Uncovering doubly charged scalars with dominant three-body decays using machine learning

TL;DR

This paper develops a deep learning approach to detect doubly charged scalars decaying via three-body processes in complex final states, enhancing search sensitivity at the high-luminosity LHC.

Contribution

It introduces a CNN-based jet image classification method for identifying doubly charged scalars with three-body decays, outperforming traditional kinematic variable analysis.

Findings

CNN outperforms fully connected networks in jet classification

Derived discovery reach and exclusion limits for HL-LHC

Applicable to composite Higgs models with fermionic UV theory

Abstract

We propose a deep learning-based search strategy for pair production of doubly charged scalars undergoing three-body decays to $W^+ t\bar b$ in the same-sign lepton plus multi-jet final state. This process is motivated by composite Higgs models with an underlying fermionic UV theory. We demonstrate that for such busy final states, jet image classification with convolutional neural networks outperforms standard fully connected networks acting on reconstructed kinematic variables. We derive the expected discovery reach and exclusion limit at the high-luminosity LHC.