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