Horizontal muon track identification with neural networks in HAWC

and the HAWC Collaboration

Horizontal muon track identification with neural networks in HAWC

and the HAWC Collaboration

Departamento de Física y Matemáticas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Horizontal muon track identification with neural networks in HAWC
Volume	395
Publication status	Published - 18 Mar 2022
Event	37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany Duration: 12 Jul 2021 → 23 Jul 2021

Publication series

Name	Proceedings of Science
Publisher	Sissa Medialab Srl

Conference

Conference	37th International Cosmic Ray Conference, ICRC 2021
Country/Territory	Germany
City	Virtual, Berlin
Period	12/7/21 → 23/7/21

Bibliographical note

Publisher Copyright:
© Copyright owned by the author(s) under the terms of the Creative Commons.

All Science Journal Classification (ASJC) codes

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Cite this

@inproceedings{dc1a530165054631b8819d0ffc63309c,

title = "Horizontal muon track identification with neural networks in HAWC",

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.",

author = "{and the HAWC Collaboration} and {Angeles Camacho}, {J. R.} and {Le{\'o}n Vargas}, H. and Abeysekara, {A. U.} and A. Albert and R. Alfaro and C. Alvarez and {\'A}lvarez, {J. D.} and {Angeles Camacho}, {J. R.} and Arteaga-Vel{\'a}zquez, {J. C.} and Arunbabu, {K. P.} and {Avila Rojas}, D. and {Ayala Solares}, {H. A.} and R. Babu and V. Baghmanyan and Barber, {A. S.} and {Becerra Gonzalez}, J. and E. Belmont-Moreno and BenZvi, {S. Y.} and D. Berley and C. Brisbois and Caballero-Mora, {K. S.} and T. Capistr{\'a}n and A. Carrami{\~n}ana and S. Casanova and O. Chaparro-Amaro and U. Cotti and J. Cotzomi and {Couti{\~n}o de Le{\'o}n}, S. and {De la Fuente}, E. and {de Le{\'o}n}, C. and L. Diaz-Cruz and {Diaz Hernandez}, R. and D{\'i}az-V{\'e}lez, {J. C.} and Dingus, {B. L.} and M. Durocher and DuVernois, {M. A.} and Ellsworth, {R. W.} and K. Engel and C. Espinoza and Fan, {K. L.} and K. Fang and {Fern{\'a}ndez Alonso}, M. and B. Fick and H. Fleischhack and Flores, {J. L.} and Fraija, {N. I.} and D. Garcia and Garc{\'i}a-Gonz{\'a}lez, {J. A.} and Garc{\'i}a-Luna, {J. L.} and H. Mart{\'i}nez-Huerta",

note = "Publisher Copyright: {\textcopyright} Copyright owned by the author(s) under the terms of the Creative Commons.; 37th International Cosmic Ray Conference, ICRC 2021 ; Conference date: 12-07-2021 Through 23-07-2021",

year = "2022",

month = mar,

day = "18",

language = "English",

volume = "395",

series = "Proceedings of Science",

publisher = "Sissa Medialab Srl",

booktitle = "Horizontal muon track identification with neural networks in HAWC",

}

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.

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 - http://www.scopus.com/inward/record.url?scp=85145007955&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85145007955&partnerID=8YFLogxK

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 -

Horizontal muon track identification with neural networks in HAWC

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

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