{"id":2153,"date":"2022-06-08T13:40:06","date_gmt":"2022-06-08T13:40:06","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/06\/08\/stunning-insights-from-james-webb-space-telescope-are-coming-thanks-to-gpu-powered-deep-learning\/"},"modified":"2022-06-08T13:40:06","modified_gmt":"2022-06-08T13:40:06","slug":"stunning-insights-from-james-webb-space-telescope-are-coming-thanks-to-gpu-powered-deep-learning","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/06\/08\/stunning-insights-from-james-webb-space-telescope-are-coming-thanks-to-gpu-powered-deep-learning\/","title":{"rendered":"Stunning Insights from James Webb Space Telescope Are Coming, Thanks to GPU-Powered Deep Learning"},"content":{"rendered":"
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NVIDIA GPUs will play a key role interpreting data streaming in from the James Webb Space Telescope, with NASA preparing to release next month the first full-color images from the $10 billion scientific instrument.<\/p>\n

The telescope\u2019s iconic array of 18 interlocking hexagonal mirrors, which span a total of 21 feet 4 inches, will be able to peer far deeper into the universe, and deeper into the universe\u2019s past, than any tool to date, unlocking discoveries for years to come.<\/p>\n

GPU-powered deep learning will play a key role in several of the highest-profile efforts to process data from the revolutionary telescope positioned a million miles away from Earth, explains UC Santa Cruz Astronomy and Astrophysics Professor Brant Robertson.<\/p>\n

\u201cThe JWST will really enable us to see the universe in a new way that we\u2019ve never seen before,\u201d said Robertson, who is playing a leading role in efforts to use AI to take advantage of the unprecedented opportunities JWST creates. \u201cSo it\u2019s really exciting.\u201d<\/p>\n

High-Stakes Science<\/b><\/h2>\n

Late last year, Robertson was among the millions tensely following the runup to the launch of the telescope, developed over the course of three decades, and loaded with instruments that define the leading edge of science.<\/p>\n

The JWST\u2019s Christmas Day launch went better than planned, allowing the telescope to slide into a LaGrange point \u2014 a kind of gravitational eddy in space that allows an object to \u201cpark\u201d indefinitely \u2014 and extending the telescope\u2019s usable life to more than 10 years.<\/p>\n

\u201cIt\u2019s working fantastically,\u201d Robertson reports. \u201cAll of the signs are it\u2019s going to be a tremendous facility for science.\u201d<\/p>\n

AI Powering New Discoveries<\/b><\/h2>\n

Robertson \u2014 who leads the computational astrophysics group at UC Santa Cruz \u2014 is among a new generation of scientists across a growing array of disciplines using AI to quickly classify the vast quantities of data \u2014 often more than can be sifted in a human lifetime \u2014 streaming in from the latest generation of scientific instruments.<\/p>\n

\u201cWhat\u2019s great about AI and machine learning is that you can train a model to actually make those decisions for you in a way that is less hands-on and more based on a set of metrics that you define,\u201d Robertson said.<\/p>\n

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Simulated image of a portion of the JADES galaxy survey, part of the preparations for galaxy surveys using JWST UCSC astronomer Brant Robertson and his team have been working on for years. (Image credit: JADES Collaboration)<\/figcaption><\/figure>\n

Working with Ryan Hausen, a Ph.D. student in UC Santa Cruz\u2019s computer science department, Robertson helped create a deep learning framework that classifies astronomical objects, such as galaxies, based on the raw data streaming out of telescopes on a pixel by pixel basis, which they called Morpheus.<\/p>\n

It quickly became a key tool for classifying images from the Hubble Space Telescope. Since then the team working on Morpheus has grown considerably, to roughly a half-dozen people at UC Santa Cruz.<\/p>\n

Researchers are able to use NVIDIA GPUs to accelerate Morpheus across a variety of platforms \u2014 from an NVIDIA DGX Station<\/a> desktop AI system to a small computing cluster equipped with several dozen NVIDIA V100 Tensor Core GPUs to sophisticated simulations runs thousands of GPUs on the Summit supercomputer at Oak Ridge National Laboratory.<\/p>\n

A Trio of High-Profile Projects<\/b><\/h2>\n

Now, with the first science data from the JWST due for release July 12, much more\u2019s coming.<\/p>\n

\u201cWe\u2019ll be applying that same framework to all of the major extragalactic JWST surveys that will be conducted in the first year,\u201d Robertson.<\/p>\n

Robertson is among a team of nearly 50 researchers who will be mapping the earliest structure of the universe through the COSMOS-Webb program, the largest general observer program selected for JWST\u2019s first year.<\/p>\n

\n

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Simulations by UCSC researchers showed how JWST can be used to map the distribution of galaxies in the early universe. The web-like structure in the background of this image is dark matter, and the yellow dots are galaxies that should be detected in the survey. (Image credit: Nicole Drakos)<\/figcaption><\/figure>\n

Over the course of more than 200 hours, the COSMOS-Webb program will survey half a million galaxies with multiband, high-resolution, near-infrared imaging and an unprecedented 32,000 galaxies in mid-infrared.<\/p>\n

\u201cThe COSMOS-Webb project is the largest contiguous area survey that will be executed with JWST for the foreseeable future,\u201d Robertson said.<\/p>\n

Robertson also serves on the steering committee for the JWST Advanced Deep Extragalactic Survey, or JADES, to produce infrared imaging and spectroscopy of unprecedented depth. Robertson and his team will put Morpheus to work classifying the survey\u2019s findings.<\/p>\n

Robertson and his team are also involved with another survey, dubbed PRIMER, to bring AI and machine learning classification capabilities to the effort.<\/p>\n

From Studying the Stars to Studying Ourselves<\/b><\/h2>\n

All these efforts promise to help humanity survey \u2014 and understand \u2014 far more of our universe than ever before. But perhaps the most surprising application Robertson has found for Morpheus is here at home.<\/p>\n

\u201cWe\u2019ve actually trained Morpheus to go back into satellite data and automatically count up how much sea ice is present in the North Atlantic over time,\u201d Robertson said, adding it could help scientists better understand and model climate change.<\/p>\n

As a result, a tool developed to help us better understand the history of our universe may soon help us better predict the future of our own small place in it.<\/p>\n

FEATURED IMAGE CREDIT: NASA<\/h5>\n

\u00a0<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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