https://doi.org/10.1140/epjp/s13360-024-05287-9
Regular Article
Assessment of few-hits machine learning classification algorithms for low-energy physics in liquid argon detectors
1
Dipartimento di Fisica “G. Occhialini”, Università di Milano-Bicocca, 20126, Milan, Italy
2
Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Milano-Bicocca, 20126, Milan, Italy
3
European Organization for Nuclear Research (CERN), 1211, Geneva, Switzerland
4
Dipartimento di Fisica, Università degli Studi di Milano, 20133, Milan, Italy
5
Istituto Nazionale di Fisica Nucleare (INFN) Sezione di Milano, 20133, Milan, Italy
Received:
15
May
2023
Accepted:
17
May
2024
Published online:
13
August
2024
The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors (“Module of Opportunity”).
Roberto Moretti and Marco Rossi contributed equally to this work.
© The Author(s) 2024
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/.
