Youtube search...
...Google search
...News search
|
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.
|
|
|
|
|
|
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
Youtube search...
...Google search
...News search
|
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
Youtube search...
...Google search
...News search
|
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
Youtube search...
...Google search
...News search
|
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
Youtube search...
...Google search
...News search
|
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
Youtube search...
...Google search
...News search
|
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
Youtube search...
...Google search
...News search
|
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
Youtube search...
...Google search
...News search
Earth
Youtube search...
...Google search
...News search
Mars
Youtube search...
...Google search
...News search
Moon
Youtube search...
...Google search
...News search
Man-made (artificial) Satellites
Youtube search...
...Google search
...News search
Asteroids
Youtube search...
...Google search
...News search
Collision Avoidance in Space
Youtube search...
...Google search
...News search
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
Youtube search...
...Google search
...News search
- Astropy Project ...a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages.
Simulation
Youtube search...
...Google search
...News search
