Astronomy

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• Capabilities
• Case Studies
  • Drug Discovery
• Practical Python for Astronomers | GitHub
• OpenNASA
• Artificial Intelligence at NASA – Current Projects and Applications - Millicent Abadicio

Google Cloud, NASA FDL & the SETI Institute in search for life on other planets In search for life on other planets, Google Cloud is partnering up with NASA FDL and SETI Institute. NASA is utilizing Google Cloud to simulate as much data as possible, reading patterns and forming connections from thousands of datasets. Watch the amazing conversation between Massimo Mascaro, Technical Director for Applied AI at Google Cloud and Seth Shostak, Senior Astronomer of the SETI Institute, about Artificial Intelligence, Machine Learning, and all the work behind of partnership of Google Cloud & NASA FDL.

AI and Space Exploration | Intel® AI Interplanetary Show | Intel Software Bill Nye, Robert Picardo, and Intel’s Hanlin Tang begin our journey by discussing how today’s AI technology helps us explore the solar system.

Jake Vanderplas - Keynote - PyCon 2017 Slides can be found at: https://speakerdeck.com/pycon2017 and https://github.com/PyCon/2017-slides"

AMLD2018 - Kevin Schawinski, ETH Zurich: Exploring the universe with AI The Applied Machine Learning Days channel features talks and performances from the Applied Machine Learning Days. AMLD is one of the largest machine learning & AI events in Europe, focused specifically on the applications of machine learning and AI, making it particularly interesting to industry and academia.

Dark Matter

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• CosmoGAN: Training a neural network to study dark matter | Kathy Kincade - Lawrence Berkeley National Laboratory

Galaxy Evolution

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Galaxies / Stars

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• Surprisingly Recent Galaxy Discovered Using Machine Learning – May Be the Last Generation Galaxy in the Long Cosmic History | National Institutes of Natural Sciences

Object Classification

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• Machine Learning Just Classified Over Half a Million Galaxies | Andy Tomaswick - Universe Today ...scientists trained the algorithm using images of spiral-patterned galaxies similar to the Milky Way. When used on the test set, the algorithm accurately classified 95.7 percent of galaxies.

Black Holes

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Sun / Solar

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Planets

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• Breakthrough AI identifies 50 new planets from old NASA data | Jessie Yeung - CNN Business

Earth

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• Environmental Science

Mars

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• NASA Is Developing an AI-Powered Navigation System for Space | Kashyap Vyas

Moon

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• Lunar Rover Footage Upscaled With AI Is as Close as You'll Get to the Experience of Driving on the Moon | Andrew Liszewski - Gizmodo

Man-made (artificial) Satellites

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• Satellite Imagery
• Inside GNSS; covers the global navigation satellite systems: GPS, Galileo, GLONASS, BeiDou, regional and augmentation systems and related technologies. ...Subscribe

Asteroids

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• Deep Asteroid | openNASA

Collision Avoidance in Space

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• Artificial Intelligence Solutions to Track and Map Space Debris | Seer Tracking
• Spacecraft Collision Avoidance Challenge | European Space Agency (ESA)
• Data: 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.
• tsfresh ...python package that automatically calculates a large number of time series characteristics, the so called features. Further the package contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks.
• Genetic Programming using random forest
  • Evaluation of a tree-based pipeline optimization tool for automating data science | R. Olson, N. Bartley, R. Urbanowicz, and J. Moore
• LightGBM ...Microsoft's gradient boosting framework that uses tree based learning algorithms
• Manhattan LSTM (MaLSTM) a Siamese architecture based on recurrent neural network
• Monte Carlo Cross-Validation

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. ...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

Astropy Project

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• Astropy Project ...a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages.

Simulation

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