BJamboree tagger

1
B-jet tagging with muons

• Efficiency of jet and muon reconstruction (bb???
  X)
• Discriminating variables
• Current performance
• Short look into neural networks
• Outlook

2
Jet reconstruction efficiency

• 0.7 cone algorithm
• Recod jet compared with mc jet within 0.2 cone
• 75 efficient

3
Muon reco efficiency
4
Muon reco efficiency (ctd)

• Total muon reconstruction efficiency 70
• Combination of Gtrack efficiency and muon
  efficiency
• Efficiency for jet? reconstruction 52

5
Discriminating variables

• Variables under consideration
• PTRel
• P? / Ejet
• DCA, DCA-significance
• DZ, DZ-significance
• Current implementation only uses PTRel

6
PTRel
7
P ? / Ejet
8
DCA DZ
9
DCA DZ significance
10
Current tagger performance
bb?? ? X, 1200 evts, 1843 b-jets, 1131 l-jets
11
Current tagger performance
bb?? X, 1500 evts, 2373 b-jets, 1446 l-jets
12
Tagger Performance
No requirements on ?
13
Neural net performance

• Use a 9-input, 18 hidden, 1 output NN
• Inputs are discriminating variables
• Trained NN on 950 bbar events
• Tested NN on 250 bbar events (not the same!)
• Caveat backgrounds (light jets) come from the
  same bbar file

14
Neural net output

• Solid blue background
• White open signal

15
Neural net performance
bb?? ? X, 200 evts, 320 b-jets, 77 l-jets
Grey Values from currently implemented tagger
16
Neural net performance
bb?? X, 1500 evts, 2373 b-jets, 1446 l-jets
Grey Values from currently implemented tagger
17
Outlook

• Discriminating variables are shaping up
• Neural network is looking promising
• Need more MC, especially background
• cc
• QCD di-jet