Back to Search
Start Over
Computer Vision for The Solar Dynamics Observatory
- Source :
- Solar Physics, Solar Physics, 2012, 275 (1-2), pp.79-113. ⟨10.1007/s11207-010-9697-y⟩, Solar Physics, Springer Verlag, 2012, 275 (1-2), pp.79-113. ⟨10.1007/s11207-010-9697-y⟩
- Publication Year :
- 2012
- Publisher :
- HAL CCSD, 2012.
-
Abstract
- International audience; In Fall 2008 NASA selected a large international consortium to produce a comprehensive automated feature-recognition system for the Solar Dynamics Observatory (SDO). The SDO data that we consider are all of the Atmospheric Imaging Assembly (AIA) images plus surface magnetic-field images from the Helioseismic and Magnetic Imager (HMI). We produce robust, very efficient, professionally coded software modules that can keep up with the SDO data stream and detect, trace, and analyze numerous phenomena, including flares, sigmoids, filaments, coronal dimmings, polarity inversion lines, sunspots, X-ray bright points, active regions, coronal holes, EIT waves, coronal mass ejections (CMEs), coronal oscillations, and jets. We also track the emergence and evolution of magnetic elements down to the smallest detectable features and will provide at least four full-disk, nonlinear, force-free magnetic field extrapolations per day. The detection of CMEs and filaments is accomplished with Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph (LASCO) and ground-based Hα data, respectively. A completely new software element is a trainable feature-detection module based on a generalized image-classification algorithm. Such a trainable module can be used to find features that have not yet been discovered (as, for example, sigmoids were in the pre-Yohkoh era). Our codes will produce entries in the Heliophysics Events Knowledgebase (HEK) as well as produce complete catalogs for results that are too numerous for inclusion in the HEK, such as the X-ray bright-point metadata. This will permit users to locate data on individual events as well as carry out statistical studies on large numbers of events, using the interface provided by the Virtual Solar Observatory. The operations concept for our computer vision system is that the data will be analyzed in near real time as soon as they arrive at the SDO Joint Science Operations Center and have undergone basic processing. This will allow the system to produce timely space-weather alerts and to guide the selection and production of quicklook images and movies, in addition to its prime mission of enabling solar science. We briefly describe the complex and unique data-processing pipeline, consisting of the hardware and control software required to handle the SDO data stream and accommodate the computer-vision modules, which has been set up at the Lockheed-Martin Space Astrophysics Laboratory (LMSAL), with an identical copy at the Smithsonian Astrophysical Observatory (SAO).
- Subjects :
- 010504 meteorology & atmospheric sciences
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Coronal hole
01 natural sciences
Software
Heliophysics
Observatory
0103 physical sciences
Coronal mass ejection
Astrophysics::Solar and Stellar Astrophysics
Computer vision
010303 astronomy & astrophysics
0105 earth and related environmental sciences
Physics
Sunspot
Solar observatory
business.industry
[SDU.ASTR.SR]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR]
Astronomy
Astronomy and Astrophysics
[PHYS.ASTR.SR]Physics [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR]
13. Climate action
Space and Planetary Science
Physics::Space Physics
Large Angle and Spectrometric Coronagraph
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 00380938 and 1573093X
- Database :
- OpenAIRE
- Journal :
- Solar Physics, Solar Physics, 2012, 275 (1-2), pp.79-113. ⟨10.1007/s11207-010-9697-y⟩, Solar Physics, Springer Verlag, 2012, 275 (1-2), pp.79-113. ⟨10.1007/s11207-010-9697-y⟩
- Accession number :
- edsair.doi.dedup.....6cb67683db608ef42f4bbbc8b967e32b
- Full Text :
- https://doi.org/10.1007/s11207-010-9697-y⟩