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Onboard Machine Learning for High-Energy Observatories for Spacecraft Autonomy and Ground Segment Operations

Year: 2025

Type: article

Abstract: Next-generation space observatories for high-energy gamma-ray astrophysics will increase scientific return using onboard machine learning (ML). This is now possible thanks to today’s low-power, radiation-tolerant processors and artificial intelligence accelerators. This paper provides an overview of current and future ML applications in gamma-ray space missions focused on high-energy transient phenomena. We discuss onboard ML use cases that will be implemented in the future, including real-time event detection and classification (e.g., gamma-ray bursts), and autonomous decision-making, such as rapid repointing to transient events or optimising instrument configuration based on the scientific target or environmental conditions.

Source: Particles

DOI: 10.3390/particles8040102

Authors: Andrea Bulgarelli, Luca Castaldini, Nicolò Parmiggiani, Ambra Di Piano, Riccardo Falco, Alessio Aboudan, Lorenzo Amati, Andrea Argan, Paolo Calabretto, Mauro Dadina, Adriano De Rosa, Valentina Fioretti, Claudio Labanti, Giulia Mattioli, Gabriele Panebianco, Carlotta Pittori, Alessandro Rizzo, Smiriti Srivastava, Enrico Virgilli

Institutions: Osservatorio di Astrofisica e Scienza dello Spazio, National Institute for Astrophysics, University of Padua, Institute for Space Astrophysics and Planetology, Astronomical Observatory of Rome

Language: English

Topic: Gamma-ray bursts and supernovae

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

OpenAlex ID: W7114892044

FWCI:

Cites: 24

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