Rapid Gamma-Ray Burst Localization aboard the e-Astrogam Satellite using a 3D Convolutional Neural Network

Ruoxi Shang, Andreas Zoglauer

BayLearn: Bay Area Machine Learning Symposium
October 16, 2019

Abstract: The discovery of the association of a short gamma-ray burst (GRB) with gravitational waves from the neutron-star-neutron-star merger GW170817 gave rise to the requirement to localize GRBs in close to real time even with the most sensitive but also data-analysis-wise most complex gamma-ray space telescopes, the Compton telescopes. Here we report on the implementation and testing of a 3D convolution neural network trained to localize the origin of Compton-scattered gamma-rays from GRB’s on the sky. The ultimate goal of this project is the implementation of a neural network for GRB localization in a FPGA aboard a satellite such as the envisioned e-Astrogam.

This poster was presented as part of the undergraduate internship research project Enabling future gamma-ray space missions, offered through BIDS and URAP.

Presented at the BayLearn Symposium (Bay Area Machine Learning Symposium) on October 16, 2019.



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Andreas Zoglauer

Space Sciences Laboratory
Alumni - DATA SCIENCE FELLOW