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Extracting the H  gg signal using ML-fitting techniques

Extracting the H  gg signal using ML-fitting techniques. The Hfitter Team: Pierre-Antoine Delsart, Sandrine Laplace, Jean-François Marchand (LAPP), Andreas Höcker, Guillaume Unal (CERN), Nicolas Berger (LAPP). Overview. Analysis Strategy: 2 steps

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Extracting the H  gg signal using ML-fitting techniques

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  1. Extracting the Hgg signal using ML-fitting techniques The Hfitter Team: Pierre-Antoine Delsart, Sandrine Laplace, Jean-François Marchand (LAPP), Andreas Höcker, Guillaume Unal (CERN), Nicolas Berger (LAPP) Nicolas Berger, H->gamma gamma CSC Meeting

  2. Overview • Analysis Strategy: 2 steps • Produce small ntuples from AOD data in Athena. • Use Maximum-likelihood fitter to extract signal yield. • Samples (CSC note): • Signal: generated with Pythia, reweighted (mainly in Higgs pT) using ResBos – maybe MC@NLO • Irreducible (gg) background : Pythia, reweighted w/ResBos • Reducible (gj, jj) backgrounds : AlpGen • Most plots shown here will have only gj Bkg. Nicolas Berger, H->gamma gamma CSC Meeting

  3. Athena Framework: HiggsToGamGam • Ntuples produced using the HiggsToGamGam package (PhysicsAnalysis/HiggsPhys/HiggsToGamGam) in 12.0.5/6 • Based on EventView, dumps Higgs information in a AAN format. • Photons cuts based on isEM tune2: • e = 74.6% for isEM selection • e = 72.7% for pTg > 15 GeV => eTot = 54.3% • Calculates refitted primary vertices (code from I. Koletsou) , photon momentum correction for each vertex. • Choice of “best vertex” : use MC truth for now • Also impose ETg1 > 40 GeV, ETg2 > 20 GeV before fitting. Nicolas Berger, H->gamma gamma CSC Meeting

  4. Event Processing AOD collections PhotonCollection ElectronCollection Et>15 GeV Matching with Conversion VxContainer Et>15 GeV OptIsEM cut EVFinalState Label Photon ConvertedPhoton Sum Photon+ConvertedPhoton  AllPh_N>=2 Keep 2 most energetic amongst Photon + ConvertedPhoton HiggsCombo: Only 1 combo thus 1 view (EV0) ! EVFinalState Label EnergeticPhoton Nicolas Berger, H->gamma gamma CSC Meeting

  5. Conversions • Rates: • ~50% of photons convert • ~20% early convs. (45 < R < 350 mm) • ~35% of events have early conv • Reconstruction: • 50% of early convs reconstructed • For others, reconstruct "1-track" conversions • Require no hits in B-layer • high efficiency • Contribution • +14% events with 2-track convs • +7% with 1-track convs Nicolas Berger, H->gamma gamma CSC Meeting

  6. Fitter • Hfitter: see /groups/catsusy/Hfitter in cvs • Performs unbinned extended ML fits, arbitrary num. of samples, categories & fit variables (based on RooFit). • Likelihood: With: And: Nicolas Berger, H->gamma gamma CSC Meeting

  7. 2.5 Large Eta Apart Large Eta Close |h2| Crack 1.65 1.35 Wide Center 1 0.75 Large Eta Apart Center 0.75 1 1.35 1.65 |h1| 2.5 Better resolution for small |h| Categories • “Categories” used to split data into subsets • Separate events into disjoint subsets • Different categories can have • different values of PDF parameters. • or different PDFs altogether • Gives finer-grained description of data • Increase significance • Reduces biases from correlations • Here, as an example, categories based on photon h’s: Nicolas Berger, H->gamma gamma CSC Meeting

  8. Higgs Production at LHC ttbar threshold Associated Productions Nicolas Berger, H->gamma gamma CSC Meeting

  9. Categories (2) • Categories for photon h (previous slide) • Merge "Large Eta Close" and "Wide Center" since resolutions are similar • Exclude crack region for the rest of the study • Categories for production modes • Njets=0,1,2 • 2-jet: 2 jets with pt>20 GeV, <p1,p2> > 0.2 rad • 1-jet: pt > 20 GeV • Separate category for 2-jet/VBF • 2 jets with pt > 20 GeV • <p1,p2> > p/2 • Dh > 3.6 • No central jet veto • Categories for associated modes • WH, ZH • ttH ? • Categories for conversions • Nconv=0,1,2 – may separate 1-track/2-track in Nconv=1 Nicolas Berger, H->gamma gamma CSC Meeting

  10. Use the following categories: WBF associated W, Z, or ttbar the remainder L = 30 fb-1 agrees with Categories : a toy example gg only:4.3s gg+VBFtogether5.1s gg+VBF+W/Z/tt:5.8s Whole sample: 4.5s WBF only2.8s H+W/Z/tt only:2.9s Bertrand Brelier Nicolas Berger, H->gamma gamma CSC Meeting

  11. g q* In Higgs rest frame Higgs g Fit variables • Use • mgg • pT(Higgs) • |cos q*|, q*=Higgs decay angle • MHJ, invariant mass of Higgs cand. and highest-pt jet. Nicolas Berger, H->gamma gamma CSC Meeting

  12. Fit Variables: mHJ NJets=1 NJets=2 NJets=2, VBF Longer tails in signal Limited by MC statistics! Nicolas Berger, H->gamma gamma CSC Meeting

  13. Variables used Not for CSC Note Nicolas Berger, H->gamma gamma CSC Meeting

  14. Fitting Strategy • Fit PDF shapes for signal and Bkg on MC • See next pages for details • Samples used are "old" with very limited statistics for bkg – updated soon with new ntuples. • Fit data with Sig+Bkg or Bkg-only models • Fit for • Nsigtotal – fixing the fraction fsignal in each category • NBkg in each category • Also keep mH floating • What window ? => Need to be optimized as fcn of lumi • Also float some background shape parameters Nicolas Berger, H->gamma gamma CSC Meeting

  15. Fractions of Events in Categories • Can gain significance by isolating VBF • Need to make sure this is not spoilt by cross-feed from other categories. Photon h Signal Bkg NJets Nicolas Berger, H->gamma gamma CSC Meeting

  16. Components Fits : mgg • Use 12.0.4 without pileup, vertex-corrected mass. • Signal: use Crystal Ball Shape • Crystal Ball: • Background: use a simple exponential – widen the mgg range for stats Background e-xm Signal Nicolas Berger, H->gamma gamma CSC Meeting

  17. mgg : Parameter values • Split x and sm in categories: • Different x for Njet categories • Different sm for photon h categories • x floats in fit to data Nicolas Berger, H->gamma gamma CSC Meeting

  18. Comparison with diphox • Spectrum OK-fitted by an exponential in the relevant region • x values similar to what was found in gjet • Need to evaluate effect of changes in bkg shape on signal yield. x = (-2.42 ± 0.01) 10-2 All photons x = (-2.87 ± 0.02) 10-2 x = (-2.12 ± 0.02) 10-2 Nicolas Berger, H->gamma gamma CSC Meeting

  19. Higgs pT Signal • Fit to functions of the form • Signal: use a sum of 2 such components. • Background: just one component (for now…) • x, l float in fit to data Distributions from PYTHIA: needs to be updated on reweighted MC Background Nicolas Berger, H->gamma gamma CSC Meeting

  20. Signal: Comparison with ResBos • ResBos: hh->B+X generator, where B boson (or gg pair) • resums soft/virtual QCD corrections (LL) => better description than NLO at small pT : • only generates gg->H (?) so look at same process with Pythia. Nicolas Berger, H->gamma gamma CSC Meeting

  21. Higgs |cos q*| • Use: Background Signal Background a2, a4 float in fit to data Nicolas Berger, H->gamma gamma CSC Meeting

  22. Higgs-Jet mass (mHJ) • Fit to functions of the form Signal Background floats in fit to data Nicolas Berger, H->gamma gamma CSC Meeting

  23. Performance Studies • Generate pseudo-data from PDFs, using expected signal/Bkg yields • Perform fit in the same conditions as data (floating parameters, etc.) • Fit the same sample with different models • Removes sensitivity to statistical effects in comparison • To calculate significance • Fit with S+B, B-only hypotheses • Calculate Dlog L = log LS+B – log LB • Get s = (2 Dlog L) [asymptotic formula, 1 dof] • Repeat on O(100) toys, average s = expected significance • Need to decide between 1-sided and 2-sided intervals (i.e. do we allow NSig < 0 ?) Nicolas Berger, H->gamma gamma CSC Meeting

  24. Example: mgg-only vs. mgg Ä cos q* Ä pT NSignal mgg Signif. 1-var Peak = 4.37 ± 0.08 Peak = 207 ± 4 Peak = 119.97 ± 0.03 4-var Signif. NSignal mgg Peak = 200 ± 3 Peak = 119.95 ± 0.03 Peak = 6.10 ± 0.08 Nicolas Berger, H->gamma gamma CSC Meeting

  25. Results • Significant gains from both categories and additional variables. Nicolas Berger, H->gamma gamma CSC Meeting

  26. Outlook • Major update effort underway for CSC note • Realistic backgrounds • Use MC@NLO for signal ? • Add conversions categories • Add categories for assoc. prod. • Further improvements • Make better use of jet info. (MVA) • Optimize fit for early data-taking • Better MC for signal and irred. bkg. would allow more realistic modeling and improve performance. • Help is welcome! Nicolas Berger, H->gamma gamma CSC Meeting

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