Difference: JetFlavourTagging (3 vs. 4)

Revision 42013-07-01 - DanProtopopescu

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ZZ Fusion Analysis

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This is a detailed record on how the Marlin framework and adjacent packages are used for our analysis of the feasibility of the ZZ fusion channel with CLIC ILD at 1.4 TeV.
 
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BRTotalUncertBands.png
 
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Neural Network training

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Jet Finder and Flavour Tagging

 
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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.
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We use the LCFI flavour tagging package. 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 (see diagram).
 
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Our steering file will contain the following LCFI component processors and a neural net trainer:
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Our steering file will contain the jet finder, flavour tagging and LCFI processors, and we will write new slcio files containing the added collections:
 
  <group name="JetFinders"/>
  <group name="MyTrueAngularJetFlavourProcessorCollection"/>
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These processors have the following functions:
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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).

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 Impact Parameter
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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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  • NeuralNetTrainer - trains the networks
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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

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"/>
  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.
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Purity and Efficiency Studies

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

 

Flavour Tagging

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  • TrueJetFlavour - determines MC flavour of reconstructed jets
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Now are ready to employ the FlavourTag processor, which will do flavour tagging using the neural nets trained in the previous step. Our steering file contains the following processors:
 
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A new file containing with these collections added is saved to be used in our analysis.
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  <processor name="MyFlavourTag"/>
  <processor name="ZZfusion"/>
 
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Acceptance Studies

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The ZZfusion processor is used in our analysis.
 
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Four Jet Events

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Acceptance Studies

 
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Purity and Efficiency Studies

 

Electrons from Hard Bremsstrahlung

Luminosity and Event Weights

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-- DanProtopopescu - 2013-06-28
 \ No newline at end of file
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META FILEATTACHMENT attachment="jet_truth_tag-steer.xml" attr="" comment="Steering file 1: jet finder and truth flavour tagging" date="1372699245" name="jet_truth_tag-steer.xml" path="jet_truth_tag-steer.xml" size="84107" stream="jet_truth_tag-steer.xml" tmpFilename="/usr/tmp/CGItemp32410" user="DanProtopopescu" version="1"
 
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