Speaker
Description
Gamma rays are key when it comes to studying topics such as dark matter from an indirect perspective or the Lorentz invariance, as well as probing a wide range of astrophysical phenomena that provide a deeper understanding of the most energetic events in the universe. They can be detected through Imaging Atmospheric Cherenkov Telescopes (IACTs), which capture images of extensive air showers generated by gamma rays and cosmic rays (high-energy particles of astrophysical origin) when they interact with the atmosphere. One of the main challenges about these images is the reconstruction of the event’s properties, i.e., obtaining the direction of arrival, energy and type (gamma ray, proton, electron, etc.) of the particles that triggered the shower. AI techniques, such as deep learning methods, have been demonstrated to be suitable for the reconstruction of these events since they are used to analyze and exploit loads of data for carrying out classification and characterization tasks. This presentation provides a brief introduction to gamma-ray astronomy and IACTs, followed by a focus on enhancing IACT event reconstruction using deep learning techniques through the CTLearn package, within the framework of the Cherenkov Telescope Array Observatory (CTAO).
