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arxiv: 2506.18277 · v1 · pith:ZKL7YV6Lnew · submitted 2025-06-23 · 🌌 astro-ph.IM · astro-ph.HE

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation

R. Alfaro , C. Alvarez , A. Andr\'es , E. Anita-Rangel , M. Araya , J.C. Arteaga-Vel\'azquez , D. Avila Rojas , H.A. Ayala Solares
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R. Babu P. Bangale E. Belmont-Moreno A. Bernal T. Capistr\'an A. Carrami\~nana F. Carre\'on S. Casanova U. Cotti E. De la Fuente D. Depaoli P. Desiati N. Di Lalla R. Diaz Hernandez M.A. DuVernois J.C. D\'iaz-V\'elez K. Engel T. Ergin C. Espinoza K.L. Fan N. Fraija S. Fraija J.A. Garc\'ia-Gonz\'alez F. Garfias N. Ghosh A. Gonzalez Mu\~noz M.M. Gonz\'alez J.A. Gonz\'alez J.A. Goodman S. Groetsch J.P. Harding S. Hern\'andez-Cadena I. Herzog D. Huang P. H\"untemeyer A. Iriarte S. Kaufmann D. Kieda K. Leavitt J. Lee H. Le\'on Vargas J.T. Linnemann A.L. Longinotti G. Luis-Raya K. Malone O. Martinez J. Mart\'inez-Castro J.A. Matthews P. Miranda-Romagnoli P.E. Mir\'on-Enriquez J.A. Montes J.A. Morales-Soto E. Moreno M. Najafi A. Nayerhoda L. Nellen N. Omodei M. Osorio E. Ponce Y. P\'erez Araujo E.G. P\'erez-P\'erez C.D. Rho A. Rodriguez Parra D. Rosa-Gonz\'alez M. Roth H. Salazar A. Sandoval J. Serna-Franco A.J. Smith Y. Son R.W. Springer O. Tibolla K. Tollefson I. Torres R. Torres-Escobedo E. Varela L. Villase\~nor X. Wang Z. Wang I.J. Watson H. Wu S. Yu H. Zhou C. de Le\'on
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Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With over a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich dataset, 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 Large High Altitude Air Shower Observatory (LHAASO) and the upcoming Southern Wide-field Gamma-ray Observatory (SWGO).

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