The next generation of gamma-ray space telescopes aims to open a new window into gamma-ray astronomy with unprecedented angular resolution, energy resolution, and sensitivity. The goals of these new telescopes range from achieving a better understanding of the element formation in our Galaxy, to identifying the physical processes at work in the extreme conditions around black holes and pulsars, to better understanding gamma-ray bursts associated with gravitational wave events, and to detecting the signatures of dark matter. However, the complex measurement process of gamma rays in our detectors along with the high background conditions, makes achieving these goals a formidable data analysis challenge. This projects aims to augment the existing data analysis techniques for current and future gamma-ray space telescopes with the latest machine learning techniques.
This project (full title: Pioneering new Data-Analysis Techniques for the next generation of gamma-ray space telescope with Machine Learning) is being offered through UC Berkeley's Undergraduate Research Apprentice Program (URAP) for the Spring 2020 academic semester. Eligible undergraduates may apply online January 14-27, 2020.
Project image: A gamma-ray burst GRB160530a as measured with the COSI telescope during its 2016 balloon flight.
Rapid Gamma-Ray Burst Localization aboard the e-Astrogam Satellite using a 3D Convolutional Neural Network
October 16, 2019 | Ruoxi Shang and Andreas Zoglauer | BayLearn Symposium