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

Machine Learning and Cosmology: New Approaches to Constraining the Dark Universe Matias Carrasco Kind is a current astronomy graduate student and a CSE fellow at the University of Illinois at Urbana-Champaign. His current research interests lie in cosmology and extragalactic astronomy, especially in large scale structure, galaxy formation and evolution, computational and theoretical cosmology, environmental dependence of galaxy properties, photometric redshift estimation, machine learning techniques and data mining.

Dr Francois Lanusse - Exploring the Cosmos with Deep Learning The main challenge of modern Cosmology is to answer pressing questions on the physical nature of dark matter and dark energy, which despite accounting for ~95% of the Universe today remain complete mysteries. This is what motivates a new generation of cosmological surveys which will map the Universe in great detail and on an unprecedented scale, implying a great potential for new discoveries but also new and outstanding challenges at every step of the science analysis, from image processing to the modelling of galaxy physics. In this talk, I will illustrate how recent advances in Deep Learning can be used to address some of these challenges and to exploit this wealth of data in new and exciting ways. A first application is the automated detection of rare astronomical objects, in this case strong gravitational lenses, using deep residual networks. In this typical image classification problem, Deep Learning has the potential of eliminating the need for human visual inspection which would have been intractable at the scale of future surveys. In a second example of application, I will present recent deep generative models and how they can be applied to emulate realistic signals when a proper physical model is lacking. In our specific application, we use deep generative models to produce realistic galaxy images, an essential part of the simulation pipeline necessary to the validation and calibration of our measurements. The last example I will mention is Deep Learning on graphs, more specifically how we use graph convolutional networks to model the properties of galaxies along the cosmic web (the large-scale structure of the Universe) in large-scale cosmological simulations.

Prof. Ofer Lahav: From Deep Learning to the Dark Universe The talk will illustrate how new Artificial Intelligence and Machine Learning methods can help unveiling the mysteries of Dark Matter and Dark Energy from large deep galaxy surveys. Prof Ofer Lahav is an astrophysicist of international renown and holds the Perren Chair of Astronomy at University College London. He is famous for his advanced research on Dark Matter and Dark Energy by using methods like Machine Learning and is one of the founders of the Dark Energy Survey (DES). He has served as Vice-President of the Royal Astronomical Society and Co-Chair of the international DES Science Committee and has been awarded a wide variety of prices and honours. His projects include HST CLASH, DESI, LSST, Euclid, and several others focussed on understanding and analysing the behaviour of Dark Matter.

Artificial Intelligence In Space Exploration In this video we will learn about the use of artificial intelligence in space exploration. Space is fascinating. There is hardly anyone who hasn’t looked at the night sky and imagined traveling through the space. There are hardly few rivalries that benefited mankind. One of them is the cold war between Russia and USA. That opened up the opportunity in space exploration. When one of these countries tried to shine over the other one, mankind shined with knowledge about space. And from there to today’s SPACEX age we have come across a long way. Starting from manually calculating the trajectory for moon landing to modern day usage of artificial intelligence in space exploration we have come a long way and yet there are many things to be done. By the way, did you know, Katherine Johnston, an African-American female physicist was the first person to do the calculations for the first actual moon landing in 1969.

Galaxy Evolution

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Understanding Galaxy Evolution through Machine Learning Presentation for Summer All Zoom Epoch of Reionization Astronomy Conference (SAZERAC) 2020

John Wu - Deep learning in astrophysics: galaxy scaling relations Deep learning in astrophysics: galaxy scaling relations. I will discuss some applications deep convolutional neural networks (convnets), and present a high-level overview of how to select, optimize, and interpret convnet models. We have trained a convnet to recognize a galaxy's chemical abundance using only an image of the galaxy; the traditional approach using spectroscopy requires at least an order of magnitude more telescope time and achieves a comparable level of accuracy to our method. We discover that the convnet can recover an empirically known scaling relation that connects galaxies' chemical enrichment and star formation histories with zero additional scatter, implying that there exists (and that the convnet has learned) a novel representation of the chemical abundance that is strongly linked to its optical-wavelength morphology.

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