Log plots, Fakes and DY

Richard St. Denis University of Glasgow/CDF 19 November 2007

H → WW Meeting

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Outline

▶ Summary of Limits and Variations ▶ Request to see LRHWW on log ▶ Examination of Fake/DY in MetSig, Mll and NN distn.

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Trainings: Conditions

In each group ▶ Remove Redundant SumJetEt (2 layer) ▶ Remove Redundant SumJetEt (1 layer) ▶ Remove SumEtJet and LRHWW (2 layer) ▶ Remove SumEtJet and all LR (2 layer) ▶ Remove SumEtJet and MetSig (2 layer) ▶ Remove SumEtJet, MetSig and LRWW (2 layer) ▶ Remove SumEtJet, MetSig and All LR (2 layer)

H → WW Meeting

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Trainings

Richard St. Denis, Glasgow University/CDF, 19 November 3

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Trainings: Observations

▶ All weighted trainings show deviation in Background training and test NN distribution.

▶ MetSig has a huge impact on observed with little impact on expected

▶ Exeception is Pythia: WW and unweighted, all variables (SumJetEt removed) shows same result as when MetSig is removed, although removing LR(HWW) creates MetSig sensitivity

▶ Pythia shows MetSig Sensitivity to some degree but with varied interaction in LR(HWW).

A higher order modelling problem does seem to be an ingredient. Turn to LRHWW by jet.

H → WW Meeting

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LRHWW:No NN Cut

Richard St. Denis, Glasgow University/CDF, 19 November 5

H → WW Meeting

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LRHWW: No NN cut

Richard St. Denis, Glasgow University/CDF, 19 November 6

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LRHWW: 0 Jets

Richard St. Denis, Glasgow University/CDF, 19 November 7

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LRHWW: 0 jets

Richard St. Denis, Glasgow University/CDF, 19 November 8

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LRHWW:1 Jet

Richard St. Denis, Glasgow University/CDF, 19 November 9

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LRHWW: 1 jet

Richard St. Denis, Glasgow University/CDF, 19 November 10

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High S/B 0 Jet

Discriminant  KS Prob  χ2/DOF  χ2 Prob  Comment
LRHWW  0.05  62/50  0.11
NN<0.63  0.02  49/48  0.43
NN>0.63  0.41  56/50  0.26

Low S/B 0 Jet

Discriminant  KS Prob  χ2/DOF  χ2 Prob  Comment
LRHWW  0.04  65/50  0.08  χ2 grow after 0.5
NN<0.5  0.18  57/50  0.23
NN>0.5  0.39  73/50  0.02

High S/B 1 Jet

Discriminant  KS Prob  χ2/DOF  χ2 Prob  Comment
LRHWW  0.12  44/50  0.73
NN<0.77  0.43  39/50  0.88
NN>0.77  0.01  56/50  0.26

Low S/B 1 Jet

Discriminant  KS Prob  χ2/DOF  χ2 Prob  Comment
LRHWW  0.04  59/50  0.18
NN<0.63  0.02  54/50  0.33
NN>0.63  0.14  49/50  0.54

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LRHWW 1jet: Observations

▶ No NN score cut • Low S/B, 0 jet, lowest bin ok, data higher in others.

• Low S/B, 1 jet, ×2 discrepancy in lowest bin, overall data above MC. Fake and Wγ dominate.

• 1 jet dominated by DY, low s/b shows fakes and Wγ in

higher scores. ▶ 0 Jet, NN cuts

• High NN Score show data above prediction: more so in Low S/B than High S/B. Background Dominantly WW.

• Low NN Score in reasonable agreement. ▶ 1 Jet, NN cuts

• High NNScore: more events than expected in both

high and low s/b

Richard St. Denis, Glasgow University/CDF, 19 November 12

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• Low NN score: first bin ok in High S/B, not easy to say in Low S/B; other bins high for low S/B, possibly ok for Low S/B.

H → WW Meeting

[figure: MetSig]

Richard St. Denis, Glasgow University/CDF, 19 November 14

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Observations Concerning MetSig

▶ 0 jet data slightly above prediction in High S/B ▶ 0 jet data quite above prediction in Low S/B ▶ Some Fake/Wγ in High S/B, significantly more in Low S/B

and shape consistent with the disagreement: a scaling of Fake/Wγ would help with agreement

▶ High S/B 1 jet has better agreement. The DY contribution good where dominant, but prediction for 3 points around MetSig of 4 is not very good. Relatively little Fake/Wγ.

▶ Low S/B 1 jet has worse agreement and again more Fake/Wγ expected.

H → WW Meeting

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Dilepton Mass

Richard St. Denis, Glasgow University/CDF, 19 November 16

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Observations Concerning Dilepton Mass

▶ 0 jet data slightly above prediction in High S/B in the region where more Fake/Wγ is expected.

▶ 0 jet data quite above prediction in Low S/B in the region where more Fake/Wγ is expected.

▶ Some Fake/Wγ in High S/B, significantly more in Low S/B and shape consistent with the disagreement: a scaling of Fake/Wγ would help with agreement

▶ High S/B 1 jet has better agreement execpt at Z resonanace. Relatively little Fake/Wγ.

▶ Low S/B 1 jet has worse agreement where low Fake/Wγ expected but also there are more events at high mass than expected.

Deficiency in the Fake/Wγ, look at NN distributions.

Richard St. Denis, Glasgow University/CDF, 19 November 17

H → WW Meeting

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NN Score Distn, High S/B

Richard St. Denis, Glasgow University/CDF, 19 November 18

H → WW Meeting

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NN Score Distn, Low S/B

Richard St. Denis, Glasgow University/CDF, 19 November 19

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Observations Concerning NN Score

▶ No Wγ but there are fakes in High S/B ▶ Lots Wγ and Fakes in Low S/B, and points all higher than

prediction.

H → WW Meeting

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Neurobayes and TMVA: All S/B

Richard St. Denis, Glasgow University/CDF, 19 November 21

H → WW Meeting

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Neurobayes and TMVA: High S/B

Richard St. Denis, Glasgow University/CDF, 19 November 22

H → WW Meeting

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Neurobayes and TMVA: Low S/B

Richard St. Denis, Glasgow University/CDF, 19 November 23

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Comment on Fakes

▶ We select fake denominators in dilepton events ▶ Most are “dirty leptons” and associated with a good

lepton, coming in the dominant channel for dileptons:

Drell Yan ▶ The DY NN removes these nicely and we see that the

remaining W+jet and fake is very small and in rough agreement. We took a large systematic.

▶ The MetSig selection and removal of fakes was not

scritinized in the same manner. ▶ This is difficult without a W+jet MC: We request this sample. ▶ The DY recognition of fakes may not be as powerful as

doing this in two steps.

Richard St. Denis, Glasgow University/CDF, 19 November 25

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▶ We will evaluate the NN scores in a DY/HWW NN and see how MetSig matches this.

▶ Why does removing MetSig help? Why does Pythia have different sensitivity?

H → WW Meeting

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NN Score Distn, High S/B, MetSig removed

Richard St. Denis, Glasgow University/CDF, 19 November 27

H → WW Meeting

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NN Score Distn, Low S/B, MetSig removed

Richard St. Denis, Glasgow University/CDF, 19 November 28

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Comment on MetSig Removal

▶ Change in High S/B shape and clearly less events at high

NN score. ▶ Closer match to Low S/B and less Low S/B in tail. More

signal.

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Summary

▶ Significant impact of MetSig. ▶ Interplay between MetSig and LR(HWW). ▶ Pythia and MC@NLO differences point to the 1 jet

modelling. ▶ 1 jet modelling for overall LRHWW and low S/B is not good ▶ LRHWW for High NN score shows data above prediction in

all bins. This should be examined for other NNs. 1 jet stats low, but more than expected.

▶ Turning to MetSig: Fakes seem underestimated and the

impact in the two neural nets could be different. ▶ Looking at Fake estimates and differences in High and

Low S/B fitting.

Richard St. Denis, Glasgow University/CDF, 19 November 30

H → WW Meeting

[figure: Backup]

Richard St. Denis, Glasgow University/CDF, 19 November 31

H → WW Meeting

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Dilepton Mass:Ratio, Hi S/B

Richard St. Denis, Glasgow University/CDF, 19 November 32