Horizontal muon track identification with neural networks in HAWC

TL;DR

This paper explores the use of neural networks to improve horizontal muon track identification in HAWC, enhancing detection efficiency for potential neutrino measurements.

Contribution

It introduces three neural network models for muon track detection in HAWC, significantly increasing identified tracks over previous algorithms.

Findings

Increased muon track detection rate

Neural networks outperform traditional algorithms

Potential to improve neutrino measurement techniques

Abstract

Nowadays the implementation of artificial neural networks in high-energy physics has obtained excellent results on improving signal detection. In this work we propose to use neural networks (NNs) for event discrimination in HAWC. This observatory is a water Cherenkov gamma-ray detector that in recent years has implemented algorithms to identify horizontal muon tracks. However, these algorithms are not very efficient. In this work we describe the implementation of three NNs: two based on image classification and one based on object detection. Using these algorithms we obtain an increase in the number of identified tracks. The results of this study could be used in the future to improve the performance of the Earth-skimming technique for the indirect measurement of neutrinos with HAWC.