TY - JOUR
T1 - Use of Machine Learning for gamma/hadron separation with HAWC
AU - the HAWC Collaboration
AU - Capistrán, T.
AU - Fan, K. L.
AU - Linnemann, J. T.
AU - Torres, I.
AU - Saz Parkinson, P. M.
AU - Yu, Philip L.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 - 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 - Fang, K.
AU - Fernández Alonso, M.
AU - Fick, B.
AU - Fleischhack, H.
AU - Flores, J. L.
AU - Fraija, N. I.
AU - Garcia, D.
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 - Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm based on a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S., VERITAS), which employ a large number of variables to separate the primary particles. In this work, we explore machine learning techniques (Boosted Decision Trees and Neural Networks) to identify the primary particles detected by HAWC. Our new gamma/hadron separation techniques were tested on data from the Crab nebula, the standard reference in Very High Energy astronomy, showing an improvement compared to the standard HAWC background rejection method.
AB - Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm based on a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S., VERITAS), which employ a large number of variables to separate the primary particles. In this work, we explore machine learning techniques (Boosted Decision Trees and Neural Networks) to identify the primary particles detected by HAWC. Our new gamma/hadron separation techniques were tested on data from the Crab nebula, the standard reference in Very High Energy astronomy, showing an improvement compared to the standard HAWC background rejection method.
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M3 - Conference article
AN - SCOPUS:85144633754
SN - 1824-8039
VL - 395
JO - Proceedings of Science
JF - Proceedings of Science
M1 - 745
T2 - 37th International Cosmic Ray Conference, ICRC 2021
Y2 - 12 July 2021 through 23 July 2021
ER -