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Higgs pair production in a Photon Collider Tohru Takahashi Hiroshima University for S.Kawada, N.Maeda, K.Ikematsu, K.Fujii,Y.Kurihara,,, 1 LCWS12 Arlington, TX
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Higgs selfcoupling A Higgs like particle found at LHC! Higgs?, SM Higgs or …. – coupling to fermions, gauge bosons mass generation – selfcoupling symmetry breaking T.Takahashi Hiroshima How can the PLC do ? even tough with the e+e-
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This study 17th General Meeting @ KEK3 Self-coupling constant in the SM Parameter of deviation from the SM Final goal: Study of Higgs self-coupling See feasibility of the measurement of Higgs pair creation in PLC. how may events expected? possible to suppress background? S.Kawada.. et.al, Phys. Rev. D 85, 113009 (2012)
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Sensitivity vs energy T.Takahashi Hiroshima η=η ’ =1,η B =0
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Beam parameters (based on TESLA optimistic) T.Takahashi Hiroshima x3.7x4.8 Ee[GeV]210195 n(10 10 )22 z (mm) 0.35 x/y [m rad] 2.5/0.03 x/y [mm]@IP 1.5/0.3 x/y [nm] 96/4.799/5.5 L [nm] 1054770 x 3.764.8 Pulse Energy[J] 10 Lgeo(e-e-) [10 34 cm -2 s -1 ] 8.78.1 Lpeak( ) [10 34 cm -2 s -1 ] 1.20.7 Ltot( )[10 34 cm -2 s -1 ] 12.65.88
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Luminosity Distribution(CAIN) x=3.76
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10 1 10 -1 10 -2 10 -3 100 Signal backgrounds 7 270GeV ->ZZ ->WW 10 -4
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Signal & Backgrounds ->WW 1.462*10 7 events/year ) ->ZZ 1.187*10 4 events/year Signal ->HH( -> bbbb) 16.4events/year Backgrounds ->bbbb 1.187*10 4 events/year Assumption for the study integrated luminosity of 5 years of PLC run 10 6
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Event generation and Detector simulation detector simulation MC events HH WW zz bbbb 9 dead !
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Analysis Signal – ->HH->bbbb Kinematics and flavor information – Jet clustering forced 4 Jets – Jet pairing – b tagging Event selection – pre-selection to reduced number of events – optimization with Neural Net 10
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jet paring 11 The jet of the least χ 2 was chosen to be the most probable combination. M 1, M 2 : reconstructed mass M H : Higgs mass are defined the same way (M bb =10GeV)
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Analysis ~ b tagging~ impact parameter <- simple ! 12 Nsig: Number of displaced tracks in a jet L
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Selection (1) 13 # Jet w/ more 0 off-vertex(>3s) tracks > 3 # Jet w/ more than 1 off-vertex(>3s) tracks > 2 b-tagging β : Lorentz factor of a particle θ : Angle between a particle and the beam HHWWZZbbbb Total807.3×10 7 59400260000 Pre-selecttion47.7813005127080000 pre-selection: cut tacks to forward/backward region loose b- tagging
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Selection (2) --- Neural Network (NN) parameters : – χ H 2, χ Z 2, χ bb 2 – transverse (longitudinal) momentum, – # of jets with displaced vertex jets, – visible energy, – Y cut value of jet clustering, – # of tracks Maximize statistical significance 14 N: # of events occurring in 5 years η: selection efficiency Sig: signal BG: background
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Example parameters For WW 15 WW HH WW HH
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Example of parameters for 4b 16 bbbb HH bbbb HH
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example parameters for ZZ 17 ZZ HH ZZ HH
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Cut statictics 17th General Meeting @ KEK18 HHWWZZbbbb Total807.3×10 7 59400260000 Pre-selecttion47.7813005127080000 WW filter12.324234380 ZZ filter5.9025913 bbbb filter 3.77±0.080+1.85.4±0.67±1 jet clustering
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Ideal clustering 17th General Meeting @ KEK19 ideal clustering using color singlet information for HH, WW,ZZ not for bbbb as color singlet is not trivial HH ZZ H or z b b
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Cut statictics 17th General Meeting @ KEK20 HHWWZZbbbb Total807.3×10 7 59400260000 Pre-selecttion47.755800417077800 WW filter408461826 ZZ filter368198 bbbb filter34.7±0.25±25.2±0.66±1
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Summary We tried to see -> HH in a photon collider based on TESLA optimistic parameters. CM energy of 270GeV is optimum for mh =120GeV It is possible to suppress backgrounds with improved jet clustering technique. – statistical significance of 4.9 with integrated luminosity corresonds to 5 years of PLC run 21
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Backup slide 22
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Ideal clustering 17th General Meeting @ KEK23
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