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

TL;DR

This paper assesses the potential to detect non-resonant di-Higgs production and measure Higgs self-coupling at the HE-LHC using advanced machine learning techniques across multiple decay channels.

Contribution

It introduces a comprehensive analysis of di-Higgs searches at the HE-LHC employing machine learning methods like BDTD, XGBoost, and DNN to improve signal-background discrimination.

Findings

Enhanced sensitivity in multiple di-Higgs channels.

Impact of Higgs self-coupling variations on search strategies.

Use of advanced ML techniques for Higgs physics analysis.

Abstract

The prospects of observing the non-resonant di-Higgs production in the Standard Model at the proposed high energy upgrade of the LHC, $viz.$ the HE-LHC$~$($\sqrt{s}=27~{\rm TeV}$ and $\mathcal{L} = 15~{\rm ab^{-1}}$) 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 $b\bar{b}\gamma\gamma$, $b\bar{b}\tau^{+}\tau^{-}$, $b\bar{b}WW^{*}$, $WW^{*}\gamma\gamma$, $b\bar{b}ZZ^{*}$ and $b\bar{b}\mu^{+}\mu^{-}$ channels. The signal-background discrimination is performed through multivariate analyses using the Boosted Decision Tree Decorrelated$~$(BDTD) algorithm in the$~$TMVA 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, $\lambda_{h}$, is also studied. The ramifications of varying $\lambda_{h}$ on the $b\bar{b}\gamma\gamma$, $b\bar{b}\tau^{+}\tau^{-}$ and $b\bar{b}WW^{*}$ search analyses optimized for the SM hypothesis is also explored.