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

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-

This study 17th General 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, (2012)

Sensitivity vs energy T.Takahashi Hiroshima η=η ’ =1,η B =0

Beam parameters (based on TESLA optimistic) T.Takahashi Hiroshima x3.7x4.8 Ee[GeV] n(10 10 )22  z (mm) 0.35  x/y [m rad] 2.5/0.03  x/y 1.5/0.3  x/y [nm] 96/4.799/5.5 L [nm] x Pulse Energy[J] 10 Lgeo(e-e-) [10 34 cm -2 s -1 ] Lpeak(  ) [10 34 cm -2 s -1 ] Ltot(  )[10 34 cm -2 s -1 ]

Luminosity Distribution(CAIN) x=3.76

Signal backgrounds 7 270GeV  ->ZZ  ->WW 10 -4

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

Event generation and Detector simulation detector simulation MC events HH WW zz bbbb 9 dead !

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

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)

Analysis ~ b tagging~ impact parameter <- simple ! 12 Nsig: Number of displaced tracks in a jet L

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× Pre-selecttion pre-selection: cut tacks to forward/backward region loose b- tagging

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

Example parameters For WW 15 WW HH WW HH

Example of parameters for 4b 16 bbbb HH bbbb HH

example parameters for ZZ 17 ZZ HH ZZ HH

Cut statictics 17th General KEK18 HHWWZZbbbb Total807.3× Pre-selecttion WW filter ZZ filter bbbb filter 3.77± ±0.67±1 jet clustering

Ideal clustering 17th General 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

Cut statictics 17th General KEK20 HHWWZZbbbb Total807.3× Pre-selecttion WW filter ZZ filter bbbb filter34.7±0.25±25.2±0.66±1

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

Backup slide 22

Ideal clustering 17th General KEK23