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

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.