Document Type
Article
Publication Date
10-10-2025
Department
Department of Physics
Abstract
Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With more than a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich data set, we introduce a machine learning approach for gamma-hadron separation. A multilayer perceptron shows the best performance, surpassing traditional and other machine learning-based methods. This approach shows a notable improvement in the detector’s sensitivity, supported by results from both simulated and real HAWC data. In particular, it achieves a 19% increase in significance for the Crab Nebula, commonly used as a benchmark. These improvements highlight the potential of machine learning to significantly enhance the performance of HAWC and provide a valuable reference for ground-based observatories, such as the Large High Altitude Air Shower Observatory and the upcoming Southern Wide-field Gamma-ray Observatory.
Publication Title
Astrophysical Journal
Recommended Citation
Alfaro, R.,
Alvarez, C.,
Andrés, A.,
Anita-Rangel, E.,
Araya, M.,
Arteaga-Velázquez, J.,
Ghosh, N.,
Hüntemeyer, P.,
Leavitt, K.,
Najafi, M.,
&
et. al
(2025).
HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation.
Astrophysical Journal,
992(1).
http://doi.org/10.3847/1538-4357/ae0186
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2063
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2025. The Author(s). Published by the American Astronomical Society. Publisher’s version of record: https://doi.org/10.3847/1538-4357/ae0186