
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Line: 743 to 743 | ||||||||
Debugging the code | ||||||||
Added: | ||||||||
> > | Before trying debugging, you should set up the environment in your terminal (when running the code normally, this is done automatically by tr${run}.job)
# Set where your GlaNtp installation is GLANTPDIR=/home/ahgemmell/GlaNtp/GlaNtpPackage/GlaNtpSVN00-00-10 source ~/GlaNtp/cleanpath3.sh export PATH=\${PATH}:${GLANTPDIR}/bin/Linux2.6-GCC_4_1 export LD_LIBRARY_PATH=\${LD_LIBRARY_PATH}:${GLANTPDIR}/shlib/Linux2.6-GCC_4_1 export LD_LIBRARY_PATH=\${LD_LIBRARY_PATH}:${GLANTPDIR}/lib/Linux2.6-GCC_4_1 | |||||||
To debug the code, two things need to be done - first, all the debug switches need to be turned on, and then you need to restrict the number of events to ~10 (for a Computentp run this will still manage to generate a 2 GB log file!). All of these switches are found in teststeerFlatReaderATLAStthSemileptonic.txt (the progenitor for all FlatReader files). The debug switches are:
GeneralParameter bool 1 Debug=0 GeneralParameter bool 1 DebugGlobalInfo=0 | ||||||||
Line: 759 to 771 | ||||||||
The easiest switch is to set NEvent=10 - however, if desired you can run over a specified range, by switching of the NEvent switch (changing it to int 0 NEvent) and switching on the other two switches, using them to specify the events you wish to run over. | ||||||||
Added: | ||||||||
> > | Then you can run a subset of a complete run, but altering the flags found in genemflat:
# Flags to limit the scope of the run if desired Computentps=1 DoTraining=0 ComputeTMVA=0 DoTemplates=0 DoStackedPlots=0 DoFit=0However, sometimes even this can not produce enough information., so there exist a few other options for checking your code. The first option is runFlatReader FlatReaderATLAStthNoNN.txt /data/atlas09/ahgemmell/NNInputFiles_v16/mergedfilesProcessed/ttH-v16.rootThis produces a lot of printout, so be sure to restrict the number of events as described above! | |||||||
Various other switches of interestIn FlatReader: |
Line: 1 to 1 | ||||||||
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 402 to 402 | ||||||||
The <sequence> parameter (in this case '0') is there so that you can specify the parameters for a given error for multiple channels, without falling foul of the uniqueness requirement for <tag>:<sequence>. We have chosen it so that it equals my_Eventtype for that process. 'Channel' is present just in case you're considering multiple channels. We're only considering the one channel in this case (SemiLeptonic). The final parameter (Process) is not actually used - the second parameter tells the ANN which errors are which, but this isn't very easily read by you, so feel free to add it in to help you keep track of the various errors! These final few parameters can be placed in any order, so long as they are separated by semicolons. | ||||||||
Added: | ||||||||
> > | teststeerFlatReaderATLAStthSemileptonic-v16.txtThis file contains parameters to control the loops over events.GeneralParameter int 1 NEvent=20000000 GeneralParameter int 0 FirstEvent=1 GeneralParameter int 0 LastEvent=10FirstEvent and LastEvent allow you to specify a range of events to run over - this is liable only to be useful during debugging. (Note that these parameters are currently turned off). NEvent gives the maximum number of events processed for any given sample - take care with this, if you are running a particularly large sample through the code.... | |||||||
Passing preselection and sensible statesMany of the generated events in our samples will not pass the preselection cuts we would use in our final analysis. Sometimes to pass preselection requires some mistakes on the part of the reconstruction (e.g. tt + 0j), othertimes to fail preselection requires either the final state particles to be inherently unsuitable for our reconstruction, or to be mis-reconstructed. However, even if an event passes preselection it is possible that the events as reconstructed give a nonsensical final state - for example, the the light jets might not be able to be combined in such a way as to give a reasonable value of the W mass. Based on a few simple mass cuts, an event passing preselection can be determined to have a sensible state or not. |
Line: 1 to 1 | ||||||||
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 521 to 521 | ||||||||
To run the script, first log into the batch system (ppepbs). The genemflat_batch_Complete2_SL5.sh script can be executed with the command: | ||||||||
Changed: | ||||||||
< < | ./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 /data/atlas07/ahgemmell/NTuple-v15-30Aug | |||||||
> > | ./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 6 /data/atlas07/ahgemmell/NTuple-v15-30Aug | |||||||
These options denote: | ||||||||
Line: 531 to 531 | ||||||||
| ||||||||
Changed: | ||||||||
< < |
| |||||||
> > |
| |||||||
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Line: 1 to 1 | ||||||||
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 252 to 252 | ||||||||
Variables used by the GlaNtp package | ||||||||
Changed: | ||||||||
< < | The variables used by the package can be divided into two sets. The first are those variables that are constant throughout the sample - the 'global' variables (e.g. cross-section of the sample). These can be specified in their own tree, where they will be recorded (and read by GlaNtp) once only. If desired, these variables can be defined within the main tree of the input file - however, then they will be recorded once per event, and read in once per event. This is obviously a bit wasteful, but for historical reasons it can be done. The other variables are those that change on an event-by-event basis. These variables include both the variables we are going to train the Neural Net on (more information relevant to those variables is given in the relevant section of this TWiki), and other useful variables, such as filter flags (that tell GlaNtp whether an event is sensible or not). All of these variables are listed in the file VariableTreeToNTPATLASttHSemiLeptonic-v15.txt | |||||||
> > | The variables used by the package can be divided into two sets. The first are those variables that are constant throughout the sample - the 'global' variables (e.g. cross-section of the sample). These can be specified in their own tree, where they will be recorded (and read by GlaNtp) once only. If desired, these variables can be defined within the main tree of the input file - however, then they will be recorded once per event, and read in once per event. This is obviously a bit wasteful, but for historical reasons it can be done. To determine which of these behaviours you use, set LoadGlobalOnEachEvent in FlatPlotter and FlatReader to 1 for the events to be read in on an event-by-event basis, or 0 to be read in once from the global tree (or from the first event only). For more information on this switch, refer to this![]() | |||||||
The file maps logical values to their branch/leaf. The tree can be the global tree or the event tree. | ||||||||
Line: 763 to 763 | ||||||||
GeneralParameter bool 1 LoadGlobalOnEachEvent=1 | ||||||||
Changed: | ||||||||
< < | This needs to be set to one if your input datasets have non-sensible states. Otherwise, global variables are loaded on one occasion from the first entry in the file, and kept. If this entry is not sensible, or for some other reason has an unreasonable answer for this global value, a problem will develop. With this switch on, the values will be loaded each and every time – obviously this slows the code down – if the global values are safely stored in every entry, it might be best to set this to false. | |||||||
> > | This needs to be set to one if you wish to load the global variables anew for each event. Otherwise the global variables will be loaded once only - from the Global tree if you have specified it, or from the first event if you haven't. Therefore, if your input datasets have non-sensible states and no global tree, this must be set to one. Otherwise, if the first entry is not sensible, or for some other reason has an unreasonable answer for this global value, a problem will develop. With this switch on, the values will be loaded each and every time – obviously this slows the code down – if the global values are safely stored in every entry, it might be best to set this to false. | |||||||
TMVAsteer.txt (genemflat_batch_Complete2_SL5.sh) |
Line: 1 to 1 | ||||||||
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 19 to 19 | ||||||||
Preparing samples for the Neural Net | ||||||||
Changed: | ||||||||
< < | Samples are produced for the Neural Net from AODs - results have previously been obtained for MC samples derived from v12 and v15 of athena. Current efforts are directed toward debugging the v15 results, and then upgrading to v16 input. The inputs are created from AODs using the TtHHbbDPDBasedAnalysis package (currently 00-04-18 and its branches are for v15, 00-04-19 is for v16). | |||||||
> > | Previous work went into producing samples for the Neural Net from AODs - results have previously been obtained for MC samples derived from v12 and v15 of athena, with work directed toward upgrading this to v16. The inputs are created from AODs using the TtHHbbDPDBasedAnalysis package (currently 00-04-18 and its branches are for v15, 00-04-19 is for v16), which can be found here![]() ![]() | |||||||
Current samples in use |
Line: 1 to 1 | ||||||||
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | ||||||||
Line: 519 to 519 | ||||||||
To run the script, first log into the batch system (ppepbs). The genemflat_batch_Complete2_SL5.sh script can be executed with the command: | ||||||||
Changed: | ||||||||
< < | ./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 1 1 /data/atlas07/ahgemmell/NTuple-v15-30Aug | |||||||
> > | ./genemflat_batch_Complete2_SL5.sh 12 480 1.0 tth 120 120 /data/atlas07/ahgemmell/NTuple-v15-30Aug | |||||||
These options denote: | ||||||||
Line: 533 to 533 | ||||||||
| ||||||||
Deleted: | ||||||||
< < | Note that it is possible to omit the training in subsequent (templating) runs, if it has previously been done (set 3rd from last argument to 0). This shortens the run time significantly. | |||||||
Once the job has been completed you will receive an email summarising the outcome. Running: | ||||||||
Line: 554 to 552 | ||||||||
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Added: | ||||||||
> > | Other switches to influence the runningIn genemflat_batch_Complete2_SL5.sh, at the start of the file there are a number of switches established:# Flags to limit the scope of the run if desired Computentps=1 DoTraining=1 ComputeTMVA=1 DoTemplates=1 DoStackedPlots=1 DoFit=1These control whether or not various parts of the code are run - the names of the flags are pretty self-explanatory about what parts of the code they control. For example, it is possible to omit the training in subsequent (templating) runs, if it has previously been done. This shortens the run time significantly. ***NOTE*** The flags DoTraining and DoTemplates had previously (until release 00-00-21) been set on the command line. They were moved from the command line when the other flags were introduced. | |||||||
Where the output is stored |
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It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts - general cleaning cuts and the like. (There was a previous version of our inputs where we also required 'sensible states' - for each candidate event we required it to reconstruct tops and Ws with vaguely realistic masses. However - this is a Neural Net analysis, so it has been decided to remove these cuts - they will after all in effect be reintroduced by the net itself if they would have been useful, and by not applying them ourselves, we are passing more information to the net.) We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in TMVAsteer.txt (created in genemflat_batch_Complete2.sh) (not currently used, as explained below) and TreeSpecATLAStth.txt. | ||||||||
Changed: | ||||||||
< < | In TreeSpecATLAStth.txt the filters control what is used for the templating, and Computentp: | |||||||
> > | TreeSpecATLAStth.txtIn TreeSpecATLAStth.txt, we establish the filters which control what is used for the templating, and Computentp: | |||||||
ListParameter SpecifyVariable:Higgs:cutMask 1 Type:int:Default:3 ListParameter SpecifyVariable:Higgs:invertWord 1 Type:int:Default:0InvertWord is used to invert the relevant bits (in this case no bits are inverted) before the cut from cutMask is applied. The cutMask will exclude from templating those events where the matching bits are equal to zero AFTER the inversion. So here, with no inversion applied, those events with my_failEvent == 3 will be used for templating. | ||||||||
Changed: | ||||||||
< < | The constraint | |||||||
> > | TMVAsteer.txt (genemflat_batch_Complete2.sh) | |||||||
GeneralParameter string 1 Constraint=(my_failEvent&3)==3 | ||||||||
Changed: | ||||||||
< < | in genemflat_batch_Complete2.sh controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first two bits are set, and not equal to zero), then the event is used for training. This filter is not used currently, as training of the net takes place based on the Computentp output - this Computentp output only contains sensible states (as specified in the TreeSpecATLAStth.txt file's filter). If further filtering is required, then care must be taken to ensure that my_failEvent (or whatever you wish to base your filter on) is specified in the VariableTreeToNTP file, so that Computentp will copy it into its output. | |||||||
> > | This controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first two bits are set, and not equal to zero), then the event is used for training. This filter is not used currently, as training of the net takes place based on the Computentp output - this Computentp output only contains sensible states (as specified in the TreeSpecATLAStth.txt file's filter). If further filtering is required, then care must be taken to ensure that my_failEvent (or whatever you wish to base your filter on) is specified in the VariableTreeToNTP file, so that Computentp will copy it into its output. | |||||||
Changed: | ||||||||
< < | If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints. | |||||||
> > | **If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints.** | |||||||
Running |
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Filters | ||||||||
Changed: | ||||||||
< < | It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts - general cleaning cuts and the like. (There was a previous version of our inputs where we also required 'sensible states' - for each candidate event we required it to reconstruct tops and Ws with vaguely realistic masses. However - this is a Neural Net analysis, so it has been decided to remove these cuts - they will after all in effect be reintroduced by the net itself if they would have been useful, and by not applying them ourselves, we are passing more information to the net.) We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in genemflat_batch_Complete2.sh (not currently used, as explained below) and TreeSpecATLAStth.txt. | |||||||
> > | It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts - general cleaning cuts and the like. (There was a previous version of our inputs where we also required 'sensible states' - for each candidate event we required it to reconstruct tops and Ws with vaguely realistic masses. However - this is a Neural Net analysis, so it has been decided to remove these cuts - they will after all in effect be reintroduced by the net itself if they would have been useful, and by not applying them ourselves, we are passing more information to the net.) We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in TMVAsteer.txt (created in genemflat_batch_Complete2.sh) (not currently used, as explained below) and TreeSpecATLAStth.txt. | |||||||
In TreeSpecATLAStth.txt the filters control what is used for the templating, and Computentp: |
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Filters | ||||||||
Changed: | ||||||||
< < | It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts and have 'sensible states'. The preselection cuts are easy enough to understand - they merely clean the sample - events which fail these are set to zero. However, it is possible for some events to have pass the cuts, but still not be anything like that which we would want (e.g. there is no way to reconstruct a top with a realistic mass). Seeing as it is likely that the backgrounds have more of these non-sensible states, to include them would be to give the ANN an unfair advantage in determining signal from background. But we don't want to simply set them equal to zero, as at other times we are going to be interested in how events passing preselection can fail to have sensible states, and so will want to examine them further. We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in genemflat_batch_Complete2.sh (not currently used, as explained below) and TreeSpecATLAStth.txt. | |||||||
> > | It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts - general cleaning cuts and the like. (There was a previous version of our inputs where we also required 'sensible states' - for each candidate event we required it to reconstruct tops and Ws with vaguely realistic masses. However - this is a Neural Net analysis, so it has been decided to remove these cuts - they will after all in effect be reintroduced by the net itself if they would have been useful, and by not applying them ourselves, we are passing more information to the net.) We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in genemflat_batch_Complete2.sh (not currently used, as explained below) and TreeSpecATLAStth.txt. | |||||||
In TreeSpecATLAStth.txt the filters control what is used for the templating, and Computentp: |
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Result of FlatAscii_global validation: OK Result of FlatTRntp validation: OK | ||||||||
Changed: | ||||||||
< < | Variables used by the Neural Net | |||||||
> > |
Variables used by the GlaNtp packageThe variables used by the package can be divided into two sets. The first are those variables that are constant throughout the sample - the 'global' variables (e.g. cross-section of the sample). These can be specified in their own tree, where they will be recorded (and read by GlaNtp) once only. If desired, these variables can be defined within the main tree of the input file - however, then they will be recorded once per event, and read in once per event. This is obviously a bit wasteful, but for historical reasons it can be done. The other variables are those that change on an event-by-event basis. These variables include both the variables we are going to train the Neural Net on (more information relevant to those variables is given in the relevant section of this TWiki), and other useful variables, such as filter flags (that tell GlaNtp whether an event is sensible or not). All of these variables are listed in the file VariableTreeToNTPATLASttHSemiLeptonic-v15.txt The file maps logical values to their branch/leaf. The tree can be the global tree or the event tree.GeneralParameter string 1 FlatTupleVar/<variable_name>=<tree>/<variable_name_in_tree>Also specified are the name of the leaf for the cutmask and invert word -- these are global values for a file. GeneralParameter string 1 CutMaskString=cutMask GeneralParameter string 1 InvertWordString=invertWordThere's also something about ListParameter EvInfoTree:1 1 NN_BJetWeight_Jet1:NN_BJetWeight_Jet1/NN_BJetWeight_Jet1that I need to ask Rick about... Variables used for training the Neural Net | |||||||
The list of variables on which the neural net is to train is set in the shell script, under TMVAvarset.txt (this file is created when the script runs). At present, these variables are: |
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The list of variables on which the neural net is to train is set in the shell script, under TMVAvarset.txt (this file is created when the script runs). At present, these variables are: | ||||||||
Changed: | ||||||||
< < | NN_BJetWeight_Jet1 NN_BJetWeight_Jet2 NN_BJetWeight_Jet3 NN_BJetWeight_Jet4 NN_BJetWeight_Jet5 NN_BJetWeight_Jet6 NN_BJet12_M NN_BJet13_M NN_BJet14_M NN_BJet23_M NN_BJet24_M NN_BJet34_M NN_BJet12_Pt NN_BJet13_Pt NN_BJet14_Pt NN_BJet23_Pt NN_BJet24_Pt NN_BJet34_Pt NN_State1_SumTopEt NN_State2_SumTopEt NN_State3_SumTopEt NN_State1_DiffTopEta NN_State2_DiffTopEta NN_State3_DiffTopEta NN_State1_DiffTopPhi NN_State2_DiffTopPhi NN_State3_DiffTopPhi | |||||||
> > | The b-weights for the six 'leading' jets - currently the jets are ranked according to their b-weights, but it is possible to rank them according to pT and energy. The decision about how to rank them is done in the AOD -> NTuple stage:
NN_BJetWeight_Jet1 NN_BJetWeight_Jet2 NN_BJetWeight_Jet3 NN_BJetWeight_Jet4 NN_BJetWeight_Jet5 NN_BJetWeight_Jet6 The masses and pT of the various jet combinations (only considering the four 'top' jets - i.e. if ranked by b-weights, the jets that we expect to really be b-jets in our signal: NN_BJet12_M NN_BJet13_M NN_BJet14_M NN_BJet23_M NN_BJet24_M NN_BJet34_M NN_BJet12_Pt NN_BJet13_Pt NN_BJet14_Pt NN_BJet23_Pt NN_BJet24_Pt NN_BJet34_Pt The sums of the eT of the two reconstructed tops, for each of the top three states: NN_State1_SumTopEt NN_State2_SumTopEt NN_State3_SumTopEt And the differences between the eta and phi of the two reconstructed tops, again from the top three states: NN_State1_DiffTopEta NN_State2_DiffTopEta NN_State3_DiffTopEta NN_State1_DiffTopPhi NN_State2_DiffTopPhi NN_State3_DiffTopPhi | |||||||
You also need to provide addresses to the Neural Net so that it can find the variables in the input trees. This is done inside VariableTreeToNTPATLASttHSemiLeptonic-v15.txt |
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Changed: | |||||||||||||||||||
< < | Issues still to be resolved1. In share/TtHHbbSetups.py:include( "AtlasGeoModel/SetGeometryVersion.py" ) include( "AtlasGeoModel/GeoModelInit.py" )Athena warns that both of these files are obsolete - this does not lead to an ERROR or WARNING, but nonetheless needs to be looked at. 2. In src/PreselectLeptons.cxx: const EMShower* shower; if ((*elecItr)->author() != egammaParameters::AuthorUnknown){ trackIsolationEnergy20 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.2); trackIsolationEnergy30 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.3); trackIsolationEnergy40 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.4); shower = (*elecItr)->detail<EMShower>(m_showerContainerName); //CCT: shower - seems to have stopped working in 15.6.0.2 for data made with r838 (v15.3.1.6) //was giving a seg fault as it tried to get etcone20 even if "shower" had not been successfully obtained! if (!shower) { mLog << MSG::WARNING << "Invalid EMShower pointer!" << endreq; }else{ etcone20 = shower->etcone20()/pT; mLog << MSG::INFO << "shower->etcone20() = " << shower->etcone20() << ", pT = " << pT << endreq; } } | ||||||||||||||||||
> > | Running the Neural Net | ||||||||||||||||||
Changed: | |||||||||||||||||||
< < | The warning message appears an awful lot - also, don't think this influences the Net inputs, but still should be looked at - do we need to get anything from the shower? | ||||||||||||||||||
> > | Things to do | ||||||||||||||||||
Changed: | |||||||||||||||||||
< < | 3. From the ye olde code, I noticed a 'placeholder' warning to check that W and top masses used in the sensible states are the same as in the generator (this hasn't been done yet). Also, perhaps we can tighten the sensible cut on the W mass? Seems rather wide at the mo (25GeV).... | ||||||||||||||||||
> > | * In the script used to make the webpage showing the results, the reference to H6AONN5MEMLP is hardwired. It should become a argument. It is the name of the method you give TMVA in the training, and so if it changes in one you should be able to change in the other | ||||||||||||||||||
Deleted: | |||||||||||||||||||
< < | Running the Neural Net | ||||||||||||||||||
Overview of the process |
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The Neural Net was then configured to exclude events where: (m_failEvent & 196608)==1 with 196608=131072+65536. | ||||||||
Added: | ||||||||
> > | Creating plots to review the dataThere is a simple shell script included in the running Neural Net code package that can produce a nice html document you can use to review a few plots of interest - plotTMVA.sh. To run it, move it into the run directory you want to review, then it's a simple one-line command:./plotTMVA.sh 120 <run> <job>N.B. This is done automatically by genemflat currently. | |||||||
Debugging the codeTo debug the code, two things need to be done - first, all the debug switches need to be turned on, and then you need to restrict the number of events to ~10 (for a Computentp run this will still manage to generate a 2 GB log file!). All of these switches are found in teststeerFlatReaderATLAStthSemileptonic.txt (the progenitor for all FlatReader files). The debug switches are: |
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Changed: | ||||||||
< < | This page has been substantially rewritten (and remains a work in progress) to focus just on the information required for a successful run of the Computentp and Neural Net package, to deliver exclusions. For information on results obtained using inputs created in v12 of athena, please refer to the archive. | |||||||
> > | This page has been substantially rewritten (and remains a work in progress) to focus just on the information required for a successful run of the Computentp and Neural Net package, to deliver exclusions. For information on results obtained using inputs created in v12 of athena, please refer to the archive. This page also describes how to run on GlaNtp - the version of the code set up for use in Glasgow, with no CDF dependencies. To use the previous version of the code (there are some important differences) refer to r93 and earlier. | |||||||
Project Aims | ||||||||
Line: 142 to 142 | ||||||||
The warning message appears an awful lot - also, don't think this influences the Net inputs, but still should be looked at - do we need to get anything from the shower? | ||||||||
Changed: | ||||||||
< < | 3. In the jobOptions file we currently have:
PreselectLeptons.McEventInfoName = "MyEvent"However, in the athena output we still have: StoreGateSvc WARNING retrieve(const): No valid proxy for object McEventInfo of type EventInfo(CLID 2101)Need to work out why this jobOption does not over-ride the default. It might itself be overridden by python/ttH_defaults.py - if this is the case, then a number of other settings are also over-ridden. 4. From the ye olde code, I noticed a 'placeholder' warning to check that W and top masses used in the sensible states are the same as in the generator (this hasn't been done yet). Also, perhaps we can tighten the sensible cut on the W mass? Seems rather wide at the mo (25GeV).... | |||||||
> > | 3. From the ye olde code, I noticed a 'placeholder' warning to check that W and top masses used in the sensible states are the same as in the generator (this hasn't been done yet). Also, perhaps we can tighten the sensible cut on the W mass? Seems rather wide at the mo (25GeV).... | |||||||
Running the Neural Net | ||||||||
Line: 230 to 216 | ||||||||
User SetupTo set up the neural net, | ||||||||
Changed: | ||||||||
< < |
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> > |
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Added: | ||||||||
> > | Getting a copy of GlaNtp
mkdir /home/ahgemmell/GlaNtp cd /home/ahgemmell/GlaNtp cp /home/stdenis/GlaNtpScript.sh .
cp /home/stdenis/atlas/testGlaNtp/cleanpath3.sh source cleanpath3.sh
export GLANTP_DATA=/data/cdf01/stdenis/GlaNtpData
./GlaNtpScript.sh SVN 00-00-10* This will check out everything, and run a few simple validations - the final output should look like this (i.e. don't be worried that not everything seems to have passed validation!): HwwFlatFitATLAS Validation succeeded Done with core tests Result of UtilBase validation: NOT DONE: NEED Result of Steer validation: OK Result of StringStringSet validation: OK Result of StringIntMap validation: OK Result of ItemCategoryMap validation: OK Result of FlatSystematic validation: OK Result of LJMetValues validation: OK Result of PhysicsProc validation: OK Result of FlatNonTriggerableFakeScale validation: OK Result of FlatProcessInfo validation: OK Result of PaletteList validation: OK Result of CutInterface validation: NOT DONE: NEED Result of NNWeight validation: NOT DONE: NEED Result of FlatFileMetadata validation: OK Result of FlatFileMetadataContainer validation: OK Result of Masks validation: NOT DONE: NEED Result of FFMetadata validation: OK Result of RUtil validation: NOT DONE: NEED Result of HistHolder validation: NOT DONE: NEED Result of GlaFlatFitCDF validation: OK Result of GlaFlatFitBigSysTableCDF validation: OK Result of GlaFlatFitBigSysTableNoScalingCDF validation: OK Result of GlaFlatFitATLAS validation: OK Result of FlatTuple validation: OK Result of FlatReWeight validation: OK Result of FlatReWeight_global validation: OK Result of FlatReWeightMVA validation: OK Result of FlatReWeightMVA_global validation: OK Result of TreeSpecGenerator validation: OK Result of FlatAscii validation: OK Result of FlatAscii_global validation: OK Result of FlatTRntp validation: OK | |||||||
Variables used by the Neural NetThe list of variables on which the neural net is to train is set in the shell script, under TMVAvarset.txt (this file is created when the script runs). At present, these variables are: |
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This file contains various parameters: | ||||||||
Changed: | ||||||||
< < | ColumnParameter BackgroundList 0 tt0j=0 | |||||||
> > | ColumnParameter BackgroundList 0 tt0j=0 ColumnParameter SignalList 1 ttH=1 | |||||||
Changed: | ||||||||
< < | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList or SignalList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but does not need to correspond to my_Eventtype. However, for completeness' sake within this file I have set it as such. | |||||||
> > | Here you specify once again the numbers assigned to the processes by my_Eventtype (for tt0j it equals zero), and list things as BackgroundList or SignalList. The number after 'BackgroundList' or 'SignalList' is unique for each process (to preserve the uniqueness of <tag>:<sequence>), but does not need to correspond to my_Eventtype. However, for completeness' sake within this file I have set it as such. The number at the end of this declaration (ttjj=0 in this case) needs to be sequential - it instructs the net of the order in which to process the samples, so it must go from 0 to n-1 (when you have n samples). | |||||||
ColumnParameter PseudoDataList 0 tt0j=0 | ||||||||
Changed: | ||||||||
< < | This is simply a restatement of the BackgroundList - the same numbers in the same place. This list specifies the processes included in the pseudoexperiments, and therefore the signal process is not included in this list. | |||||||
> > | This is simply a restatement of the BackgroundList (as we're looking for exclusion, the pseudodata is background only) - the same numbers in the same place. This list specifies the processes included in the pseudoexperiments, and therefore the signal process is not included in this list. | |||||||
ListParameter ProcessLabels:1 1 tt0j:t#bar{t}0j | ||||||||
Changed: | ||||||||
< < | The number after ProcessLabels again doesn't correspond to my_Eventtype - I have made it the same as the number after BackgroundList/SignalList and PseudoDataList. The important feature from this is that it tells the ANN what to label each of the various processes as in the results plots. | |||||||
> > | The number after ProcessLabels again doesn't correspond to my_Eventtype - I have made it the same as the number after BackgroundList/SignalList and PseudoDataList. The important feature from this is that it tells the ANN what to label each of the various processes as in the results plots. Again, the numbers must run from 0 to n-1. | |||||||
ColumnParameter UCSDPalette 0 tt0j=19 | ||||||||
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ColumnParameter ProcessOrder 0 tt0j=0 | ||||||||
Changed: | ||||||||
< < | Once again, the number on its own (in this case 0) is the same as the other such instances in this file. The final number (zero in this case) is the order in which this process should be plotted - i.e. in this case, the tt0j sample will be plotted first in the output, with the other samples piled on top of it. | |||||||
> > | Once again, the number on its own (in this case 0) is the same as the other such instances in this file. The final number (zero in this case) is the order in which this process should be plotted - i.e. in this case, the tt0j sample will be plotted first in the output, with the other samples piled on top of it. This number obviously does not need to correspond to my_Eventtype. | |||||||
FlatSysSetAtlastth1.txt |
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tr[run number].o[PBS job number] This output file is written as soon as the job stops, and contains a summary of the full run - useful for looking for error messages. | ||||||||
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> > | ||||||||
Limitations |
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Need to work out why this jobOption does not over-ride the default. It might itself be overridden by python/ttH_defaults.py - if this is the case, then a number of other settings are also over-ridden. | ||||||||
Added: | ||||||||
> > | 4. From the ye olde code, I noticed a 'placeholder' warning to check that W and top masses used in the sensible states are the same as in the generator (this hasn't been done yet). Also, perhaps we can tighten the sensible cut on the W mass? Seems rather wide at the mo (25GeV).... | |||||||
Running the Neural NetOverview of the process |
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This needs to be set to one if your input datasets have non-sensible states. Otherwise, global variables are loaded on one occasion from the first entry in the file, and kept. If this entry is not sensible, or for some other reason has an unreasonable answer for this global value, a problem will develop. With this switch on, the values will be loaded each and every time – obviously this slows the code down – if the global values are safely stored in every entry, it might be best to set this to false. | ||||||||
Added: | ||||||||
> > | TMVAsteer.txt (genemflat_batch_Complete2_SL5.sh)H6AONN5MEMLP MLP 1 !H:!V:NCycles=1000:HiddenLayers=N+1,N:RandomSeed=9876543If the phrase 'H6AONN5MEMLP' is changed, then this change must also be propogated to the webpage plotter (e-mail from Rick 1 Mar 2011) | |||||||
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Samples are produced for the Neural Net from AODs - results have previously been obtained for MC samples derived from v12 and v15 of athena. Current efforts are directed toward debugging the v15 results, and then upgrading to v16 input. The inputs are created from AODs using the TtHHbbDPDBasedAnalysis package (currently 00-04-18 and its branches are for v15, 00-04-19 is for v16). | ||||||||
Added: | ||||||||
> > | Current samples in useInput data and cross-sectionsThese cross-sections are for the overall process, at √s = 7 TeV. The ttH sample cross-sections are provided for the overall process - the MC is divided into two samples with W+ and W- independent of one another. These two samples are merged before being put through the ANN. The tt samples were initially generated to produce the equivalent of 75fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. **IMPORTANT** The Filter Efficiency for these samples was calculated based on a no-pileup sample. The filter is generator level, and one of the things it will cut an event for is not enough jets. However, pileup adds jets, but these are added well after the filter. The net result is that a number of events that failed the filter would have passed, had the pileup been added earlier in the process. This means the filter efficiency (and thus the cross-sections) are incorrect, by a yet to determined amount.... For the other samples, however, we do need to worry about branching ratios - the quoted initial cross-section includes all final states, so we need to apply branching ratios to the cross-section to reduce it down, so that it reflects the sample we've generated. We then subsequently need to reduce the cross-section further so that it reflects the number of sensible states.<-- /editTable --> These cross-sections and branching ratios are correct as of 8 Feb 2011. qq→ttbb (EWK) is currently not being used, thanks to a bug in the production of the MC Number of events surviving preselection, weights and TrainWeights(See later in the TWiki for an explanation of weights and TrainWeights.) This table will be completed with all the relevant weights and TrainWeights at a later date - these values are to be compared to the output from Computentp to ensure everything is working as intended, and are calculated for the sensible cross-sections/events. (A quick check of the TrainWeight is to multiply the number so events of each background by their TrainWeight and sum them - by design, this should equal the number of entries in the ttH sample.)<-- /editTable --> | |||||||
Issues still to be resolved1. In share/TtHHbbSetups.py: | ||||||||
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Currently, the type of event you are looking at is determined by looking at my_failEvent. States failing preselection have this equal to 0, passing preselection but not having a sensible final state equal 1 and passing preselection and having a sensible final state equal 3. These numbers are the basis of a number of bitwise tests - thus when setting your own my_failEvents, consider which bits in a binary string you want to represent various things, and then convert those to decimal. | ||||||||
Deleted: | ||||||||
< < | Current samples in useInput data and cross-sectionsThese cross-sections are for the overall process, at √s = 7 TeV. The ttH sample cross-sections are provided for the overall process - the MC is divided into two samples with W+ and W- independent of one another. These two samples are merged before being put through the ANN. The tt samples were initially generated to produce the equivalent of 75fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. **IMPORTANT** The Filter Efficiency for these samples was calculated based on a no-pileup sample. The filter is generator level, and one of the things it will cut an event for is not enough jets. However, pileup adds jets, but these are added well after the filter. The net result is that a number of events that failed the filter would have passed, had the pileup been added earlier in the process. This means the filter efficiency (and thus the cross-sections) are incorrect, by a yet to determined amount.... For the other samples, however, we do need to worry about branching ratios - the quoted initial cross-section includes all final states, so we need to apply branching ratios to the cross-section to reduce it down, so that it reflects the sample we've generated. We then subsequently need to reduce the cross-section further so that it reflects the number of sensible states.<-- /editTable --> These cross-sections and branching ratios are correct as of 8 Feb 2011. qq→ttbb (EWK) is currently not being used, thanks to a bug in the production of the MC Number of events surviving preselection, weights and TrainWeightsThis table will be completed with all the relevant weights and TrainWeights at a later date - these values are to be compared to the output from Computentp to ensure everything is working as intended, and are calculated for the sensible cross-sections/events. (A quick check of the TrainWeight is to multiply the number so events of each background by their TrainWeight and sum them - by design, this should equal the number of entries in the ttH sample.)<-- /editTable --> | |||||||
Setting Systematic UncertaintiesThe fitting code can take into account two different types of systematic uncertainty - rate and shape. The basic method to obtain both these uncertainties is that you should make your input samples for both your nominal sample, and for the two bounds of a given error (e.g. Initial State Radiation, ISR). Repeat this for all of the errors you wish to consider. The rate systematic uncertainty is simply how the number of events change that pass your preselection cuts etc. (you can only consider this, if you like). To obtain the shape uncertainty, you should pass each of the resulting datasets through the ANN (up to and including the templating, so that you have ANN results for both the nominal results, and as a result of varying each background). These ANN outputs can then be used to produce the rate uncertainties based on their integrals, before being normalised to the nominal cross-section so as to find the shape uncertainty - a measure of the percentage change in the bin-by-bin distribution for each error. |
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The key feature of a neural network is its ability to be "trained" to recognise patterns in data, allowing high efficiency algorithms to be developed with relative ease. This training is typically done with sample data which has been generated artificially, resulting in an algorithm that is very effective at recognising certain patterns in data sets. The only shortcoming is the danger of "over-training" an ANN, meaning that it becomes overly discriminating and searches across a narrower range of patterns than is desired (one countermeasure is to add extra noise to training data). Computentp :- Simply running the code as above will result in less than optimal Neural Net training. The training procedure requires equal numbers of events from signal and from background (in this case it results in half of the signal events being used in training, half for testing). However, the above code will take events from the background signal samples in proportion to the file sizes - these result in proportions not quite in accordance with physical ratios. As the Neural Net weights results according to information about the cross-section of the process and so on stored in the tree, the final result is that while the outputs are weighted in a physical fashion, the Net is not trained to the same ratios, and so is not optimally trained. To solve this problem, Computentp is used to mix together all background and signal samples., and assign TrainWeights to them, so that the events are weighted correctly for the Net's training. | ||||||||
Added: | ||||||||
> > |
Preparing samples for the Neural NetSamples are produced for the Neural Net from AODs - results have previously been obtained for MC samples derived from v12 and v15 of athena. Current efforts are directed toward debugging the v15 results, and then upgrading to v16 input. The inputs are created from AODs using the TtHHbbDPDBasedAnalysis package (currently 00-04-18 and its branches are for v15, 00-04-19 is for v16).Issues still to be resolved1. In share/TtHHbbSetups.py:include( "AtlasGeoModel/SetGeometryVersion.py" ) include( "AtlasGeoModel/GeoModelInit.py" )Athena warns that both of these files are obsolete - this does not lead to an ERROR or WARNING, but nonetheless needs to be looked at. 2. In src/PreselectLeptons.cxx: const EMShower* shower; if ((*elecItr)->author() != egammaParameters::AuthorUnknown){ trackIsolationEnergy20 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.2); trackIsolationEnergy30 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.3); trackIsolationEnergy40 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.4); shower = (*elecItr)->detail<EMShower>(m_showerContainerName); //CCT: shower - seems to have stopped working in 15.6.0.2 for data made with r838 (v15.3.1.6) //was giving a seg fault as it tried to get etcone20 even if "shower" had not been successfully obtained! if (!shower) { mLog << MSG::WARNING << "Invalid EMShower pointer!" << endreq; }else{ etcone20 = shower->etcone20()/pT; mLog << MSG::INFO << "shower->etcone20() = " << shower->etcone20() << ", pT = " << pT << endreq; } }The warning message appears an awful lot - also, don't think this influences the Net inputs, but still should be looked at - do we need to get anything from the shower? 3. In the jobOptions file we currently have: PreselectLeptons.McEventInfoName = "MyEvent"However, in the athena output we still have: StoreGateSvc WARNING retrieve(const): No valid proxy for object McEventInfo of type EventInfo(CLID 2101)Need to work out why this jobOption does not over-ride the default. It might itself be overridden by python/ttH_defaults.py - if this is the case, then a number of other settings are also over-ridden. | |||||||
Running the Neural NetOverview of the process |
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GeneralParameter string 1 Constraint=(my_failEvent&3)==3 | ||||||||
Changed: | ||||||||
< < | in genemflat_batch_Complete2.sh controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first to bits are not set, and are equal to zero), then the event is used for training. This filter is not used currently, as training of the net takes place based on the Computentp output - this Computentp output only contains sensible states (as specified in the TreeSpecATLAStth.txt file's filter). If further filtering is required, then care must be taken to ensure that my_failEvent (or whatever you wish to base your filter on) is specified in the VariableTreeToNTP file, so that Computentp will copy it into its output. | |||||||
> > | in genemflat_batch_Complete2.sh controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first two bits are set, and not equal to zero), then the event is used for training. This filter is not used currently, as training of the net takes place based on the Computentp output - this Computentp output only contains sensible states (as specified in the TreeSpecATLAStth.txt file's filter). If further filtering is required, then care must be taken to ensure that my_failEvent (or whatever you wish to base your filter on) is specified in the VariableTreeToNTP file, so that Computentp will copy it into its output. | |||||||
If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints. |
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Filters | ||||||||
Changed: | ||||||||
< < | It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts and have 'sensible states'. The preselection cuts are easy enough to understand - they merely clean the sample - events which fail these are set to zero. However, it is possible for some events to have pass the cuts, but still not be anything like that which we would want (e.g. there is no way to reconstruct a top with a realistic mass). Seeing as it is likely that the backgrounds have more of these non-sensible states, to include them would be to give the ANN an unfair advantage in determining signal from background. But we don't want to simply set them equal to zero, as at other times we are going to be interested in how events passing preselection can fail to have sensible states, and so will want to examine them further. We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in genemflat_batch_Complete2.sh and TreeSpecATLAStth.txt.
The constraint
GeneralParameter string 1 Constraint=(my_failEvent&3)==0in genemflat_batch_Complete2.sh controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first to bits are not set, and are equal to zero), then the event is used for training. If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints. | |||||||
> > | It is possible for the inputs to the ANN to have more events in than those that you want to pass to on for processing. We only want to train the ANN on those samples that would pass our preselection cuts and have 'sensible states'. The preselection cuts are easy enough to understand - they merely clean the sample - events which fail these are set to zero. However, it is possible for some events to have pass the cuts, but still not be anything like that which we would want (e.g. there is no way to reconstruct a top with a realistic mass). Seeing as it is likely that the backgrounds have more of these non-sensible states, to include them would be to give the ANN an unfair advantage in determining signal from background. But we don't want to simply set them equal to zero, as at other times we are going to be interested in how events passing preselection can fail to have sensible states, and so will want to examine them further. We therefore have filters so that Computentp and the ANN only look at events of our choosing. These filters take the place of various bitwise tests in genemflat_batch_Complete2.sh (not currently used, as explained below) and TreeSpecATLAStth.txt. | |||||||
In TreeSpecATLAStth.txt the filters control what is used for the templating, and Computentp: | ||||||||
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InvertWord is used to invert the relevant bits (in this case no bits are inverted) before the cut from cutMask is applied. The cutMask will exclude from templating those events where the matching bits are equal to zero AFTER the inversion. So here, with no inversion applied, those events with my_failEvent == 3 will be used for templating. | ||||||||
Changed: | ||||||||
< < | **NOTE** The above example is inconsistent - the Constraint excludes those that have my_failEvent==3, while the InvertWord/CutMask excludes those that have my_failEvent!=3. This requires some working out to make sure everything works, and is ongoing.... | |||||||
> > | The constraint
GeneralParameter string 1 Constraint=(my_failEvent&3)==3in genemflat_batch_Complete2.sh controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first to bits are not set, and are equal to zero), then the event is used for training. This filter is not used currently, as training of the net takes place based on the Computentp output - this Computentp output only contains sensible states (as specified in the TreeSpecATLAStth.txt file's filter). If further filtering is required, then care must be taken to ensure that my_failEvent (or whatever you wish to base your filter on) is specified in the VariableTreeToNTP file, so that Computentp will copy it into its output. If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints. | |||||||
Running |
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in genemflat_batch_Complete2.sh controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first to bits are not set, and are equal to zero), then the event is used for training. | ||||||||
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> > | If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&. This is because USEHILOSB adds more constraints. | |||||||
In TreeSpecATLAStth.txt the filters control what is used for the templating, and Computentp:
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NN_BJetWeight_Jet1 NN_BJetWeight_Jet2 NN_BJetWeight_Jet3 NN_BJetWeight_Jet4 NN_BJetWeight_Jet5 NN_BJetWeight_Jet6 NN_BJet12_M NN_BJet13_M NN_BJet14_M NN_BJet23_M NN_BJet24_M NN_BJet34_M NN_BJet12_Pt NN_BJet13_Pt NN_BJet14_Pt NN_BJet23_Pt NN_BJet24_Pt NN_BJet34_Pt NN_State1_SumTopEt NN_State2_SumTopEt NN_State3_SumTopEt NN_State1_DiffTopEta NN_State2_DiffTopEta NN_State3_DiffTopEta NN_State1_DiffTopPhi NN_State2_DiffTopPhi NN_State3_DiffTopPhi | ||||||||||||||||||||||||||||
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> > | You also need to provide addresses to the Neural Net so that it can find the variables in the input trees. This is done inside VariableTreeToNTPATLASttHSemiLeptonic-v15.txt
ListParameter EvInfoTree:1 1 NN_BJetWeight_Jet1:NN_BJetWeight_Jet1/NN_BJetWeight_Jet1Currently all information is in the EvInfoTree, which provides event level information. However, future work will involve trying to establish a GlobalInfoTree, which contains information about the entire sample, such as cross-section - this will only need to be loaded once, and saves having to write the same information into the tree repeatedly, and subsequently reading it repeatedly. | |||||||||||||||||||||||||||
Variable Weights in the Neural NetTo set up a neural net for the analysis of a particular kind of data it is necessary to train it with sample data; this process will adjust the "weights" on each variable that the neural net analyses in the ntuple, in order to optimise performance. These weights can then be viewed as a scatter plot in ROOT. | ||||||||||||||||||||||||||||
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This table will be completed with all the relevant weights and TrainWeights at a later date - these values are to be compared to the output from Computentp to ensure everything is working as intended, and are calculated for the sensible cross-sections/events. (A quick check of the TrainWeight is to multiply the number so events of each background by their TrainWeight and sum them - by design, this should equal the number of entries in the ttH sample.) | ||||||||||||||||||||||||||||
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The first parameter consists of two parts in this example: 'Combine' and 'Lumi'. The second part is the name of the uncertainty being considered. The first part 'Combine' (and the associated semicolon between them) is optional. It tells the ANN that the uncertainty thus labelled are independent of each other, and can be added in quadrature. 'OnOff' obviously tells the ANN to consider those uncertainty (1) or not (0). 'Low' and 'High' establish the relevant bounds of the uncertainty as fractions of the total (however, for the ANN these uncertainties are symmetrised, so to save time they are here assumed to be symmetric unless elsewhere stated) - note that these are not the uncertainties on the quantity, but rather the effect of that uncertainty on the rate of your process. Process is not actually read by the ANN, but is there to make the whole thing more human-friendly to read. The current errors, and their bounds are below. If no source for these error bounds is given, then they were the defaults found in the files from time immemorial (where as necessary I assumed that all tt + X errors were the same, as were all ttbb (QCD) errors, as in the original files the only samples considered were ttjj, ttbb(EWK), ttbb(QCD) and ttH - these errors probably originate from the CSC note). If you are only considering rate uncertainties, this is where the fitting code will find the relevant numbers. | ||||||||||||||||||||||||||||
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< < | in genemflat_batch_Complete2.sh will exclude from training those events where the above constraint is false. So in the above example, only events with the first two bits equal to one will pass the filter. | |||||||||||||||||||||||||||
> > | in genemflat_batch_Complete2.sh controls the events used in the training, using a bitwise comparison. If the constraint is true (i.e. the first to bits are not set, and are equal to zero), then the event is used for training. | |||||||||||||||||||||||||||
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< < | In TreeSpecATLAStth.txt the filters are set with: | |||||||||||||||||||||||||||
> > | In TreeSpecATLAStth.txt the filters control what is used for the templating, and Computentp: | |||||||||||||||||||||||||||
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< < | ListParameter SpecifyVariable:Higgs:cutMask 1 Type:int:Default:1 | |||||||||||||||||||||||||||
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< < | The constraint is hardwired to be off the form where my_failEvent&cutMask==0 would fail the event, exaclty like the constraint. However, it is not beyond the realms of possibility where you want events with bits of my_failEvent set to zero to pass, not fail. In genemflat this is easily done by changing ==0 into ==1 - however, we cannot directly do this in TreeSpec. To get around the problem we have invertWord - this simply flips the relevant bits in my_failEvent before passing them to the test. | |||||||||||||||||||||||||||
> > | InvertWord is used to invert the relevant bits (in this case no bits are inverted) before the cut from cutMask is applied. The cutMask will exclude from templating those events where the matching bits are equal to zero AFTER the inversion. So here, with no inversion applied, those events with my_failEvent == 3 will be used for templating. **NOTE** The above example is inconsistent - the Constraint excludes those that have my_failEvent==3, while the InvertWord/CutMask excludes those that have my_failEvent!=3. This requires some working out to make sure everything works, and is ongoing.... | |||||||||||||||||||||||||||
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The tt samples were initially generated to produce the equivalent of 75fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. | |||||||||||||||||||||||||
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> > | **IMPORTANT** The Filter Efficiency for these samples was calculated based on a no-pileup sample. The filter is generator level, and one of the things it will cut an event for is not enough jets. However, pileup adds jets, but these are added well after the filter. The net result is that a number of events that failed the filter would have passed, had the pileup been added earlier in the process. This means the filter efficiency (and thus the cross-sections) are incorrect, by a yet to determined amount.... | ||||||||||||||||||||||||
For the other samples, however, we do need to worry about branching ratios - the quoted initial cross-section includes all final states, so we need to apply branching ratios to the cross-section to reduce it down, so that it reflects the sample we've generated. We then subsequently need to reduce the cross-section further so that it reflects the number of sensible states. | |||||||||||||||||||||||||
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For the other samples, however, we do need to worry about branching ratios - the quoted initial cross-section includes all final states, so we need to apply branching ratios to the cross-section to reduce it down, so that it reflects the sample we've generated. We then subsequently need to reduce the cross-section further so that it reflects the number of sensible states. | |||||||||||||||||||||||||
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These cross-sections and branching ratios are correct as of 8 Feb 2011. qq→ttbb (EWK) is currently not being used, thanks to a bug in the production of the MC |
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< < | These cross-sections are for the overall process, at √s = 7 TeV. The 'final effective cross-section' is the cross-section for the sample we're looking at in in its entirety. For processing in the ANN this is further modified after an initial run by the ANN production code, based on how many events of the total cross-section produce sensible states - the ANN trains only on sensible states, so we must use the cross-section relevant to those in our final analysis. | |||||||||||||||||||||||||||
> > | These cross-sections are for the overall process, at √s = 7 TeV. | |||||||||||||||||||||||||||
The ttH sample cross-sections are provided for the overall process - the MC is divided into two samples with W+ and W- independent of one another. These two samples are merged before being put through the ANN. | ||||||||||||||||||||||||||||
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> > | The tt samples were initially generated to produce the equivalent of 75fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. For the other samples, however, we do need to worry about branching ratios - the quoted initial cross-section includes all final states, so we need to apply branching ratios to the cross-section to reduce it down, so that it reflects the sample we've generated. We then subsequently need to reduce the cross-section further so that it reflects the number of sensible states. | |||||||||||||||||||||||||||
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The <sequence> parameter (in this case '0') is there so that you can specify the parameters for a given error for multiple channels, without falling foul of the uniqueness requirement for <tag>:<sequence>. We have chosen it so that it equals my_Eventtype for that process. 'Channel' is present just in case you're considering multiple channels. We're only considering the one channel in this case (SemiLeptonic). The final parameter (Process) is not actually used - the second parameter tells the ANN which errors are which, but this isn't very easily read by you, so feel free to add it in to help you keep track of the various errors! These final few parameters can be placed in any order, so long as they are separated by semicolons. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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> > | Passing preselection and sensible statesMany of the generated events in our samples will not pass the preselection cuts we would use in our final analysis. Sometimes to pass preselection requires some mistakes on the part of the reconstruction (e.g. tt + 0j), othertimes to fail preselection requires either the final state particles to be inherently unsuitable for our reconstruction, or to be mis-reconstructed. However, even if an event passes preselection it is possible that the events as reconstructed give a nonsensical final state - for example, the the light jets might not be able to be combined in such a way as to give a reasonable value of the W mass. Based on a few simple mass cuts, an event passing preselection can be determined to have a sensible state or not. Currently, the type of event you are looking at is determined by looking at my_failEvent. States failing preselection have this equal to 0, passing preselection but not having a sensible final state equal 1 and passing preselection and having a sensible final state equal 3. These numbers are the basis of a number of bitwise tests - thus when setting your own my_failEvents, consider which bits in a binary string you want to represent various things, and then convert those to decimal. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current samples in useInput data and cross-sectionsThese cross-sections are for the overall process, at √s = 7 TeV. The 'final effective cross-section' is the cross-section for the sample we're looking at in in its entirety. For processing in the ANN this is further modified after an initial run by the ANN production code, based on how many events of the total cross-section produce sensible states - the ANN trains only on sensible states, so we must use the cross-section relevant to those in our final analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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> > | The ttH sample cross-sections are provided for the overall process - the MC is divided into two samples with W+ and W- independent of one another. These two samples are merged before being put through the ANN. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number of events surviving preselection, weights and TrainWeights | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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> > | This table will be completed with all the relevant weights and TrainWeights at a later date - these values are to be compared to the output from Computentp to ensure everything is working as intended, and are calculated for the sensible cross-sections/events. (A quick check of the TrainWeight is to multiply the number so events of each background by their TrainWeight and sum them - by design, this should equal the number of entries in the ttH sample.)
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Setting Systematic UncertaintiesThe fitting code can take into account two different types of systematic uncertainty - rate and shape. The basic method to obtain both these uncertainties is that you should make your input samples for both your nominal sample, and for the two bounds of a given error (e.g. Initial State Radiation, ISR). Repeat this for all of the errors you wish to consider. The rate systematic uncertainty is simply how the number of events change that pass your preselection cuts etc. (you can only consider this, if you like). To obtain the shape uncertainty, you should pass each of the resulting datasets through the ANN (up to and including the templating, so that you have ANN results for both the nominal results, and as a result of varying each background). These ANN outputs can then be used to produce the rate uncertainties based on their integrals, before being normalised to the nominal cross-section so as to find the shape uncertainty - a measure of the percentage change in the bin-by-bin distribution for each error. |
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<-- p { margin-bottom: 0.21cm; }h1 { margin-bottom: 0.21cm; }h1.western { font-family: "Liberation Serif",serif; }h1.cjk { font-family: "DejaVu Sans"; }h1.ctl { font-family: "DejaVu Sans"; }h2 { margin-bottom: 0.21cm; }h4 { margin-bottom: 0.21cm; }h5 { margin-bottom: 0.21cm; }h3 { margin-bottom: 0.21cm; }h3.western { font-family: "Liberation Serif",serif; }h3.cjk { font-family: "DejaVu Sans"; }h3.ctl { font-family: "DejaVu Sans"; }pre.cjk { font-family: "DejaVu Sans",monospace; }a:link { } --> Computentp, Neural Nets and MCLIMITS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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The <sequence> parameter (in this case '0') is there so that you can specify the parameters for a given error for multiple channels, without falling foul of the uniqueness requirement for <tag>:<sequence>. We have chosen it so that it equals my_Eventtype for that process. 'Channel' is present just in case you're considering multiple channels. We're only considering the one channel in this case (SemiLeptonic). The final parameter (Process) is not actually used - the second parameter tells the ANN which errors are which, but this isn't very easily read by you, so feel free to add it in to help you keep track of the various errors! These final few parameters can be placed in any order, so long as they are separated by semicolons. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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< < | Current samples in use and the relevant cross-sections | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
> > | Current samples in useInput data and cross-sections | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
These cross-sections are for the overall process, at √s = 7 TeV. The 'final effective cross-section' is the cross-section for the sample we're looking at in in its entirety. For processing in the ANN this is further modified after an initial run by the ANN production code, based on how many events of the total cross-section produce sensible states - the ANN trains only on sensible states, so we must use the cross-section relevant to those in our final analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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These cross-sections and branching ratios are correct as of 8 Feb 2011. qq→ttbb (EWK) is currently not being used, thanks to a bug in the production of the MC | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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> > | Number of events surviving preselection, weights and TrainWeights | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Setting Systematic UncertaintiesThe fitting code can take into account two different types of systematic uncertainty - rate and shape. The basic method to obtain both these uncertainties is that you should make your input samples for both your nominal sample, and for the two bounds of a given error (e.g. Initial State Radiation, ISR). Repeat this for all of the errors you wish to consider. The rate systematic uncertainty is simply how the number of events change that pass your preselection cuts etc. (you can only consider this, if you like). To obtain the shape uncertainty, you should pass each of the resulting datasets through the ANN (up to and including the templating, so that you have ANN results for both the nominal results, and as a result of varying each background). These ANN outputs can then be used to produce the rate uncertainties based on their integrals, before being normalised to the nominal cross-section |