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

Yashar Hezaveh: Mapping distant galaxies with artificial intelligence We are in the midst of a revolution in computing, with "machine learning" algorithms solving problems for which major progress was thought to be decades away. As a result, computers can now exceed human performance at tasks such as recognizing patterns in images, playing complex games such as Go, and even driving cars. Can we harness the same machine learning algorithms to advance our understanding of physics? Understanding materials at the atomic level could lead to rapid advances in areas such as solar energy harvesting and the design of new computer hardware. The complex behavior of trillions of atoms all obeying the esoteric rules of quantum mechanics is not, however, easy for humans to untangle on their own. At this teachers' conference, we hear from scientists using the techniques of artificial intelligence and machine learning to advance our understanding of the quantum behavior of such matter as magnets, metals and superconductors, as well as the constituents of atoms. We also hear how insights from physics could be repurposed to advance the field of machine learning itself.

AI in the Sky | Artificial Intelligence in Space Exploration | upGrad - Future Forward Right from the beginning of time, we have been studying and exploring outer space. Today, as we are receiving the most advanced data, Artificial Intelligence is contributing significantly to space exploration by helping scientists make empirical, smart decisions faster. upGrad presents #FutureForward, AI in Sky. Check out this video to know how AI is empowering scientists in the fields of astronomy.

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.

Galaxy Classification with Machine Learning - Jim Geach (University of Hertfordshire) on 08/03/2019.

Machine Learning Based Morphological Classification of Type Ia Supernova Host Galaxies Gavin McCabe (Caltech) CASSI Symposium, 08/21/2020

Data, Science: Investigating 1 Million Galaxies with Humans and TensorFlow - Mike Walmsley Deep learning is making astrophysicists excited and afraid. We're excited because we can show a neural network every galaxy in the sky and ask questions, and afraid because we don't understand the answers. I'll talk about how you can investigate galaxy evolution with TensorFlow and crowdsourced labels, what we're discovering, and where it all falls apart. www.pydata.org

Machine Learning Application on Galaxy Morphological Classification Using Dark Energy Survey Images by Ting-Yun Cheng (University of Nottingham) on 08/03/2019.

CONVOLUTED COSMOS : Automatic Classification of Galaxy Images Using Deep Learning In this project, Galaxy Image Classification using a Deep Convolution Neural Network is presented. The galaxy can be classified based on its features into three main categories, namely: Elliptical, Spiral, and Irregular. The proposed deep galaxy architecture consists of one input convolutional layer having 16 filters, followed by 3 hidden layers, 1 penultimate dense layer and an Output Softmax layer. It is trained over 3232 images for 200 epochs and achieved a testing accuracy 97.38% which outperformed conventional classifiers like Support Vector Machine and previous research contributions in the same domain of Galaxy Image Classification.

Astronomical Source Classification with Deep Learning This video is a 2 minute introduction to a project (with a longer video here: https://youtu.be/kRuVtPhhKJU) that uses deep learning and convolutional neural networks to classify X-Ray images from the Chandra X-Ray Observatory into classes of either galaxy or galaxy cluster. It was developed as part of Harvard E89 Deep Learning Final Project.

Black Holes

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Sensing to Intelligence: From Human Body to Black Holes in Space / Caltech Seminar Day Session II Caltech professors Azita Emami and Katherine Bouman discussed a holistic approach that brings together the two distinct fields of sensor technology and artificial intelligence through the co-design of sensors and algorithms. The aim is to create sensor networks that intelligently and autonomously process their own data and adapt their activity in real time based on what they detect, enabling transformative discoveries in areas ranging from neural signals in the human body to black holes in space. Speakers: Katherine Bouman Assistant Professor of Computing and Mathematical Sciences and Electrical Engineering; Rosenberg Scholar Azita Emami Andrew and Peggy Cherng Professor of Electrical Engineering and Medical Engineering; Investigator, Heritage Medical Research Institute; Executive Officer for Electrical Engineering

What Would a Journey to the Black Hole Be Like? Today, I'm setting off on a journey toward the nearest black hole. But don't worry - I'll keep you in the know by live-streaming my entire adventure! I'll have someone to talk to during the flight, and he can help me if things get really tough! My travel buddy's name is Liam. Liam is a robot with artificial intelligence. Space distances are seriously long. That's why traveling there would take way more time than you'd like to spend on the road! For example, Voyager 1, a space probe launched in 1977, was traveling out of the Solar System at a speed of 40,000 miles per hour. If my spacecraft moved at the same speed, it would take me a whole 77,000 years to get to the nearest star! But luckily, my spaceship is much faster than that. So let the journey begin!

Sun / Solar

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Deep Regression for Imaging Solar Magnetograms using Pyramid Generative Adversarial Networks Rasha Alshehhi (NYU)

The Path to Safe Fusion Energy through Deep Learning When the plasma inside a fusion reactor becomes unstable, there can be a release of energy that seriously damages the reactor. Key to preventing damage is predicting when these disruptions are about to happen. Professor William Tang of Princeton has successfully created a neural network that predicts these disruptions to a high level of accuracy, and was invited to give a talk at SDSC on his work. Tang's neural network is tuned specifically to predict disruptions, but the techniques used to train the neural network have crosscutting applications to other areas of scientific interest like cancer research.

Planets

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

Sara Seager: Search for Planets and Life Outside Our Solar System | Lex Fridman Podcast #116 Sara Seager is a planetary scientist at MIT, known for her work on the search for exoplanets.

Looking for exoplanets using supervised machine learning "Data science in astronomical high-contrast imaging: looking for exoplanets using supervised machine learning" by Carlos Alberto Gomez Gonzalez , IPAG, Université Grenoble Alpes

Artificial Intelligence and NASA Data Used to Discover Eighth Planet Circling Distant Star Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light years from Earth. The planet was discovered in data from NASA’s Kepler space telescope. The newly-discovered Kepler-90i -- a sizzling hot, rocky planet that orbits its star once every 14.4 days -- was found by researchers from Google and The University of Texas at Austin using machine learning. Machine learning is an approach to artificial intelligence in which computers “learn.” In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets.

AI helped NASA find a new planet! Follow Nexter for more: https://nexter.org/ In the most recent NASA news, they have discovered an eighth planet which revolves around the star. This new planet adds to the growing Earth like planets list. Known as one of the Kepler planet, this planet was discovered by Google’s artificial intelligence by thoroughly searching those weak signals which has been overlooked by the Kepler Space Telescope. This newest galactic figure adds up the new planets discovered by NASA. This new planet, which is being touted as “Earth 2.0”, this Kepler new planet is located around Kepler-90 which is a star which has a distance of 2,500 light years from earth. This star was discovered in 2014. The new planet, however, cannot handle life like the Earth does because of its solar system. Kepler 90 is bigger by 20 percent and 5 percent hotter compared to our sun. The participation of computers and artificial intelligence in the planet searching process should not be of concern, however, by human beings. Artificial intelligence and technology are just there to help scientists and researchers look for more planets and find one that can support human life. More and more Earth sized planets are being discovered and being added into the NASA new planets. A lot of them are Kepler exoplanets. Astronomy and heavenly bodies are something that is very interesting, and every intergalactic news is being followed instantly. For more news and fun facts about the outer space, check out Nexter now.

SETI Talks - Big Astronomy Begins: Searching for Exoplanets with AI To uncover the mysteries of the universe, astronomers are becoming greedy, making more observations than they can possibly analyze manually. Large photometric surveys from space telescopes like Kepler and the future TESS are no exception and today modern astronomers use artificial Intelligence (AI) algorithms to help them reveal the existence of exoplanets hidden in many years of observations of hundreds of thousands of stars. For this SETI Talk, we invited two researchers involved in the Kepler mission and AI to discuss the potential of neural networks to transform astronomy. Jeff Smith, Data scientist at the SETI Institute, has developed data processing and planet detection algorithms for Kepler since 2010 and is now involved in developing the pipeline for the future TESS mission. Chris Shallue, a senior software engineer at Google AI has used a neural network to analyze archival data from the Kepler Space Telescope to reveal the existence of two unknown exoplanets, named Kepler-90i and Kepler-80g. After presenting their recent work, we will discuss the impact of this new mode of scientific discovery, where artificial intelligence can assist humans in mapping out parts of the galaxy that have not yet been fully revealed. Chris Shallue is a Senior Research Software Engineer on the Google AI team in Mountain View, California. His research is currently focused on machine learning techniques for identifying planets in data collected by the NASA Kepler space telescope. He also works on image captioning, natural language modeling and machine learning theory. Chris was previously a member of the Google Display Ads team where he worked on ad selection and personalization for GMail and Google Maps. Prior to joining Google, Chris was teaching, studying and researching mathematics. Dr. Jeffrey Smith began his academic passion in the field of High Energy Accelerator Physics. His Ph.D. thesis was on the design of the International Linear Collider (ILC), a 22 mile-long electron-positron accelerator that will complement the discoveries being made at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland. After Cornell, Jeff joined the SLAC National Accelerator Laboratory at Stanford University to continue his work on the ILC and also to develop upgrade hardware for the LHC. After a successful career looking into the tiniest of inner-spaces Jeff decided to look up to the stars. Dr. Smith, now at the SETI Institute, develops data processing and planet detection algorithms for the Kepler and TESS Missions. Eking out planet signals in the Kepler data has proven to be a challenging and rewarding endeavor but looking toward the future, Dr. Smith is involved with developing new methods for use with the Transiting Exoplanet Survey Satellite (TESS), a new NASA planet finding mission to find Earth’s nearest cousins in a our galactic back yard.

Astrobiology and the Search for Extraterrestrial Life - with Ian Crawford What can modern results in astrobiology tell us about the prospects for finding intelligent life elsewhere in the Universe? The famous Drake equation, which provides a rough estimate of the number of civilisations in our galaxy, predicts that space should be teaming with aliens. So where are they and why have we not found them yet? Watch the Q&A here: https://youtu.be/l7Xh_aphD30 Ian Andrew Crawford is professor of planetary science and astrobiology at Birkbeck, University of London. Crawford is a specialist in the science and exploration of the Moon and in the search for life in the Universe. Before switching his research interests to planetary science in 2003, Crawford had a 15-year career at University College London as an observational astronomer specialising in studies of the interstellar medium. He is the author of over 130 peer-reviewed research papers in the fields of astronomy, planetary science, astrobiology and space exploration. Crawford is a Fellow, and currently Vice President, of the Royal Astronomical Society, and a former member of the European Space Sciences Committee (ESSC) of the European Science Foundation. In 2014 he was appointed to the European Space Agency's Human Spaceflight and Exploration Science Advisory Committee (HESAC).

Earth

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• Environmental Science

Microsoft Introduces AI For Earth To Help The Planet With Machine Learning Hot off Google announcing its own initiative for improving artificial intelligence's impact on humanity, fellow AI research giant Microsoft has announced a program dedicated to improving the planet through machine learning. AI for Earth, revealed during Microsoft's AI event today in London, will assist organizations using AI for environmental protection, innovation and research — particularly those addressing issues in water conservation, agriculture, biodiversity and climate change. According to Microsoft, AI for Earth will operate on three major "pillars" — granting access to Microsoft's resources for research groups, providing educational resources to teach said groups how to utilize AI optimally and special lighthouse projects that innovate AI's ability to study the environment.

Neal Jean, " "Combining satellite imagery and machine learning to predict poverty" Neal Jean, Michael Xie, Stefano Ermon, Matt Davis, Marshall Burke, David Lobell "Combining satellite imagery and machine learning to predict poverty" Stanford University Depts of Computer Science and Earth Systems Science CompSust-2016 4th International Conference on Computational Sustainability July 7, 2016

Geospatial Machine Learning for Urban Development Ilke Demir, Postdoctoral Research Scientist, Facebook Presented at MLconf 2018 Abstract: The collective mission of mapping the world is never complete: We need to discover and classify roads, settlements, land types, landmarks, and addresses. The recent proliferation of remote sensing data (overhang images, LiDAR, sensors) enabled automatic extraction of such structures to better understand our world. In this talk, we will first mention the motivation and results of DeepGlobe Satellite Image Challenge[1][2], for road extraction, building detection, and land cover classification. Then we will go into details of an example approach[3] which proposes a complete system to use deep learning for generating street addresses for unmapped developing countries. The approach applies deep learning to extract road vectors from satellite images, then processes the street network to output linear and hierarchical street addresses, by labeling regions, roads, and blocks; based on addressing schemes around the world and coherent with human cognitive system. We will share and demonstrate the motivation and algorithm behind the scenes, then compare them to current open and industrial solutions, and walk through our open source code[4] to generate the addresses for a given bounding box.

Machine Learning with Earth Observation Imagery AWS Public Sector Summit 2018 - Washington, D.C. For just a moment, think of the immense amount of data generated by Earth-observing systems. The sheer volume often makes it impractical for humans alone to perform the analysis, and accordingly, many groups are turning to artificial intelligence (AI) and machine learning (ML) algorithms to support their analysis. We'll hear from Development Seed and EOS about how they are using AI and ML to unlock the power of this planetary-scale data that is becoming increasingly more accessible in the cloud. From open-source libraries and human-in-the loop initial processing passes, to fully automated pipelines, we'll examine the new capacity for analysis now possible with technology.

Mars

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

The 'Space Architects' of Mars | The Age of A.I. Life on Mars won't be how David Bowie pictured it. With the help of artificial intelligence, architects and scientists are painting a picture of what life in space could be like and how it will be possible. The Age of A.I. is a 8 part documentary series hosted by Robert Downey Jr. covering the ways Artificial Intelligence, Machine Learning and Neural Networks will change the world.

STEAM | Using AI to build our best possible future on Mars Astreia Founder Dr. Natalie Rens joins us to talk about Artificial Intelligence. We cover her plans to use AI here on Earth and how that AI can then assist on colonizing the Red Planet and beyond.

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