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Computer Vision for Solar Physics SDO Science Workshop, May 2011 Computer Vision for Solar Physics Piet Martens Montana State University Center for Astrophysics

Computer Vision for Solar Physics

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Computer Vision for Solar Physics. Piet Martens Montana State University Center for Astrophysics. The Peta -byte Challenge. SDO Feature Finding Team. International team of solar scientists, computer scientists, and expert programmers. Module Homes - PowerPoint PPT Presentation

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Page 1: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

Computer Vision for Solar Physics

Piet Martens Montana State UniversityCenter for Astrophysics

Page 2: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

The Peta-byte Challenge

Page 3: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

SDO Feature Finding Team

Module HomesHarvard-Smithsonian: Flare Detective, Dimming Detector, Bright Point Detector,EIT Waves, Polarity Inversion Line Mapping MSU: Trainable ModuleJohns Hopkins-APL: Filament DetectionBoston University: Coronal JetsSwRI: SWAMIS: Magnetic Feature Tracking, Emerging Flux, Sunspots. CMEsRoyal Observatory of Belgium: SpoCa: Active Regions, Coronal HolesNew Mexico State: OscillationsAcademy of Athens: SigmoidsMax Planck Lindau: NLFFF Extrapolations

International team of solar scientists, computer scientists, and expert programmers.

Page 4: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

Automated tracking of the origin, evolution, and disappearance (eruption) of all filaments. Outlines contours, determines chirality, tracks individual filaments, handles mergers and splitting.

)

Filament Tracking (Bernasconi)

Page 5: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

Jet Detection in Coronal Holes (Savcheva)

Jet detection in AIA 193 image; polar coronal hole, close to south pole.

Office 2004 Test Drive User
Page 6: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

Sigmoid Sniffer (Raouafi, Georgoulis)

Sigmoids detected with the sigmoid sniffer in a Hinode/XRT image (left) and AIA(right, 94 A). The sigmoid sniffer is set up for both XRT and AIA images.

Page 7: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

Bright Point Detector (Saar, Farid)

Bright Point Detector applied to AIA 193 image. Intensity scaling is logarithmic, detected BPs have been overlayed. Daily summary to HEK, full output in separate catalog.

Page 8: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

Coronal Dimmings (MWD)

• Dimming is seen as a decrease in intensity in both EUV and X-ray images (e.g., Thompson et al., 1998; Sterling & Hudson, 1997).

• Good correlation with CME events: will serve as SDO CME alert• Plasma from dimmings makes up (at least part of) the CME mass

Coronal dimming at flux rope footpoints

Coronal dimmings/TCHs

(EIT)

• First space-based observation by Skylab mission (1973-74): “Transient Coronal Holes (TCHs)”

POSSIBLE CAUSES:1. Density depletion due to an evacuation of plasma

along “opened” field lines2. Temperature variation

Module developed by Gemma Attrill, Alisdair Davey and Meredith Wills-Davey (SAO). Installed in pipeline, needs calibration.

Page 9: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

What would one use this for?

From the FFT produced metadata, a user can produce with a few IDL line commands information that previously would have taken years to compile, e.g.:

• Draw a butterfly diagram for active regions• Find all filaments that coincide with sigmoids. Correlate sigmoid handedness with filament chirality• Correlate EUV jets with small scale flux emergence in coronal holes only• Draw PIL maps with regions of high shear and large magnetic field gradients overlaid, to pinpoint potential flaring regions. Then correlate with actual flare occurrence.• Produce real time automated space weather alerts and Quicklook data for flares, CME’s, and flux emergence

Page 10: Computer Vision for Solar Physics

Computer Vision for Solar PhysicsSDO Science Workshop, May 2011

SDO Feature Finding Team

Team CompositionMSU: Martens (PI), Angryk, Banda*, Schuh*, Atanu*, Atreides* (trainable module)Harvard-Smithsonian: Kasper (PM), Davey (pipeline, interfaces, hardware),

Korreck (documentation, outreach), Grigis, Testa (flares), Saar, Farid* (XRBP’s), Engell* (PILs)

Lockheed-Martin: Timmons (pipeline, interfaces, HEK), HurlburtJohns Hopkins-APL: Bernasconi (filaments), Raouafi (sigmoids)NASA-Marshall: Cirtain (pipeline, interfaces)Boston University: Savcheva (jets)SwRI: DeForest, Lamb* (magnetic feature tracking, sunspots, CMEs), Wills-Davey

(dimmings, EIT Waves)Royal Observatory of Belgium: Delouille, Mampaey, Verbeek (Spoca: AR, CHs)New Mexico State: McAteer (oscillations)Academy of Athens: Georgoulis (sigmoids, filaments)Max Planck Lindau: Wiegelmann (full disk NLFFF extrapolations)

International team of solar scientists, computer scientists, and expert programmers.