Difference: JetFlavourTagging (4 vs. 5)

Revision 52013-07-03 - DanProtopopescu

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META TOPICPARENT name="WebHome"

ZZ Fusion Analysis

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The JetFinder processor reconstructs 4 jets events from the input collection (LooseSelectedPandoraPFANewPFOs was used). For the reconstructed 4 jets, TrueAngularJetFlavourProcessor does 'truth tagging', i.e. determines the MC jet flavour (b-jet or c-jet).
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The JetFinder processor reconstructs 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 LCFI processors have the following functions:

  • IPRPCut - selects Reconstructed Particles based on track parameters, number of hits etc.
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  • PerEventIPFitter - finds the event Interaction Point
  • 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
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  • 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 PT)
  • 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):

Processor Type
<-- -->
Sorted ascending
Input Collection name Output Collection name
MyFlavourTagInputs FlavourTagInputs ZVRESDecayChains, FTSelectedJets FlavourTagInputs
MyPerEventIPFitter PerEventIPFitter IPFitSelectedParticles IPVertex
IPRPCut RPCut LooseSelectedPandoraPFANewPFOs IPFitSelectedParticles
ZVRESRPCut RPCut RecoMCTruthLink, Durham_4Jets ZVRESSelectedJets
FTRPCut RPCut RecoMCTruthLink, ZVRESDecayChains FTSelectedJets
JetFinder SatoruJetFinder LooseSelectedPandoraPFANewPFOs Durham_4Jets
MyTrueAngularJetFlavour TrueAngularJetFlavour MCParticlesSkimmed, Durham_4Jets TrueJetFlavour_4Jets
MyZVTOP_ZVRES ZVTOP_ZVRES IPVertex, ZVRESSelectedJets ZVRESDecayChains, ZVRESDecayChainRPTracks, ZVRESSelectedJets

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

  The LCIOOutput processor creates new slcio files containing the new collections added by the above processors. We found that the most time-consuming processor is ZVTOP_ZVRES with more than 10 s/event.

Neural Network Training

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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:
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The slcio files created at the previous step contain the collections FTSelectedJets, 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"/>
 
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