Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC

TL;DR

This paper employs machine learning techniques to enhance the search for radiative decays of neutralinos into dark matter at the LHC, potentially uncovering new physics beyond current methods.

Contribution

It introduces ML-based analysis methods to improve detection sensitivity for radiative neutralino decays in supersymmetry at the LHC.

Findings

ML methods extend the LHC reach into unexplored parameter space.

ML techniques outperform traditional cut-based analyses.

Potential for discovering neutralino decays consistent with dark matter observations.

Abstract

The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario. We demonstrate that using ML techniques would enable access to most of the parameter space unexplored by other searches.