Update on Higgs self-couplingTomáš Laštovička

FZU AV CRWG6 Meeting 29/11/2011

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Outlook

– Quick reminder– Reconstruction of the Higgs mass– Secondary vertex assisted jet finding– A different approach to neural net training– Higgs presence in 595 sample – cross-check– Summary and next steps

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Analysis chain

FastJet kT algorithm, R = 0.7 in exclusive mode requesting 4 jets MarlinKinFit

– The combinations of 2+2 jets with highest probability of having the same invariant mass selected and re-fitted.

Analysis chain essentially follows the chain for h→bB analysis Neural net selection input variables added

– pTmax, pT

min, MarlinKinFit probability, ymin, event invariant mass

– which is nice, on the other hand, one can not use the momentum conservation due to neutrinos in the final state of hhnunu (and qqqqnunu).

σ

Δσ0.85

λ

Δλ

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SiD Samples with 60BX Overlay – Full Sim/Rec

2Q Sample ProdID Events Twiki Events used C-Section [fb]

H → bB 596 128210 127325 419.81*BR(H → bB)

H → cC 597 127380 128842 419.81*BR(H → cC)

e-e+ → qqνeνe 636 167624 1305.31

e-e+ → qqνee- 598 95430 91336 5254.86

e-e+ → qqe-e+ 600 80340 89712 3341.13

e-e+ → qq 599 99720 96176 3075.98

4Q Sample ProdID Events Twiki Events used C-Section [fb]

e-e+ → HHνeνe 527 19030 0.622

e-e+ → qqqqνeνe 595 201160 576574 97.65

i.e. qqqq, qqqqνee, qqqqee are not included, nor is the HHee channel (ZZ fusion)

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Higgs invariant mass reconstruction I

One of the most powerful quantities to select the signal events. The reconstruction was rather poor, done in the following way:

– Make 4 jets in 2 pairs → 3 combinations– MarlinKinFit constraint – both pairs have the same invariant mass, re-fit jets– Choose the combination with the highest probability (taken from MarlinKinFit).

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Higgs invariant mass reconstruction II

A different approach:– Use MarlinKinFit’s probability only to find the jet combination– Take measured jet four-vectors, not re-fitted ones– Plot the combination with higher mass (and the one with lower mass too)

Looks much more reasonable

In HHnunu events there is most often something missing…

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Higgs invariant mass reconstruction III

Adding max(M1inv,M2

inv) to neural net inputs indeed improves the signal separation – Although not as much as one would expect

• Cross section uncertainty: from 19-21% to around 18%– min(M1

inv,M2inv) does not appear very useful

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Secondary vertex assisted jet finding I

Red and green are particles from different Hs, blue squares are secondary vertices and black points are directions of Hs (no RPs and jets overlaid)

η

φ

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Secondary vertex assisted jet finding II

Use secondary vertex information in the following way1) Find jets, find secondary vertices (LCFI)

2) For each secondary vertex add a particle (neutral B-meson) to container of particles

3) Find jets again

4) Carefully remove added particles from containers and from jets

5) Do all the rest of the LCFI package

After implementing this idea, I run into a couple of issues– Some jets consisted of one added particle alone, i.e. no particles

– Higgs invariant mass reconstruction is not improved.

– Consequently, the results on self-coupling are actually worse.

– This approach would require further work.

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A different approach to neural net training

The usual way– take variables, feed them to one neural net, flag signal as 1, background as 0.

Another way– take the same variables– feed them to two neural nets– train 1st to separate 4-jet events from 2-jet events– train 2nd to separate signal events from 4-jet background– merge outputs from both neural nets in 3rd neural net with 2 inputs, 1 output

– This further improves the signal separation. from ~18 % to 16-16.5%

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Higgs presence in 595 sample (ee → qqqqνν)

595 sample (qqqqnunu) contains 120GeV Higgs– It was subtracted statistically so far = “subtract 527 from 595”– On the MC level there are no intermediate particles

• combine quarks with electron/positron parent particles• If there is a combination where both pairs have Minv ≈ 120GeV reject event

In statistical subtraction, the “signal sample”, present in the background, was effectively used to train the neural net to recognize signal as background– i.e. signal was assigned to signal and background 1:1– Despite tiny signal amount, one would expect improvement

• Nevertheless, when removing 2-Higgs events, the result is about the same as for the statistical subtraction method (on the level of fluctuations)

– i.e. the cross section uncertainty is around 16-16.5% → 14% on HHH

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Summary and next steps

The best numbers so far:– Cross section uncertainty: 16% – HHH stat. uncertainty: 13.5%

– Nsignal = 135 Nbkg = 327

• only 8% signal selection efficiency• no qqnunu, qqnue, qqee , qq• surprising contribution from h→bb (36)

– With all the 2-q backgrounds from h→bb analysis plus qqqqνν background.

– Other 4-q backgrounds not included/simulated

Stephane generated 130GeV, 140GeV signal samples. Lower CLIC energies: 1.4TeV

Side-comment:There should be no useful information hidden in this plot, since it was used as a NN input.

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BACKUP

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Control Plots I

Where is the problem?– Higgs invariant mass reconstruction is poor, so is the jet flavour tag

it’s the jets, they are poorly reconstructed

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Control Plots II

ymin and ymax should be the most useful quantities in order to separate 4Q events from 2Q events

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Control Plots III

Invariant mass plot after neural net selection– Working point = lowest statistical uncertainty Signal:

~200 of 1240 Background: ~1440 (2Q contribution is around 400)