Abstract
High-energy transient astrophysical phenomena, such as supernovae and binary neutron star mergers, benefit from a multi-wavelength investigation in which a space- or balloon-based omnidirectional telescope detects and localizes early high-energy emissions (such as a gamma-ray burst), then alerts a narrow-field follow-up instrument to observe the source. The high-energy telescope must provide a map that assigns to each sky location a likelihood that the source appears there. To issue prompt alerts despite limits on communication bandwidth and latency, it is desirable to compute this map aboard the high-energy telescope, but doing so requires rapid response while computing under stringent size, weight, and power constraints. This work describes a real-time likelihood mapping implementation for Compton telescopes that is suitable for on-board computation. We use an adaptive multi-resolution approach and exploit parallelism and data reduction opportunities to achieve sub-second construction of high-resolution maps (HEALPix Nside=64) using a detailed instrument response matrix on a low-power (< 10 W) embedded computing platform. We validate the speed and accuracy of our mapping approach on simulated high-energy transients from the third COSI Data Challenge.
Recommended Citation
J. Buhler and M. Sudvarg, "Real-time Likelihood Map Generation To Localize Short-duration Gamma-ray Transients," Proceedings of Science, vol. 501, article no. 587, SISSA Medialab Srl, Dec 2025.
The definitive version is available at https://doi.org/10.22323/1.501.0587
Meeting Name
39th International Cosmic Ray Conference (ICRC2025)
Department(s)
Computer Science
Publication Status
Open Access
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
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
30 Dec 2025
Included in
Databases and Information Systems Commons, Other Computer Sciences Commons, Systems Architecture Commons
Comments
Acknowledgements: We wish to acknowledge our colleagues in the APT collaboration (https://adapt.physics.wustl.edu/) and to thank the COSI Science Team for assistance with cosipy and the DC3 data sets. This work was supported by NASA award 80NSSC21K1741.