1 / 26

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

jrichmond
Download Presentation

Extracting the H  gg signal using ML-fitting techniques

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related