TY - GEN
T1 - Horizontal muon track identification with neural networks in HAWC
AU - and the HAWC Collaboration
AU - Angeles Camacho, J. R.
AU - León Vargas, H.
AU - Abeysekara, A. U.
AU - Albert, A.
AU - Alfaro, R.
AU - Alvarez, C.
AU - Álvarez, J. D.
AU - Angeles Camacho, J. R.
AU - Arteaga-Velázquez, J. C.
AU - Arunbabu, K. P.
AU - Avila Rojas, D.
AU - Ayala Solares, H. A.
AU - Babu, R.
AU - Baghmanyan, V.
AU - Barber, A. S.
AU - Becerra Gonzalez, J.
AU - Belmont-Moreno, E.
AU - BenZvi, S. Y.
AU - Berley, D.
AU - Brisbois, C.
AU - Caballero-Mora, K. S.
AU - Capistrán, T.
AU - Carramiñana, A.
AU - Casanova, S.
AU - Chaparro-Amaro, O.
AU - Cotti, U.
AU - Cotzomi, J.
AU - Coutiño de León, S.
AU - De la Fuente, E.
AU - de León, C.
AU - Diaz-Cruz, L.
AU - Diaz Hernandez, R.
AU - Díaz-Vélez, J. C.
AU - Dingus, B. L.
AU - Durocher, M.
AU - DuVernois, M. A.
AU - Ellsworth, R. W.
AU - Engel, K.
AU - Espinoza, C.
AU - Fan, K. L.
AU - Fang, K.
AU - Fernández Alonso, M.
AU - Fick, B.
AU - Fleischhack, H.
AU - Flores, J. L.
AU - Fraija, N. I.
AU - Garcia, D.
AU - García-González, J. A.
AU - García-Luna, J. L.
AU - Martínez-Huerta, H.
N1 - Publisher Copyright:
© Copyright owned by the author(s) under the terms of the Creative Commons.
PY - 2022/3/18
Y1 - 2022/3/18
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85145007955
UR - https://www.scopus.com/pages/publications/85145007955#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85145007955
VL - 395
T3 - Proceedings of Science
BT - Horizontal muon track identification with neural networks in HAWC
T2 - 37th International Cosmic Ray Conference, ICRC 2021
Y2 - 12 July 2021 through 23 July 2021
ER -