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.
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The definitive version is available at https://doi.org/10.22323/1.395.1036
Department(s)
Physics
Publication Status
Open Access
International Standard Serial Number (ISSN)
1824-8039
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Sissa Medialab Srl, All rights reserved.
Publication Date
18 Mar 2022
Comments
National Science Foundation, Grant PRODEP-SEP UDG-CA-499