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Slingshot Ushers ‘Agatha’ AI Detection System into Satellite AI World

By Mark Holmes | June 6, 2024

Slingshot Aerospace’s Agatha AI system (Photo by Slingshot Aerospace)

Slingshot Aerospace is bringing the benefits of AI into the satellite arena. The company confirmed Wednesday that it, alongside the Defense Advanced Research Project Agency (DARPA), rolled out a new AI system, called Agatha, that identifies anomalous spacecraft within large satellite constellations.

Slingshot’s Agatha was trained on over 60 years of simulated constellation data that Slingshot created. Slingshot then closed the so-called ‘sim-to-real transfer’ gap and proved-out the system by finding non-nefarious outliers in operational, real-world commercial constellations. After identifying a number of outlier satellites within those constellations, Slingshot successfully confirmed with the respective satellite operators that the identified satellites did differ from the others in hardware, mission, and/or operational parameters.

Slingshot believes AI technologies like Agatha are needed to monitor satellite constellations and track the growing number of objects in space. Agatha specifically analyzes high-resolution astrometric, contextual, and photometric data from the Slingshot Platform’s vast data lake, which aggregates data from the Slingshot Global Sensor Network, Slingshot Seradata, and other public and proprietary sources. Agatha also evaluates the locations and times of satellites’ communications with Earth and a variety of other data streams.

“Agatha represents a breakthrough in how AI can deliver unparalleled space domain awareness, as its ability to find these needles in the haystack is something no human, or team of humans, could possibly execute. Identifying malfunctioning or potentially nefarious objects and their objectives within large satellite constellations is a complex challenge that required us to reach beyond traditional approaches and develop a novel and scalable AI algorithm. Our Agatha model has also proven its ability to deliver high-quality insights that provide ‘explainability’ or context for why specific objects were flagged,” Dylan Kesler, Director of Data Science and AI, Slingshot Aerospace, said in a statement.