Difference: AtlasDataAnalysis (139 vs. 140)

Revision 1402012-02-13 - AlistairGemmell

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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....

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Setting which variables to plot and train on

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).

VariableTreeToNTPATLASttHSemiLeptonic-v16.txt

ListParameter   EvInfoTree:1  1 my_NN_BJetWeight_Jet1:my_NN_BJetWeight_Jet1/my_NN_BJetWeight_Jet1

This 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.txt

ListParameter    SpecifyVariable:my_NN_BJetWeight_Jet1  1 Type:double

This 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.txt

ColumnParameter   SpecifyHist:my_NN_BJetWeight_Jet1    0  OnOff=1:Min=-5:Max=10

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

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 Uncertainties

The 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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