Difference: AtlasDataAnalysis (93 vs. 94)

Revision 942011-05-23 - AlistairGemmell

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Computentp, Neural Nets and MCLIMITS

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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.
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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.
 

Project Aims

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  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?
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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)....

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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

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User Setup

To set up the neural net,

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  • check out the latest version from subversion (check what this is on trac) using the command

    svn co https://ppesvn.physics.gla.ac.uk/svn/atlas/NNFitter/tags/NNFitter-00-00-0X %BR%

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  • check out the latest version of the code running framework from subversion (check what this is on trac) using the command

    svn co https://ppesvn.physics.gla.ac.uk/svn/atlas/NNFitter/tags/NNFitter-00-00-0X %BR%

  • check out a version of the GlaNtp code into your home directory (or set up genemflat_batch_Complete2_SL5.sh to point at someone else's installation of the the code). The procedure for how to do this is described in the next section.

 
  • ensure you know the ntuple_area variable to be passed in at run-time to genemflat_batch_Complete2_SL5.sh. This will be the directory where the input ntuples are stored (currently /data/atlas07/ahgemmell/NTuple-v15-30Aug for ntuples which have sensible states, events passing preselection and events failing preselection having my_failEvent == 3, 1 and 0 respectively – note that by their very nature, sensible states have also passed preselection).

  • The BASEBATCHDIR is now set automatically to the working directory when the script is executed.

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Getting a copy of GlaNtp

  • Set yourself up for access into SVN (e.g.

    svn proxy init

  • Create the directory where you want to set up your copy, and copy in the setup script:

mkdir /home/ahgemmell/GlaNtp
cd /home/ahgemmell/GlaNtp
cp /home/stdenis/GlaNtpScript.sh .

  • To avoid any potential problems with previously set up aliases etc, clear them all (you will need to do this during the running of the code, so you might as well copy it into your directory):

cp /home/stdenis/atlas/testGlaNtp/cleanpath3.sh
source cleanpath3.sh

  • Make a directory to hold the code itself:

    mkdir GlaNtpPackage

  • GlaNtpScript.sh not only checks out and compiles the code, it also then goes and validates it. You need to define the input for this:

export GLANTP_DATA=/data/cdf01/stdenis/GlaNtpData

  • You now run the script, specifying whether you want a specific tag (e.g. 00-00-10), or just from the head of the trunk (h) so you're more free to play around with it. It's always a good idea to check out a specific tag, so that whatever you do to the head, you can still run over a valid release.

./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 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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