HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation

Résumé fourni par la source

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.

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