Use of Machine Learning for gamma/hadron separation with HAWC

The HAWC Collaboration

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Article number745
JournalProceedings of Science
StatePublished - Mar 18 2022
Event37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany
Duration: Jul 12 2021Jul 23 2021

All Science Journal Classification (ASJC) codes

  • General


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