Astronomy

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• Capabilities
• Case Studies
• Drug Discovery
• Practical Python for Astronomers | GitHub
• OpenNASA
• Inside GNSS; covers the global navigation satellite systems: GPS, Galileo, GLONASS, BeiDou, regional and augmentation systems and related technologies. ...Subscribe
• CosmoGAN: Training a neural network to study dark matter | Kathy Kincade - Lawrence Berkeley National Laboratory
• NASA Is Developing an AI-Powered Navigation System for Space | Kashyap Vyas
• Artificial Intelligence at NASA – Current Projects and Applications - Millicent Abadicio
• Using generative modeling to investigate the physical changes that galaxies undergo as they evolve. (The software they used treats the latent space somewhat differently from the way a generative adversarial network treats it, so it is not technically a GAN, though similar.) . How Artificial Intelligence Is Changing Science | Dan Falk - Quanta Magazine
• Lunar Rover Footage Upscaled With AI Is as Close as You'll Get to the Experience of Driving on the Moon | Andrew Liszewski - Gizmodo
• Surprisingly Recent Galaxy Discovered Using Machine Learning – May Be the Last Generation Galaxy in the Long Cosmic History | National Institutes of Natural Sciences

Asteroids

• Deep Asteroid | openNASA

Universe Simulation

• Worlds’s first AI universe simulator knows things it shouldn’t | Thomas Frey

Collision Avoidance in Space

• Spacecraft Collision Avoidance Challenge | European Space Agency (ESA)
• Close encounters between two objects |European Space Agency (ESA)
• Kessler_Syndrome | Wikipedia ... a theoretical scenario in which the density of objects in low Earth orbit (LEO) due to space pollution is high enough that collisions between objects could cause a cascade in which each collision generates space debris that increases the likelihood of further collisions.

Challenge: Today, active collision avoidance among orbiting satellites has become a routine task in space operations, relying on validated, accurate and timely space surveillance data. For a typical satellite in Low Earth Orbit, hundreds of alerts are issued every week corresponding to possible close encounters between a satellite and another space object (in the form of conjunction data messages CDMs). After automatic processing and filtering, there remain about 2 actionable alerts per spacecraft and week, requiring detailed follow-up by an analyst. On average, at the European Space Agency, more than one collision avoidance manoeuvre is performed per satellite and year. In this challenge, you are tasked to build a model to predict the final collision risk estimate between a given satellite and a space object (e.g. another satellite, space debris, etc). To do so, you will have access to a database of real-world conjunction data messages (CDMs) carefully prepared at ESA. Learn more about the challenge and the data.

Results: Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform the various operators who can then plan risk mitigation measures. Such measures could be aided by the development of suitable machine learning models predicting, for example, the evolution of the collision risk in time. In an attempt to study this opportunity, the European Space Agency released, in October 2019, a large curated dataset containing information about close approach events, in the form of Conjunction Data Messages (CDMs), collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, a machine learning competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying machine learning methods to this problem domain. Spacecraft Collision Avoidance Challenge: design and results of a machine learning competition | T. Uriot, D. Izzo, L. Simoes, R. Abay, N. Einecke, S. Rebhan, J. Martinez-Heras, F. Letizia, J. Siminski, and K. Merz