We propose a deep learning-based search strategy for pair production of doubly charged scalars undergoing three-body decays to W+tbˉ 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.
@article{arxiv.2304.09195,
title = {Uncovering doubly charged scalars with dominant three-body decays using machine learning},
author = {Thomas Flacke and Jeong Han Kim and Manuel Kunkel and Pyungwon Ko and Jun Seung Pi and Werner Porod and Leonard Schwarze},
journal= {arXiv preprint arXiv:2304.09195},
year = {2023}
}