Prospects of non-resonant di-Higgs searches and Higgs boson self-coupling measurement at the HE-LHC using machine learning techniques

Amit Adhikary; Rahool Kumar Barman; Biplob Bhattacherjee

doi:10.1007/JHEP12(2020)179

paper

Prospects of non-resonant di-Higgs searches and Higgs boson self-coupling measurement at the HE-LHC using machine learning techniques

arXiv:2006.11879 · doi:10.1007/JHEP12(2020)179

Abstract

The prospects of observing the non-resonant di-Higgs production in the Standard Model at the proposed high energy upgrade of the LHC, $v i z .$ the HE-LHC( $s = 27 TeV$ and $L = 15 a b^{- 1}$ ) is studied. Various di-Higgs final states are considered based on their cleanliness and signal yields. The search for the non-resonant double Higgs production at the HE-LHC is performed in the $b b ˉ γ γ$ , $b b ˉ τ^{+} τ^{-}$ , $b b ˉ W W^{*}$ , $W W^{*} γ γ$ , $b b ˉ Z Z^{*}$ and $b b ˉ μ^{+} μ^{-}$ channels. The signal-background discrimination is performed through multivariate analyses using the Boosted Decision Tree Decorrelated(BDTD) algorithm in theTMVA framework, the XGBoost toolkit and Deep Neural Network(DNN). The variation in the kinematics of Higgs pair production as a function of the self-coupling of the Higgs boson, $λ_{h}$ , is also studied. The ramifications of varying $λ_{h}$ on the $b b ˉ γ γ$ , $b b ˉ τ^{+} τ^{-}$ and $b b ˉ W W^{*}$ search analyses optimized for the SM hypothesis is also explored.

Version 2; Modified significance formula used; version accepted for publication in JHEP

Prospects of non-resonant di-Higgs searches and Higgs boson self-coupling measurement at the HE-LHC using machine learning techniques

Abstract

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