Jet Tagging studies for VBF Higgs -> WW -> 2l2nu Neural Network for JetTagging

Kind of NN

1. NN on single jet
2. NN on a pair of jets with some information from the remaining jets
3. NN on all pair of jet (the first 4 pt ordered pairs of jets)

Analysis steps

• Creation of a TTree to store everything needed to study Jet tagging efficiencies and performance.
  • Tree producer
  • config file
  • cmsRun file python

Single Jet NN

1. Creation of a TTree to create input variables for NN
   • Tree producer
   • example:

      JetTagging_NN_SingleTag_Creator 0.4 input.root output.root 

     where 0.4 is the max ΔR for MC matching with MC tagged quarks
2. Training of the NN TMultiLayerPerceptron (H160WW2l sample)
   • root macro
   • 24k events training (5.5k signal + 18.5k background) + 6k events test
   • output of the training is a set of weights. Otherwise the functions.cxx and functions.h
3. Test of the NN TMultiLayerPerceptron
   • root macro
4. PostTraining of the NN TMultiLayerPerceptron
   • root macro
5. Test of the NN in CMSSW (just a function) -> jet pair with maximum NN output
   • bin code -> comparison between Pt max, Mjj max e NN max methods

Results

• NN efficiencies graphs: cut selection on NN output, NN output and Pt of jets, signal and background efficiencies
• Purity: given a VBF event, number of events with two right jets tagged / total number of VBF events
  • images produced by

     JetTagging_MyNN 0.4 input.root output.root

    and then analizyng the tree

    tTag

    with

    drawMjjPt_purity_Tree.cc

H160

H170

H200

H500

• Purity: given a VBF event, number of events with two right jets tagged / total number of VBF events. L2+L3 corrected jets (NN not yet trained, but old trained one reused)

H160

H170

H200

H500

* Purity: given a VBF event, number of events with two right jets tagged / total number of VBF events. L2+L3 corrected jets (NN re-trained )

H160

H170

H200

H500

Multi Pair Jet NN

Single Pair Jet NN

1. Creation of a TTree to create input variables for NN
   • Tree producer
   • example:

      JetTagging_NN_SinglePairTag_Creator 0.4 input.root output.root 

     where 0.4 is the max ΔR for MC matching with MC tagged quarks
2. Training of the NN TMultiLayerPerceptron (H160WW2l sample)
   • root macro
   • 24k events training + 6k events test
   • output of the training is a set of weights.
3. Test of the NN TMultiLayerPerceptron
   • root macro
4. Test of the NN in CMSSW -> jet pair with maximum NN output
   • bin code -> comparison between Pt max, Mjj max e NN max methods

Results

• NN efficiencies graphs: cut selection on NN output, NN output and Pt of the higher pt jet, signal and background efficiencies
• Purity: given a VBF event, number of events with two right jets tagged / total number of VBF events
  • images produced by

     JetTagging_MyNN_SinglePair 0.4 input.root output.root

    and then analizyng the tree

    tTag

    with

    drawMjjPt_purity_Tree.cc

  • No energy correction samples

H160

H170

H200

H500

• • L2+L3 energy correction samples

Sample	Image
H130_tautau_2l/Summer08_IDEAL_V9_v1/GEN-SIM-RECO	L2+L3

-- AndreaMassironi - 04 Mar 2009