https://doi.org/10.1140/epjc/s10052-025-13748-3
Regular Article - Theoretical Physics
Improving smuon searches with neural networks
1
Department of Physics, University of Johannesburg, PO Box 524, 2006, Auckland Park, South Africa
2
Laboratoire de Physique Théorique et Hautes Énergies (LPTHE), UMR 7589, Sorbonne Université and CNRS, 4 Place Jussieu, 75252, Paris Cedex 05, France
Received:
21
November
2024
Accepted:
30
December
2024
Published online:
22
January
2025
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 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.
© The Author(s) 2025
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Funded by SCOAP3.
