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GTC ON-DEMAND

Presentation
Media
Abstract:

NASA's Heliophysics Division operates a fleet of spacecraft to monitor the Sun's activity and how its changes drive space weather. We show how science and mission capabilities can be enhanced by deep learning:
(a) mega-Kelvin thermometry of the Sun's corona by using a deep neural network (DNN) to solve a compressed sensing problem, and
(b) revival of a spectrograph by using convolutional neural networks (CNNs) to measure the Sun's extreme UV spectral irradiance. This work was done at NASA's Frontier Development Lab, a public-private partnership between NASA and industry partners (including the SETI Institute, NVIDIA, IBM, Lockheed Martin, Google, Intel & kx).

NASA's Heliophysics Division operates a fleet of spacecraft to monitor the Sun's activity and how its changes drive space weather. We show how science and mission capabilities can be enhanced by deep learning:
(a) mega-Kelvin thermometry of the Sun's corona by using a deep neural network (DNN) to solve a compressed sensing problem, and
(b) revival of a spectrograph by using convolutional neural networks (CNNs) to measure the Sun's extreme UV spectral irradiance. This work was done at NASA's Frontier Development Lab, a public-private partnership between NASA and industry partners (including the SETI Institute, NVIDIA, IBM, Lockheed Martin, Google, Intel & kx).

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Topics:
Accelerated Data Science, Deep Learning and AI, Computer Vision and Machine Vision
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8199
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Abstract:
NASA's heliophysics division operates a fleet of spacecraft, the so-called Heliophysics System Observatory, to monitor the Sun's activity and how its changes drive space weather in interplanetary space and in the near-Earth environment. We'll present case studies of how a number of challenging problems encountered in heliophysics can be tackled using deep learning: spectropolarimetric inversions for measuring the magnetic field on the solar surface, and mega-Kelvin thermometry of the Sun's corona by using a deep neural network to solve a compressed sensing problem. These low-cost solutions make possible new concepts for deep space missions for space weather monitoring. Some of the work in this presentation was made possible by NASA's Frontier Development Lab, a public-private partnership between the agency and industry partners (including the SETI Institute, NVIDIA, IBM, Intel, kx & Lockheed Martin), whose mission is to use artificial intelligence to tackle problems related to planetary defense and heliophysics.
NASA's heliophysics division operates a fleet of spacecraft, the so-called Heliophysics System Observatory, to monitor the Sun's activity and how its changes drive space weather in interplanetary space and in the near-Earth environment. We'll present case studies of how a number of challenging problems encountered in heliophysics can be tackled using deep learning: spectropolarimetric inversions for measuring the magnetic field on the solar surface, and mega-Kelvin thermometry of the Sun's corona by using a deep neural network to solve a compressed sensing problem. These low-cost solutions make possible new concepts for deep space missions for space weather monitoring. Some of the work in this presentation was made possible by NASA's Frontier Development Lab, a public-private partnership between the agency and industry partners (including the SETI Institute, NVIDIA, IBM, Intel, kx & Lockheed Martin), whose mission is to use artificial intelligence to tackle problems related to planetary defense and heliophysics.  Back
 
Topics:
AI Application Deployment and Inference, Accelerated Analytics, Astronomy and Astrophysics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8222
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Abstract:

Since its launch in 2010, NASA's Solar Dynamics Observatory (SDO) has continuously monitored the Sun's changes in magnetic activity. Both the Atmospheric Imaging Assembly (AIA) and Helioseismic & Magnetic Imager (HMI) instruments onboard SDO deliver 4096x4096 pixel images at a cadence of more than one image per second. Although SDO images are free from distortion by absorption and scattering in the Earth's atmosphere, images are still blurred by the intrinsic point spread functions of the telescopes. In this presentation, we show how the instrument teams have deployed CUDA-enabled GPUs to perform deconvolution of SDO images. The presentation will demonstrate how we leveraged cuFFT and Thrust to implement an efficient image processing pipeline.

Since its launch in 2010, NASA's Solar Dynamics Observatory (SDO) has continuously monitored the Sun's changes in magnetic activity. Both the Atmospheric Imaging Assembly (AIA) and Helioseismic & Magnetic Imager (HMI) instruments onboard SDO deliver 4096x4096 pixel images at a cadence of more than one image per second. Although SDO images are free from distortion by absorption and scattering in the Earth's atmosphere, images are still blurred by the intrinsic point spread functions of the telescopes. In this presentation, we show how the instrument teams have deployed CUDA-enabled GPUs to perform deconvolution of SDO images. The presentation will demonstrate how we leveraged cuFFT and Thrust to implement an efficient image processing pipeline.

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Topics:
Astronomy and Astrophysics, Video and Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5209
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Speakers:
Mark Cheung
- Lockheed Martin Solar & Astrophysics Laboratory
Abstract:
Learn how GPU computing is enabling astrophysicists to study our closest star. NASA''s recently launched Solar Dynamics Observatory is continuously streaming full-disk images of the Sun at visible, UV and EUV wavelengths. This presentation will discuss ways that GPU computing is helping scientists cope with the analysis of the immense data volumes as well as in numerical modeling of the Sun.
Learn how GPU computing is enabling astrophysicists to study our closest star. NASA''s recently launched Solar Dynamics Observatory is continuously streaming full-disk images of the Sun at visible, UV and EUV wavelengths. This presentation will discuss ways that GPU computing is helping scientists cope with the analysis of the immense data volumes as well as in numerical modeling of the Sun.  Back
 
Topics:
Astronomy and Astrophysics, Computational Fluid Dynamics, Computer Vision and Machine Vision, Physics Simulation
Type:
Talk
Event:
GTC Silicon Valley
Year:
2010
Session ID:
S102178
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Speakers:
Mark Cheung
 
Topics:
Developer - Tools & Libraries
Type:
Poster
Event:
GTC Silicon Valley
Year:
2009
Session ID:
P0989
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