Difference: JetFlavourTagging (1 vs. 30)

Jet Flavour Tagging Howto with LCFIVertex

Note: this wiki refers to the LCFIVertex package, that can be found now on GitHub here.

• 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):

Using 4-Jet background

This is done following the same procedure, but using qqll 1.4TeV, CLIC_ILD, DST-s (2091) and the script subl.sh and its dependencies.

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>

• KTscripts.tgz: Scripts and templates modified to use FastJet/KT algorithm

Training the neural nets with eeqq background added

We can train 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.

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

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/

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 or any number of vertices:

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/

// 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>

<--In fti-steer.xml this parameter is called "FlavourTagCollection", without the 's' -->

root -l MakePurityVsEfficiencyRootPlot.C

Jet Flavour Tagging Howto

Jet Finder and Truth Tagging

Flavour Tagging

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

<parameter name="TrueJetFlavourCollection" type="string">TrueJetFlavour_4Jets </parameter>

Purity and Efficiency Studies

To determine the optimal cut for our b-tagging, a purity vs. efficiency study was performed. One can use the MakePurityVsEfficiencyRootPlot.C macro provided by the LCFIVertex package. Note that LCFI must be compiled with ROOT if one wants .root output from PlotProcessor (instead of .txt). For this, add as usual

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

<parameter name="TrueJetFlavourCollection" type="string">TrueJetFlavour_4Jets </parameter>

The LCIOOutput processor creates new slcio files containing the new collections added by the above processors.

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.

• 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.

Jet Finder and Flavour Tagging

The LCFI processors have the following functions:

• PerEventIPFitter - finds the event Interaction Point

Neural Network Training

The slcio files created at the previous step contain the collections Durham_4Jets, FlavourTagInputs and TrueJetFlavour_4Jets, which we will use now to train our neural nets. We use the NeuralNetTrainer code included in the LCFI package. Separate nets were trained for 1, 2, or 3+ vertices to identify b-jets, c-jets, and c-jets with b background. Our steering file contains only:

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

Purity and Efficiency Studies

To determine the optimal cut for our b-tagging, a purity vs. efficiency study was performed.

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

Acceptance Studies

ZZ Fusion Analysis

Neural Network training

We use the flavour tagging package LCFIVertex. This package consists of a topological vertex finder ZVTOP, which reconstructs secondary interactions, and a multivariate classifier which combines several jet-related variables to tag bottom, charm, and light quark jets.

Our steering file will contain the following LCFI component processors and a neural net trainer:

  <processor name="IPRPCutProcessor"/>
  <processor name="MyPerEventIPFitterProcessor"/>
  <processor name="ZVRESRPCutProcessor"/>
  <processor name="MyZVTOP_ZVRES"/>
  <processor name="FTRPCutProcessor"/>
  <processor name="MyFlavourTagInputsProcessor"/>
  <processor name="MyNeuralNetTrainer" type="GlasgowNeuralNetTrainer"/>

These processors have the following functions:

• IPRPCut - selects Reconstructed Particles based on track parameters, number of hits etc.
• PerEventIPFitter - finds the event Impact Parameter
• ZVRESRP - vertex finder for reconstructed particles
• ZVTOP_ZVRES - topological vertex finder
• FTRPCut: - flavour tagging reconstructed particle cuts
• FlavourTagInputs - from vertices and tracks calculates discriminating variables for the neural net
• NeuralNetTrainer - trains the networks

The neural nets are saved as XML files in nnets/ and will be used for flavour tagging (next step). No slcio output is written at this time.

Flavour Tagging

• TrueJetFlavour - determines MC flavour of reconstructed jets

A new file containing with these collections added is saved to be used in our analysis.

Acceptance Studies

Four Jet Events

Purity and Efficiency Studies

Electrons from Hard Bremsstrahlung

Luminosity and Event Weights

-- DanProtopopescu - 2013-06-28