Probing the Triple Higgs Self-Interaction at the Large Hadron Collider

TL;DR

This paper introduces a new kinematic analysis method that enhances the detection sensitivity of double Higgs production at the LHC by utilizing advanced variables and background discrimination techniques.

Contribution

A novel kinematic approach that improves the discovery potential of double Higgs production, surpassing previous machine learning-based methods.

Findings

Significant increase in sensitivity for double Higgs detection.

Method can be generalized to other Higgs production channels.

Utilizes advanced kinematic variables like Topness and Higgsness.

Abstract

We propose a novel kinematic method to expedite the discovery of the double Higgs ($hh$) production in the $\ell^+\ell^- b \bar{b} + E_T \hspace{-0.52cm} \big / ~$ final state. We make full use of recently developed kinematic variables, as well as the variables $\it Topness$ for the dominant background (top quark pair production) and $\it Higgsness$ for the signal. We obtain a significant increase in sensitivity compared to the previous analyses which used sophisticated algorithms like boosted decision trees or neutral networks. The method can be easily generalized to resonant $hh$ production as well as other non-resonant channels.