Improving smuon searches with Neural Networks

TL;DR

This paper introduces a neural network-based approach to enhance LHC searches for light electroweak scalars decaying to muons and invisible particles, demonstrating improved sensitivity over traditional methods.

Contribution

It presents a novel neural network discriminator for LHC searches, with a workflow from simulation to limit projection, and shows improved sensitivity using publicly available tools.

Findings

Neural networks improve search sensitivity for light scalars.

Different signal regions can be optimized with trained networks.

The method outperforms previous search strategies.

Abstract

We demonstrate that neural networks can be used to improve search strategies, over existing strategies, in LHC searches for light electroweak-charged scalars that decay to a muon and a heavy invisible fermion. We propose a new search involving a neural network discriminator as a final cut and show that different signal regions can be defined using networks trained on different subsets of signal samples (distinguishing low-mass and high-mass regions). We also present a workflow using publicly-available analysis tools, that can lead, from background and signal simulation, to network training, through to finding projections for limits using an analysis and ${\tt ONNX}$ libraries to interface network and recasting tools. We provide an estimate of the sensitivity of our search from Run 2 LHC data, and projections for higher luminosities, showing a clear advantage over previous methods.