Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis

Imaging Atmospheric Cherenkov Telescopes (IACTs) have revolutionized our understanding of the universe at very high energies (VHEs), enabling groundbreaking discoveries of extreme astrophysical phenomena. These instruments capture the brief flashes of Cherenkov light produced when VHE particles interact with Earth’s atmosphere, providing unique insights into cosmic accelerators and high-energy radiation sources. A fundamental challenge in IACT observations lies in distinguishing the rare gamma-ray signals from an overwhelming background of cosmic-ray events. For every gamma-ray photon detected from even the brightest sources, thousands of cosmic-ray-induced atmospheric showers trigger the telescopes. This profound signal-to-background imbalance necessitates sophisticated discrimination techniques that can effectively isolate genuine gamma-ray events while maintaining high rejection efficiency for cosmic-ray backgrounds. The most common method involves the parametrization of the morphological feature of the shower images. However, we know that gamma-ray and hadron showers also differ in their time evolution. Here, we describe how the pixel time tags (i.e., the record of when each camera pixel is lit up by the incoming shower) can help in the discrimination between photonic and hadronic showers, with a focus on the ASTRI Mini-Array Cherenkov Event Reconstruction. Our methodology employs a Random Forest classifier with optimized hyperparameters, trained on a balanced dataset of gamma and hadron events. The model incorporates feature importance analysis to select the most discriminating temporal parameters from a comprehensive set of time-based features. This machine learning approach enables effective integration of both morphological and temporal information, resulting in improved classification performance, especially at lower energies.