Horizontal muon track identification with neural networks in HAWC

and the HAWC Collaboration

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Article number1036
JournalProceedings of Science
Publication statusPublished - 18 Mar 2022
Event37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany
Duration: 12 Jul 202123 Jul 2021

Bibliographical note

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All Science Journal Classification (ASJC) codes

  • General


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