Difference: JetFlavourTagging (29 vs. 30)

Jet Finder and Truth Tagging

  <group name="JetFinders"/>
  <group name="MyTrueAngularJetFlavourProcessorCollection"/>
  <processor name="IPRPCutProcessor"/>
  <processor name="MyPerEventIPFitterProcessor"/>
  <processor name="ZVRESRPCutProcessor"/>
  <processor name="MyZVTOP_ZVRES"/>
  <processor name="FTRPCutProcessor"/>
  <processor name="MyFlavourTagInputsProcessor"/>
  <processor name="MyLCIOOutputProcessor"/>  

The JetFinder processor reconstructs 2 and 4 jets events from the input collection (LooseSelectedPandoraPFANewPFOs was used). For the reconstructed 4 jets, MyTrueAngularJetFlavourProcessor determines MC Jet Flavour by angular matching of heavy quarks to jets, and also determines hadronic and partonic charge of the jet. The LCIOOutput processor creates new slcio files containing the new collections added by the above processors.

The LCFI processors have the following functions:

• IPRPCut - selects Reconstructed Particles based on track parameters, number of hits etc.
• MyPerEventIPFitter - determines IP position and error from the tracks in an event by simple fitting
• ZVRESRPCut - applies cuts on the d0 and z0 values of the track
• MyZVTOP_ZVRES - topological vertex finder
• FTRPCut - flavour tagging reconstructed particle cuts (on d0, z0 and P_T)
• MyFlavourTagInputs - from vertices and tracks calculates discriminating variables for the neural net

Table of input and output collections for our setup (one can choose other names, of course):

Our input slcio files contain the collections: LooseSelectedPandoraPFANewPFOs, MCParticlesSkimmed, PandoraPFANewClusters, PandoraPFANewPFOs, PandoraPFANewReclusterMonitoring, ProngVertices, RecoMCTruthLink, SelectedLDCTracks, SelectedPandoraPFANewPFOs, TightSelectedPandoraPFANewPFOs and V0Vertices.

The processors listed above could be run in sequence, or split in several steps, invoking a LCIOOutput processor to write intermediate slcio outputs at every step. Here's a script for that, where the intermediate xml files are slight modifications of the files provided in LCFIVertex/steering examples. We found that the most time-consuming processor is ZVTOP_ZVRES with more than 10 s/event.

We found it easier to run processors 1 to 6 on batches of 10 input files, and save the outputs as /afs/phas.gla.ac.uk/data/ilc/datasets01/1.4tev/ZVRES_out/runX_out.slcio. Then we ran processors 7 and 8 on these files to produce a new set ftiX_out.slcio. We used half of these files to train the neural net, but then again was easier to run flavour tagging (see below) on the individual ftiX_out.slcio files. We used the other half of the files files for the purity vs. efficiency plots (see below). These slcio output files were stored in /afs/phas.gla.ac.uk/data/ilc/datasets01/1.4tev/ZVRES_out/ as well.

Troubleshooting: The b3_D0CutValue parameter of the IPRPCutProcessor was set to 5O instead of 50, and was causing a crash. For the ZVRESRPCut processor, h1_MCPIDEnable had to be set to false. See also this post.

Neural Network Training

In the previous step, we have extracted the discriminating parameters and truth-tagged the jets. The slcio files created are stored in:

/afs/phas.gla.ac.uk/data/ilc/datasets01/1.4tev/ZVRES_out/

  <processor name="MyNeuralNetTrainer" type="GlasgowNeuralNetTrainer"/>

// Theta cut parameters: _jetThetaAngleCut < theta < (180 - _jetThetaAngleCut)
        registerProcessorParameter( "JetThetaAngleCut" ,
                                    "Cut on the jets theta angle"  ,
                                    _jetThetaAngleCut,
                                    (float)15.);

<!-- Jet theta angle cut (only the Glasgow NN trainer has this option) -->
                <parameter name="JetThetaAngleCut" type="float"> 24. </parameter>

Flavour Tagging

Now we are ready to employ the FlavourTag processor, which will do flavour tagging using the neural nets trained in the previous step. The input slcio files contain the FlavourTagInputs and FTSelectedJets (or Durham_4Jets, not sure if there's a difference at this level) collections.

  <processor name="MyFlavourTag"/>
  <processor name="MyLCIOOutputProcessor"/>  

Purity and Efficiency Studies

To determine the optimal cut for our b-tagging, a purity vs. efficiency study needs to be done. We use the MakePurityVsEfficiencyRootPlot.C macro provided by the LCFIVertex package. First, we have to run:

  <processor name="MyAIDAProcessor"/>
  <processor name="MyPlot"/>
  <processor name="MyLCFIAIDAPlotProcessor"/>

<parameter name="TrueJetFlavourCollection" type="string">TrueJetFlavour_4Jets </parameter>
<!--In fti-steer.xml this parameter is called "FlavourTagCollection", without the 's' -->  
<parameter name="FlavourTagCollections" type="string">FlavourTagGla </parameter>

FIND_PACKAGE( ROOT REQUIRED )
FOREACH( pkg ROOT ) 
 IF( ${pkg}_FOUND )
  INCLUDE_DIRECTORIES( ${${pkg}_INCLUDE_DIRS} )
  ADD_DEFINITIONS( ${${pkg}_DEFINITIONS} )
 ENDIF()
ENDFOREACH()

Once the AIDA Plots processors are run via Marlin, a text file PurityEfficiencyOutput.txt and a RAIDA root file are produced. We customised the MakePurityVsEfficiencyRootPlot.C macro and ran it to use the RAIDA file as input to produce the purity vs. efficiency plots:

root -l MakePurityVsEfficiencyRootPlotGla.C

The cut values and corresponding purity and efficiencies are tabulated in the file PurityEfficiencyOutput.txt. We used another Root macro to plot these values and determine the optimum b-cut.

For b-tagging, we've compared the plot produced by MakePurityVsEfficiencyRootPlotGla.C and graphs drawn with the data tabulated in PurityEfficiencyOutput.txt, for 1, 2, 3 (corresponding to distinct neural networks) or any number of vertices (which we don't know yet how to interpret):

Here's the script used to extract the numbers from PurityEfficiencyOutput.txt

Adding background

Up to now all that input slcio files contained was signal, so the 'background' in the plots above is not really background. To add background, we've downloaded 1.4!TeV eeqq files to:

/afs/phas.gla.ac.uk/data/ilc/datasets01/1.4tev/qq/ILD/DST/00002091/000/

Training the neural nets with eeqq background added

We have trained another set of neural nets but adding this time eeqq background to the input. The resulting neural nets (25k signal + 25k background) are here: bnets.tgz.

Using an alternate Jet Finder

The whole sequence can be redone with and alternate jet finder. We used '!FastJet' and we replaced the Durham algorithm with KT. For this, we changed Durham_4Jets to Kt_4Jets and used

<processor name="MyFastJetProcessor4" type="FastJetProcessor">
       <parameter name="algorithm" type="StringVec"> kt_algorithm 1.0 </parameter>
       <parameter name="clusteringMode" type="StringVec"> ExclusiveNJets 4 </parameter>
       <parameter name="jetOut" type="string" lcioOutType="ReconstructedParticle"> Kt_4Jets </parameter>
       <parameter name="recParticleIn" type="string" lcioInType="ReconstructedParticle"> LooseSelectedPandoraPFANewPFOs </parameter>
       <parameter name="recombinationScheme" type="string">E_scheme </parameter>
       <parameter name="recParticleOut" type="string" lcioOutType="ReconstructedParticle"> LooseSelectedPandoraPFOsInKt_4Jets </parameter>
</processor>

Using 4-Jet background

This is done following the same procedure, but using qqll 1.4TeV, CLIC_ILD DST-s (2645) and the script subqqll.sh and its dependencies, zvresQQllPBS.sh, and train_qnets.xml. The relevant files are:

• qqll_scripts.tgz: Scripts for signal + qqll background, KT algorithm
• qnets.tgz: Neural nets for signal + qqll background, KT jet finder algorithm

The outputs are stored in the usual directory: /afs/phas.gla.ac.uk/data/ilc/datasets01/1.4tev/ZVRES_out/*qnets*. Purity vs. efficiency plot using qnets (1 vertex NN is gibberish):

Useful Links

• LCFIVertex Tutorial: http://www-pnp.physics.ox.ac.uk/~hillert/VP/LCFIVertex-v00-01/Tutorials.html
• Trained Neural Nets: https://svnsrv.desy.de/viewvc/tagnet/LCFI_MokkaBasedNets/