Convolutional Neural Network data analysis development for the Large Sized Telescope of CTA and broadband study of the blazar 1ES 1959+650

This thesis representes the summary of the activities I performed in the field of very-high-energy (VHE) gamma-ray astronomy, and it is articulated in two distinct parts. VHE gamma-ray astronomy is the science studying the photons emitted at TeV energies in cataclysmic events of the Universe. When these highly-energetic gamma-rays interact with the high atmosphere of the Earth, they produce cascades of particles that emit flashes of Cherenkov light. Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and convert them into shower images than can be analyzed to extract the properties of the primary gamma ray. Dominating background for IACTs is constituted by images produced by cosmic hadrons, with typical noise-to-signal ratios of several orders of magnitude. The standard machine learning technique adopted to separate gamma-rays from hadrons is based on a set of parameters extracted from the images. On the other hand, state-of-the-art Deep Learning techniques such as Convolutional Neural Networks could enhance the analysis, since they are able to autonomously extract features from raw images, exploiting the pixel-wise information irreversibly washed out during the parametrization process. In the first part of this work, I present the development of a novel approach to the analysis of the images produced by a new-generation IACT, the Large Sized Telescope (LST) of CTA. I use Convolutional Neural Networks to separate gamma rays from the dominating background of cosmic hadrons and to reconstruct their properties, showing that this technology performs remarkably better than the standard analysis technique. In the second part, I present the study of the emission of the blazar 1ES 1959+650, that is an ive galaxy emitting two extremely energetic jets of plasma in the outer space. First, I describe the analysis of the VHE gamma-ray emission observed during 2017 by the MAGIC IACTs, then I perform a multiwavelength characterization of the activity exhibited by the source between 2016 and 2020, modeling its broadband emission. The results of this investigation give insights on the mechanisms at work in the jets, preluding to a wider study that will probe the physics of this kind of sources to a deeper degree.