# Portraying Double Higgs at the Large Hadron Collider

## TL;DR

This paper proposes a deep learning approach to enhance the detection of double Higgs production at the LHC, significantly improving signal sensitivity in a challenging final state with two b-jets, two leptons, and missing energy.

## Contribution

It introduces a novel deep learning framework that leverages full kinematic information and jet images to improve double Higgs signal detection at the LHC.

## Key findings

- Substantial increase in signal sensitivity over existing methods
- Deep learning effectively captures correlations among input variables
- Method adaptable to other processes with similar final states

## Abstract

We examine the discovery potential for double Higgs production at the high luminosity LHC in the final state with two $b$-tagged jets, two leptons and missing transverse momentum. Although this dilepton final state has been considered a difficult channel due to the large backgrounds, we argue that it is possible to obtain sizable signal significance, by adopting a deep learning framework making full use of the relevant kinematics along with the jet images from the Higgs decay. For the relevant number of signal events we obtain a substantial increase in signal sensitivity over existing analyses. We discuss relative improvements at each stage and the correlations among the different input variables for the neutral network. The proposed method can be easily generalized to the semi-leptonic channel of double Higgs production, as well as to other processes with similar final states.