Abstract

We characterize the performance of our computational pipeline for real-time gamma-ray burst (GRB) detection and localization aboard the Advanced Particle-astrophysics Telescope (APT) – a space-based observatory for MeV to TeV gamma-ray astronomy – and its smaller, balloon-borne prototype, the Antarctic Demonstrator for APT (ADAPT), whose scientific focus will be the detection of MeV transients. These instruments observe scintillation light from multiple Compton scattering and photoabsorption of gamma-ray photons across a series of CsI detector layers. We infer the incident angle of each photon's first scattering to localize its source direction to a Compton ring about the vector defined by its first two interactions, then intersect rings from multiple photons to identify the GRB's source direction. We first describe algorithmic improvements that enhance localization accuracy (measured in our previous GEANT4 model of APT) while running in under 0.5 seconds on a low-power ARMv8 processor – fast enough to permit real-time redirection of other instruments for follow-up observations. We then study our pipeline's behavior using a model of the smaller ADAPT detector that incorporates realistic estimates of instrument noise and atmospheric background radiation. Adding SiPM-based edge detectors, which gather more light from each scintillation, greatly benefits ADAPT's localization accuracy. We expect that ADAPT can localize normally-incident GRBs of fluence 1 MeV/cm2 and 1-second duration to within 2-3 degrees at least 68% of the time. The full APT instrument, with its larger detector area and lack of atmospheric background, should be substantially more accurate even on GRBs of fluence as low as 0.1 MeV/cm2

Meeting Name

38th International Cosmic Ray Conference (ICRC2023)

Department(s)

Computer Science

Publication Status

Open Access

Comments

National Aeronautics and Space Administration, Grant 80NSSC21K1741

International Standard Serial Number (ISSN)

1824-8039

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publication Date

27 Sep 2024

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