Cats vs Dogs, Photons vs Hadrons

Abstract

In gamma ray astronomy with Cherenkov telescopes, machine learning models are needed to guess what kind of particles generated the detected light, and their energies and directions. The focus in this work is on the classification task, training a simple convolutional neural network suitable for binary classification (as it could be a cats vs dogs classification problem), using as input uncleaned images generated by Montecarlo data for a single ASTRI telescope. Results show an enhanced discriminant power with respect to classical random forest methods.