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").

本文讨论机器学习在低能物理学中的应用，以及利用卷积神经网络和 Transformer-Encoder 方法优化探测器参数以处理低数量事件分类问题的结果。