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Model ID @ 500 GeV

Model ID @ 500 GeV. Taikan Suehara ICEPP, The Univ. of Tokyo. Three models. Inert Higgs WIMP: h I (scalar) Visible: h ± (scalar) SUSY WIMP: c 0 (fermion) Visible: c ± (fermion) LHT WIMP: A H (vector) Visible: W H ± (vector). All give 2W + 2WIMP fs. Analysis overview.

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Model ID @ 500 GeV

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  1. Model ID @ 500 GeV Taikan Suehara ICEPP, The Univ. of Tokyo

  2. Three models • InertHiggs • WIMP: hI (scalar) • Visible: h±(scalar) • SUSY • WIMP: c0 (fermion) • Visible: c± (fermion) • LHT • WIMP: AH (vector) • Visible: WH± (vector) All give 2W + 2WIMPfs.

  3. Analysis overview • √s = 500 GeV, 500 fb-1 • full simulation • NO Initial polarization • Use same massesfor all the models m(visible) = 231.57 GeV, m(WIMP)=44.03 GeV • Cross sectionis normalized (200fb & 40fb). • Use W hadronic decay(cut leptonicevents) • Mass-> production angle calculation • Full SM backgroundincluded Weighted to 500fb-1

  4. ILDdetector &framework Event generation(signal, SM background) Stdhep event files Full-MC simulation(GEANT4) • Gridによる大量のjob投入 Hit, MCinfo(LCIO file) • Tracking • Particle Flow • Flavor tagging etc. Event reconstruction ILDdetector(ILD00) • Vertex: Si pixel (3x2 layers) • Silicon Tracker: 4 layers • TPC(main tracker) • ECAL (0.5x0.5 cm tile, Si/W) • HCAL (3x3 cm tile, Sci/Fe) • Solenoid(3.5Tesla) • Muon detector Particle information (ROOT tree) Analysis • Event weighting • Separation cuts • Analysis (mass, angle…)

  5. Event generation • Signal: JSF/physsimStructure • HELAS for helicity amp. calculation • BASES for numerical integration • SPRING for event generation • Pythia for quark fragmentation • ISR/Beamstrahlung included • ~0.1 M events for each process generated • Background: SLAC (ILCstandard sample) • whizard/pythia for all SM processes • ~14 M events in total (80-200 fb-1 for 2/4/6 fermion events, 0.1-1 fb-1 for eg/gg events)

  6. Selection cuts BG suppression cuts “qqqq (non-b) + missing” • 4-jet clustering (Durham) • # Track>=20 • 160 < Evis < 400 GeV • each Ejet > 5GeV • |cosq|jet < 0.99 • unlike 3jet(yth,3 > 0.001) • each jet has >= 2tracks • no > 25 GeVleptons • |cosq|miss < 0.9 • sum |cosq| < 2.6 • sum b-quark prob. < 1 • kinematic fit converged • 65 < mW < 95 GeV

  7. Selection efficiency • IH eff: 59.8%, pur (200 fb): 78.6%, pur (40 fb): 42.3% • SUSY eff: 58.3%, pur (200 fb): 77.7%, pur (40 fb): 41.0% • LHT eff: 58.9%, pur (200 fb): 78.2%, pur (40 fb): 41.8% Acceptable selection performance

  8. W energy distribution (for mass) MC truth (signal only) Reco (kin-fitted) MC & reco have a good correlation.Difference of shapes (esp.WH) is notused for the discrimination in this analysis.

  9. Mass determination • Fitting: 3rd polynomial x Voigt function (10 params) • All 10 params free • Fix 8 params (except edge positions):obtain errors of the edge positions

  10. Mass resolution • Edge positions from the fit: (MC: 96.32/174.78) • Masses (MC: 231.57/44.03)

  11. Production angle • Production angle of the visiblenew particle can be calculatedfrom W directions with massesof new particles andback-to-back assumption. • The angle has spin info.but two-fold ambiguity (solution of 2nd equation) exists.

  12. Discriminant distribution • The distribution looks reasonable. • ~30% of the events cannot be used.(discriminant > 0 events selected)

  13. 1D distribution 200fb • MC & reco have reasonable correlation. • Three models seem to be discriminated.

  14. 2D distribution (2 solutions) IH SUSY SMBG LHT Chi-square can be derived using these distributions.

  15. Chi-square value • One of the [IH, SUSY, LHT] + SMBG-> ‘Template’ (not randomized) • One of the [IH, SUSY, LHT] +(SMBG with Poisson randomization)-> ‘Data’ (for other sample than template) • (One of the [IH, SUSY, LHT] + SMBG)with Poisson randomization-> ‘Data (for the same sample as template) • Normalization (needed or not?) • Obtain difference/sqrt(template) bin by bin • Square sum of them -> Chi-square value

  16. Chi-square result (200fb) Number of bin = 210 preliminary

  17. Chi-square result (40 fb) Number of bin = 210 preliminary

  18. To Do

  19. Mass error on angle - MCvsReco • chi-square value: 392 (w/o stat fluctuation)(cf. 210 bins) after normalization • ‘Coefficient’ can be derived?(need comparison with other models) IH, MCTruth IH, reco

  20. Difference of distribution on masses? • Plan to make 25 mass points(5 visible mass x 5 DM mass) • m(visible): 231.57± 0.1x (error in 200fb) ± 0.xx (error in 40fb) • m(DM): 44.03 ± … same • Possible in generator levelalmost impossible in full simulation-> use the ‘Coefficient’ (MC-reco ratio)?

  21. Threshold scan • Toy-MC?

  22. Summary (schedule?) • Distribution of production angle looks reasonable. • Three models can be separated well in 200fb – some separation in 40 fb • Mass error should be considered • Need some more work (several days?) • Threshold scan • Need some more work (a day?) • Write a paper (several days?) • A dedicated week means a calendar month??

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