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 from gamma showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm that is a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. HESS, 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 that were 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.
Machine Learning, G/H separation, High Energy
|Subcategory||Experimental Methods & Instrumentation|