Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors

arXiv:2305.09744v2 Announce Type: replace-cross
Abstract: 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 …

abstract algorithms arxiv assessment classification cs.lg encode energy events information low low-energy machine machine learning massive physics physics.data-an physics.ins-det type