  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.
