Application of Machine and Deep Learning Methods to the Analysis of IACTs Data

The Imaging Atmospheric Cherenkov technique opened a previously inaccessible window for the study of astrophysical sources of radiation in the very high-energy regime (TeV) and is playing a significant role in the discovery and characterization of very high-energy gamma-ray emitters. However, the data collected by Imaging Atmospheric Cherenkov Telescopes (IACTs) are highly dominated, even for the most powerful sources, by the overwhelming background due to cosmic-ray nuclei and cosmic-ray electrons. For this reason, the analysis of IACTs data demands a highly efficient background rejection technique able to discriminate gamma-ray induced signal. On the other hand, the analysis of ring images produced by muons in an IACT provides a powerful and precise method to calibrate the overall optical throughput and monitor the telescope optical point-spread function. A robust muon tagger to collect large and highly pure samples of muon events is therefore required for calibration purposes. Gamma/hadron discrimination and muon tagging through Machine and Deep Learning techniques are the main topics of the present work.