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").

翻译：低能物理领域中的大型液氩时间投影室（TPC）的物理潜力尚未完全发掘，因为少击中事件所编码的信息难以被传统分类算法有效利用。机器学习（ML）技术在此类分类问题中展现出最佳性能。本文评估了机器学习算法与传统（确定性）算法的性能对比。我们证明，在低能物理最具挑战性的分类问题（单β事件与双β事件区分）中，卷积神经网络（CNN）和Transformer-Encoder方法均优于确定性算法。我们讨论了Transformer-Encoder方法相对于CNN的优势与局限性，并利用这些方法优化探测器参数，重点关注DUNE二期探测器（“机遇模块”）。