Speaker
Description
Gamma-ray Astrophysics concerns the most powerful particle accelerators in the universe and the origins of cosmic rays. In recent years, the field has continuously progressed from studies of individual sources to large-scale surveys. Due to the detection technique used by ground-based gamma-ray telescopes, machine learning has enabled considerable progress in recent years.
The data volumes acquired in modern-day astrophysics prohibit manual examination and increasingly machine learning techniques are also employed for source identification and classification. In this talk, I will explain how machine learning has benefited gamma-ray astronomy and will also introduce some of the planned astrophysical facilities of the future, along with their associated challenges.
