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STATUS ON MVA - BTAGGING OPTIONS
UPDATE 16/11/2016
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INPUTS AND CONFIGURATION
▸ MC15c CxAOD_00-24-07
▸ Normalised to 13.18 fb-1
▸ No PileUp reweighting, no fit SF
▸ MVA selection
▸ bTagging strategy : AllSignalJets
▸ Evaluate significances on rebinned histograms (transformation D, 10, 5)
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TESTED CONFIGURATIONS
▸ ICHEP variables | bTag : 70%, 77, 85%
▸ Training : truthtagging
▸ Evaluation : truthtagging or direct tagging
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MAIN YIELDS - DIRECT TAGGING 70%
Sample 2j 3jVH 17,3 20,3ZZ 33,6 33,1WZ 9,9 17,6
Zbb 140-280 393,7 647,7Wbb 99,7 249,5
singletop_Wt 13,7 75,1singletop_t 21,3 112,9singletop_s 11,6 16,5
tt 160,8 1227,1Background 890,0 2669,9
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RESULTS▸ ICHEP variables, TruthTagging for training
▸ 77% slightly better (~2.5% w.r.t. 70%) : was already the case in early studies (but only 0.7%), and this study was concerned with the kFold bug
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Evaluation DirectTagging TruthTaggingWP 70% 77% 85% 70% 77% 85%2j 1,61 1,66 1,54 1,53 1,58 1,453j 1,19 1,20 1,05 1,10 1,13 1,03
Combined 2,00 2,05 1,86 1,89 1,94 1,78
SHERPA 2.2.1 V+JET SAMPLES
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INPUTS AND CONFIGURATION
▸ MC15c CxAOD_00-24-07
▸ Normalised to 13.18 fb-1
▸ No PileUp reweighting, no fit SF
▸ MVA selection
▸ bTagging strategy : AllSignalJets
▸ Evaluate significances on rebinned histograms (trafo D, 10, 5)
▸ Replacing V+jets Sherpa 2.2 samples with v2.2.1
▸ No Ztautau samples in version 2.2.1 : keep 2.2 for this sample
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COMPARISON OF INCLUSIVE YIELDS
All backgrounds Zbb (Znunu samples)
2 jets 3 jets 2 jets 3 jets
Sherpa 2.2 992.195 2819.52 524 848
Sherpa 2.2.1 1011.57 2836.53 535 848
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RESULTS▸ ICHEP variables - 70% - truth tagging for training and evaluation
Sherpa 2.2 Sherpa 2.2.1
2j 1,53 1,52
3j 1,10 1,09
Combined 1,89 1,87
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VARIABLES RANKING UPDATE 07 / 11 /16
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CONFIGURATION
▸ MC15c CxAOD_00-24-07
▸ Normalised to 13.12 fb-1
▸ No PileUp reweighting, no fit SF
▸ MVA selection
▸ bTagging strategy : AllSignalJets at 70% bJet efficiency WP
▸ Truthtagging for training and evaluation
▸ Evaluate significances on rebinned histograms (trafoD, 10,5)
▸ Independent ranking for 2j and 3j categories
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A FEW ADDITIONAL VARIABLES
▸ absyBB = |y(bJet1,bJet2)|
▸ cosbstar = cos(β*)
▸ pTBA = (pTB1-pTB2) / (pTB1 + pTB2)
▸ pTH = pT(bJet1+bJet2)
▸ pTHMETA = (pTH-MET)/(pTH+MET)
▸ METprime = 2*mBB/dRBB
▸ dRB1J3, dRB2J3, mindRBJ3, maxdRBJ3 = angular separation bJet / 3rd jet
▸ |etaJ3|
▸ Starting training from mBB + 1variable, to see if dRBB and MET have the same impact in discrimination
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RANKING 2J
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2J TRAINING VARIABLES (ORDERED FROM BEST TO WORSE)
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2J TRAINING VARIABLES (ORDERED FROM BEST TO WORSE)
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2J TRAINING VARIABLES (ORDERED FROM BEST TO WORSE)
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RANKING 3J17
order of “1rst variables” : dRBBCorr, mBBJCorr, MET, dRB1J3, pTB2Corr, maxdRBJ3, HT, pTB1, pTBA, etaJ3, pTHMETA, dPhiMETdijet, pTJ3, mindRBJ3, dEtaBB, dRB2J3, abscostheta, pTH, absyBB, METprime
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3J TRAINING VARIABLES (ORDERED FROM BEST TO WORSE)
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3J TRAINING VARIABLES (ORDERED FROM BEST TO WORSE)
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3J TRAINING VARIABLES (ORDERED FROM BEST TO WORSE)
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3J TRAINING VARIABLES (ORDERED FROM BEST TO WORSE)
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PLANS
▸ Look at differences in significance between kFolds
▸ Look at the evolution of correlations between spectator variables and BDT output
▸ Example : in the 2j category, is BDT{mBB, dRBB} much less correlated to pTB2 than BDT{mBB, dRBB, pTB2}, …
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MET VS METPRIME
▸ Take the events used for training/testing
▸ look at the correlation with weighted events
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MET VS METPRIME : SIGNAL 2 JETS
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MET VS METPRIME : BACKGROUND 2 JETS
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MET VS METPRIME : SIGNAL 3 JETS
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MET VS METPRIME : BACKGROUND 3JETS