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).
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.