Machine Learning Aboard the ADAPT Gamma-Ray Telescope

The Advanced Particle-astrophysics Telescope (APT) is an orbital mission concept designed to contribute to multi-messenger observations of transient phenomena in deep space. APT will be uniquely able to detect and accurately localize short-duration gamma-ray bursts (GRBs) in the sky in real time. Current detection and analysis systems require resource-intensive ground-based computations; in contrast, APT will perform on-board analysis of GRBs, demanding analytical tools that deliver accurate results under severe size, weight, and power constraints.In this work, we describe a neural network approach in our computation pipeline for GRB localization, demonstrating the capabilities of two neural networks: one to discard signals from background radiation, and one to estimate the uncertainty of GRB source direction constraints associated with individual gamma-ray photons. We validate the accuracy and computational efficiency of our networks using a physical simulation of GRB detection in the Antarctic Demonstrator for APT (ADAPT), a high-altitude balloon-borne prototype for APT.