Computer Vision for the Solar Dynamics Observatory (SDO)

Abstract 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). Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Martens, P. C. H. [verfasserIn] Attrill, G. D. R. Davey, A. R. Engell, A. Farid, S. Grigis, P. C. Kasper, J. Korreck, K. Saar, S. H. Savcheva, A. Su, Y. Testa, P. Wills-Davey, M. Bernasconi, P. N. Raouafi, N.-E. Delouille, V. A. Hochedez, J. F. Cirtain, J. W. DeForest, C. E. Angryk, R. A. De Moortel, I. Wiegelmann, T. Georgoulis, M. K. McAteer, R. T. J. Timmons, R. P.

Format:	Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Instrumentation and data management Solar Dynamics Observatory

Anmerkung:	© The Author(s) 2011

Übergeordnetes Werk:	Enthalten in: Solar physics - Springer Netherlands, 1967, 275(2011), 1-2 vom: 07. Jan., Seite 79-113
Übergeordnetes Werk:	volume:275 ; year:2011 ; number:1-2 ; day:07 ; month:01 ; pages:79-113

Links:	Volltext

DOI / URN:	10.1007/s11207-010-9697-y

Katalog-ID:	OLC2033610055

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allfields	10.1007/s11207-010-9697-y doi (DE-627)OLC2033610055 (DE-He213)s11207-010-9697-y-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Martens, P. C. H. verfasserin aut Computer Vision for the Solar Dynamics Observatory (SDO) 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2011 Abstract 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). Instrumentation and data management Solar Dynamics Observatory Attrill, G. D. R. aut Davey, A. R. aut Engell, A. aut Farid, S. aut Grigis, P. C. aut Kasper, J. aut Korreck, K. aut Saar, S. H. aut Savcheva, A. aut Su, Y. aut Testa, P. aut Wills-Davey, M. aut Bernasconi, P. N. aut Raouafi, N.-E. aut Delouille, V. A. aut Hochedez, J. F. aut Cirtain, J. W. aut DeForest, C. E. aut Angryk, R. A. aut De Moortel, I. aut Wiegelmann, T. aut Georgoulis, M. K. aut McAteer, R. T. J. aut Timmons, R. P. aut Enthalten in Solar physics Springer Netherlands, 1967 275(2011), 1-2 vom: 07. Jan., Seite 79-113 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:275 year:2011 number:1-2 day:07 month:01 pages:79-113 https://doi.org/10.1007/s11207-010-9697-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 AR 275 2011 1-2 07 01 79-113
spelling	10.1007/s11207-010-9697-y doi (DE-627)OLC2033610055 (DE-He213)s11207-010-9697-y-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Martens, P. C. H. verfasserin aut Computer Vision for the Solar Dynamics Observatory (SDO) 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2011 Abstract 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). Instrumentation and data management Solar Dynamics Observatory Attrill, G. D. R. aut Davey, A. R. aut Engell, A. aut Farid, S. aut Grigis, P. C. aut Kasper, J. aut Korreck, K. aut Saar, S. H. aut Savcheva, A. aut Su, Y. aut Testa, P. aut Wills-Davey, M. aut Bernasconi, P. N. aut Raouafi, N.-E. aut Delouille, V. A. aut Hochedez, J. F. aut Cirtain, J. W. aut DeForest, C. E. aut Angryk, R. A. aut De Moortel, I. aut Wiegelmann, T. aut Georgoulis, M. K. aut McAteer, R. T. J. aut Timmons, R. P. aut Enthalten in Solar physics Springer Netherlands, 1967 275(2011), 1-2 vom: 07. Jan., Seite 79-113 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:275 year:2011 number:1-2 day:07 month:01 pages:79-113 https://doi.org/10.1007/s11207-010-9697-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 AR 275 2011 1-2 07 01 79-113
allfields_unstemmed	10.1007/s11207-010-9697-y doi (DE-627)OLC2033610055 (DE-He213)s11207-010-9697-y-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Martens, P. C. H. verfasserin aut Computer Vision for the Solar Dynamics Observatory (SDO) 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2011 Abstract 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). Instrumentation and data management Solar Dynamics Observatory Attrill, G. D. R. aut Davey, A. R. aut Engell, A. aut Farid, S. aut Grigis, P. C. aut Kasper, J. aut Korreck, K. aut Saar, S. H. aut Savcheva, A. aut Su, Y. aut Testa, P. aut Wills-Davey, M. aut Bernasconi, P. N. aut Raouafi, N.-E. aut Delouille, V. A. aut Hochedez, J. F. aut Cirtain, J. W. aut DeForest, C. E. aut Angryk, R. A. aut De Moortel, I. aut Wiegelmann, T. aut Georgoulis, M. K. aut McAteer, R. T. J. aut Timmons, R. P. aut Enthalten in Solar physics Springer Netherlands, 1967 275(2011), 1-2 vom: 07. Jan., Seite 79-113 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:275 year:2011 number:1-2 day:07 month:01 pages:79-113 https://doi.org/10.1007/s11207-010-9697-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 AR 275 2011 1-2 07 01 79-113
allfieldsGer	10.1007/s11207-010-9697-y doi (DE-627)OLC2033610055 (DE-He213)s11207-010-9697-y-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Martens, P. C. H. verfasserin aut Computer Vision for the Solar Dynamics Observatory (SDO) 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2011 Abstract 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). Instrumentation and data management Solar Dynamics Observatory Attrill, G. D. R. aut Davey, A. R. aut Engell, A. aut Farid, S. aut Grigis, P. C. aut Kasper, J. aut Korreck, K. aut Saar, S. H. aut Savcheva, A. aut Su, Y. aut Testa, P. aut Wills-Davey, M. aut Bernasconi, P. N. aut Raouafi, N.-E. aut Delouille, V. A. aut Hochedez, J. F. aut Cirtain, J. W. aut DeForest, C. E. aut Angryk, R. A. aut De Moortel, I. aut Wiegelmann, T. aut Georgoulis, M. K. aut McAteer, R. T. J. aut Timmons, R. P. aut Enthalten in Solar physics Springer Netherlands, 1967 275(2011), 1-2 vom: 07. Jan., Seite 79-113 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:275 year:2011 number:1-2 day:07 month:01 pages:79-113 https://doi.org/10.1007/s11207-010-9697-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 AR 275 2011 1-2 07 01 79-113
allfieldsSound	10.1007/s11207-010-9697-y doi (DE-627)OLC2033610055 (DE-He213)s11207-010-9697-y-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Martens, P. C. H. verfasserin aut Computer Vision for the Solar Dynamics Observatory (SDO) 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2011 Abstract 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). Instrumentation and data management Solar Dynamics Observatory Attrill, G. D. R. aut Davey, A. R. aut Engell, A. aut Farid, S. aut Grigis, P. C. aut Kasper, J. aut Korreck, K. aut Saar, S. H. aut Savcheva, A. aut Su, Y. aut Testa, P. aut Wills-Davey, M. aut Bernasconi, P. N. aut Raouafi, N.-E. aut Delouille, V. A. aut Hochedez, J. F. aut Cirtain, J. W. aut DeForest, C. E. aut Angryk, R. A. aut De Moortel, I. aut Wiegelmann, T. aut Georgoulis, M. K. aut McAteer, R. T. J. aut Timmons, R. P. aut Enthalten in Solar physics Springer Netherlands, 1967 275(2011), 1-2 vom: 07. Jan., Seite 79-113 (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:275 year:2011 number:1-2 day:07 month:01 pages:79-113 https://doi.org/10.1007/s11207-010-9697-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_40 GBV_ILN_47 GBV_ILN_70 AR 275 2011 1-2 07 01 79-113
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source	Enthalten in Solar physics 275(2011), 1-2 vom: 07. Jan., Seite 79-113 volume:275 year:2011 number:1-2 day:07 month:01 pages:79-113
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title	Computer Vision for the Solar Dynamics Observatory (SDO)
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title_sort	computer vision for the solar dynamics observatory (sdo)
title_auth	Computer Vision for the Solar Dynamics Observatory (SDO)
abstract	Abstract 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). © The Author(s) 2011
abstractGer	Abstract 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). © The Author(s) 2011
abstract_unstemmed	Abstract 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). © The Author(s) 2011
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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. 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Jan., Seite 79-113</subfield><subfield code="w">(DE-627)129856010</subfield><subfield code="w">(DE-600)281593-X</subfield><subfield code="w">(DE-576)015160033</subfield><subfield code="x">0038-0938</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:275</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:1-2</subfield><subfield code="g">day:07</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:79-113</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11207-010-9697-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-AST</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-AST</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_47</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">275</subfield><subfield code="j">2011</subfield><subfield code="e">1-2</subfield><subfield code="b">07</subfield><subfield code="c">01</subfield><subfield code="h">79-113</subfield></datafield></record></collection>
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