Horizontal muon track identification with neural networks in HAWC
- and the HAWC Collaboration,
- J. R. Angeles Camacho(corresponding author)(Author),
- H. León Vargas(Author),
- A. U. Abeysekara(Author),
- A. Albert(Author),
- R. Alfaro(Author)
- Instituto de Física,
- University of Utah,
- Los Alamos National Laboratory,
- Universidad Nacional Autónoma de México,
- Universidad Autonoma de Chiapas,
- Universidad Michoacana de San Nicolas de Hidalgo
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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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Original language
EnglishPublication milestones
- Published - 18/03/2022
Publication status
Volume
395Publication series
- Publisher name: Sissa Medialab Srl
Publication series name: Proceedings of Science
Publication IDs
- Scopus: 85145007955
