Machine Learning Aboard The ADAPT Gamma-Ray Telescope
Abstract
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.
Recommended Citation
Y. Htet et al., "Machine Learning Aboard The ADAPT Gamma-Ray Telescope," Proceedings of Sc 2024 W Workshops of the International Conference for High Performance Computing Networking Storage and Analysis, pp. 4 - 10, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/SCW63240.2024.00008
Department(s)
Computer Science
Keywords and Phrases
machine learning; multi-messenger astrophysics; neural networks
International Standard Book Number (ISBN)
979-8-3503-5554-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2024
Comments
National Aeronautics and Space Administration, Grant 80NSSC21K1741