Charged Higgs Phenomenology in di-bjet channel with $H^{\pm} \rightarrow W^{\pm}h$ in 2HDM Type-II using Machine Learning Technique

TL;DR

This paper explores the detection of charged Higgs bosons decaying into a W boson and a neutral Higgs in the 2HDM Type-II, utilizing machine learning to optimize analysis and improve discovery prospects at the LHC.

Contribution

It introduces a machine learning-based approach to optimize the search for charged Higgs bosons in the W+h decay channel within the 2HDM Type-II, focusing on an unexplored tan beta window.

Findings

Optimized kinematic cuts for charged Higgs detection.

Machine learning enhances sensitivity for 5 sigma discovery.

Analysis identifies viable parameter space for future searches.

Abstract

The latest LHC collaborations results on $\sigma_{H^{\pm}}BR(H^{\pm} \rightarrow \tau^{\pm}\nu)$ and $\sigma_{H^{\pm}}BR(H^{+} \rightarrow t\bar{b})$ are used to impose constraints on the charged Higgs $H^{\pm}$ parameters within the Two Higgs Doublet Model (2HDM). But it leaves $1.5 \leq tan \beta \leq 3$ window unexplored where the $BR(H^{\pm} \rightarrow W^{\pm}h)$ becomes sizable for $m_{H^{\pm}} > m_{t}$. In this manuscript $H^{\pm} \rightarrow W^{\pm}h$ is investigated with neutral Higgs boson decaying to a pair of b-quarks and the discovery prospects of charged Higgs boson is discussed. In particular, the analysis is optimized by putting the kinematic cuts and prospects of using Machine Learning Technique to derive values of $\sigma\times BR$ needed for a $5 \sigma$ discovery at the LHC.