Pioneering New Data-Analysis Techniques for the Next Generation of Gamma-Ray Space Telescopes
In high-energy astrophysics, the next generation of gamma-ray space telescopes will be used to study gamma-ray bursts, supernovae, pulsars, black holes, dark matter, and more with unprecedented angular resolution and sensitivity. However, accurately tracking Compton-scattering events in the detectors of these telescopes is challenging since most detector systems are not fast enough to time each of the individual interactions. Over the past two semesters, my research has involved using deep learning techniques to try to improve upon classical event reconstruction techniques. In the summer, I will focus on implementing and training a graph neural network to identify the most probable particle tracks in detectors that will be used in future missions.