Study of Higgsinvisible using kinematic fit Yu Kato The Univ. of - PowerPoint PPT Presentation

Study of Higgs→invisible using kinematic fit Yu Kato The Univ. of Tokyo Asian Physics and Software Meeting Dec. 15, 2017

Improve of recoil mass dist. efficiency: 65.9% significance: 22.3 efficiency: 61.8% significance: 17.0 New Result w/ kinematic fit Previous Result (LCWS2016) bkg signal significance: 19.7 2017/12/15 Right bkg signal efficiency: 63.5% significance: 15.5 Left 2 Study of Higgs->invisible using kinematic fit efficiency: 65.8%

Upper Limit of BR(H→inv.) w/ kinfit 2017/12/15 Study of Higgs->invisible using kinematic fit 3 250 fb -1 of each Pol. UL of BR(H→inv.) = 0.824% (95%CL) UL of BR(H→inv.) = 0.483% (95%CL) UL of BR(H→inv.) = 0.215% (95%CL) UL of BR(H→inv.) = 0.423% (95%CL) 250 GeV Stage : 900 fb -1 of each Pol.

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Upper Limit of BR(H→inv.) w/ kinfit 2017/12/15 Study of Higgs->invisible using kinematic fit 7

q Z q Z ν ν ν X X ν q q 8 Study of Higgs->invisible using kinematic fit 2017/12/15 Motivation it signifies new physics beyond SM (BSM) l In SM, Higgs decays invisibly through LHC CMS result (95% CL) H → ZZ ∗ → 4& (BR(H → *+,.)~0.1%) observed (expected) l If BR(H → *+,.) exceeds SM prediction , 24% (23%) l We estimate SM upper limit of BR(H → *+,.) Dark Matter… SUSY… l Compare between left & right polarization at the ILC invisible BSM invisible 45 H → ZZ ∗ → 4& ~0.1% 45 H → XX ~? ? ? % visible visible Ø A. Ishikawa (Tohoku Univ.), ”Search for Invisible Higgs Decays at the ILC” LCWS2014@Belgrade Ø M. A. Thomson (Univ. of Cambridge), arXiv: 1509.02853 (2015)

9 fitter engine : NewtonFitter ( use Newton-Raphson iterative method ) Study of Higgs->invisible using kinematic fit 2017/12/15 kinematic fit (make my own kinematic fitter based on Kurata-san’s kinematic fitter) � 3. consider ISR effect ( e.g. include aa_lowpt overlay) 2. apply BW mass constraint & Z natural width 1. samples : ffH_ZZ_4n { ILD_l4_v02, ILD_s4_v02, (ILD_o1_v05) } p We want to improve recoil mass resolution of Higgs->inv. analysis. 8 9 8 : → ;<, ; → >>, < → *+,*?*@A8 − J K F − L F ⃗ K F B CDE = ? p Apply kinematic fit minimize χ 2 under constraints => method of Lagrange multipliers N F = O V W 9X O ⃗ V \ ] ⃗ ⃗ PQR − O ⃗ SDTU ⃗ PQR − O ⃗ SDTU + 2[ • measured parameters O ⃗ : J ^X , _ `X , a ^X , J ^F , _ `F , a ^F ⃗ : b ^X,^F = b K = 91.2 GeV , g ^X = |L ^X |/J • constraint function ] ^X , g ^F = |L ^F |/J ^F • covariance matrix W : j k (J, lm?_), j n (J, lm?_), j o (J, lm?_) p Use MarlinKinfit p Status • include jet resolution for each event ( from Z->uds 2jet sample data ) • recoil mass sigma becomes smaller a little p Plan

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