The Feature Finding Team for the Solar Dynamics Observatory

The Solar Dynamics Observatory continually observes the Sun, producing a huge amount (1.5TB per day) of high quality data. In order to analyze the data near real time, NASA selected an international consortium (Feature Finding Team, FFT) to produce a comprehensive automated feature-recognition system for SDO data. The automated system is comprised of individual “modules” developed by the various institutions.

The images below are some examples of the different detection modules. Find the complete list on the FFT Module site menu or click the round buttons below to browse the detection modules.

Filament Detection Module

Filament detection module

The Advanced Automated Filament Detection and Characterization Code (AAFDCC) module was deployed on March 30, 2010 and is currently running on the SDO-FFT cluster at SAO. The AAFDCC (Bernasconi) provides automated tracking of the origin, evolution, and disappearance (eruption) of all Hα filaments. Outlines contours, determines chirality, tracks individual filaments, handles mergers and splitting.

CME detection module

CME finder module image

Active Region Detection Module

Active Region module SPoCA

Active region finder (SPoCA) module screenshot

The Spatial Possibilistic Clustering Algorithm (SPoCA) has been implemented to deliver modules detecting Active Regions (ARs) and Coronal Holes (CH)

SDO FFT Active region detection module video

The Spatial Possibilistic Clustering Algorithm (SPoCA) has been implemented to deliver modules detecting Active Regions (ARs) and Coronal Holes (CHs).

Coronal Hole Detection

Coronal Hole Detection Module SPoCA

Coronal Hole finder module

SDO FFT Coronal Hole detection module video

The Spatial Possibilistic Clustering Algorithm (SPoCA) has been implemented to deliver modules detecting Active Regions (ARs) and Coronal Holes (CHs).

Bright Point Finder module

Bright Point Finder module

Bright Point Detector module

SDO FFT Bright point detection module video

(Saar, Farid): This module detects EUV Bright Points from AIA images, and measures location, outline, size, and brightness. The figure shows detections in slight excess brightness, overlaid on the original AIA 193 ̊A image.

Coronal Dimming Region Detection

Dimming Region Detection Module

Coronal Dimmings Detector module

Coronal Dimmings Detector: Detects, tracks, and categorizes coronal dimmings and provides space weather alerts. The AIA 193 ̊A image from February 2nd 2011 shows an inset dimming region.

EIT (EUV) Wave detection module (CORPita)

“EIT” (EUV) Wave detection module (CORPita)

EIT EUV wave detection module

SDO FFT “EIT” (EUV) Wave detection module video

(Long): The EIT wave module (CORPita) is triggered by the flare module and uses AIA 193 ̊A difference images to identify and track propagating wave fronts as they emanate from the flare site. The module then determines and records the location, width, intensity, and kinematics of the pulse.

Flare Detection module

Flare Detective module

Flare Detective (flare detection) module

Stewardship of the Flare Detective Module was passed from Paolo Grigis to Henry Winter and Paola Testa at SAO. This module rebins image in macro-pixels and analyzes light curves in parallel. AIA 193 ̊A image, May 5th 2010. Produces flare alerts, flare class, light curves, DEMs, quicktime movies.

Polarity Inversion Line Detection module

Polarity Inversion Line detection module

polarity inversion line detection module

SDO FFT polarity inversion line (PIL) detection module movie

(Engell): This module finds and draws PILs from HMI magnetograms. The yellow lines represent the PILs. Red boxes delineate Active Regions. PILs with very large horizontal field gradients, prone to flaring, are drawn in red. The green is high gradient associated to potential field extrapolations.

Trainable Module

Trainable Module

SDO FFT Trainable Module

Trainable, multi-purpose detection module (Angryk, Banda, Martens, Schuh): This module can be trained by example to detect any type of solar event or phenomenon. The figure above shows a full-disk Hα image segmented into grid-based cells, with training examples on the left and detected events on the right. The cells in the left image are labeled: filament (yellow), no-filament (green), and discard (blue). The cells in the right image correspond to: true positive (green), false positive (blue), and false negative (red) detections. We find an initial overlap of over 80% compared to detections by the AAFDCC module, without any fine-tuning of the trainable module.

Jet Detection

(Savcheva): The code is designed to detect and measure jets in Coronal Holes. The AIA 193 ̊A figure shows that in its current state of development the jets are clearly brought out by edge enhancement techniques.

Non-Linear Force-Free Field (NLFFF) Extrapolation Tools

NLFFF extrapolation image

Non-Linear Force Free Field (NLFFF) extrapolation image

Non-Linear Force-Free Field Extrapolations (NLFFF; Wiegelmann): Shown are AIA images of AR11158, observed 2011-02- 14T20:34:00.34. Overplotted are the NLFFF extrapolated magnetic field lines extrapolated from an SDO/HMI vector-magnetogram.

Sunspot detection module

Sunspot detection and tracking

(Zharkova, Zharkov): Detection and tracking of sunspots, and measurement of their outline from HMI images in Fe I at 6173 ̊A. Red delineates the umbra, blue the penumbra.

Oscillations Detector Module

Oscillations Detector Module

(McAteer, Calabro, Pevtsov): This module measures the strength, period, and duration of oscillatory motions in any of the AIA channels using a wavelet technique. The module can be triggered by any event registered by other modules, such as flares, filament eruptions, CMEs, and jets. Shown is a 120 x120 arcsec window with areas of 3 minute (blue) periodicity (left) and 5 minute periodicity (red) oscillations with lifetimes (right) between 1.5 (blue) and 5 (red) full periods in a 1 hour time window.

Sigmoid Detection Module (Sigmoid Sniffer)

Sigmoid sniffer image

SDO FFT Sigmoid sniffer module movie

Sigmoid Sniffer (Georgoulis, Raouafi): Automatically detects and outlines X-ray sigmoids in the solar corona, determining their chirality (direct [S] or inverse [Z] sigmoids), aspect ratio, heliographic location, and host solar active region, if any. Operates in 94 ̊A (example shown) and 131 ̊A AIA images. The square encloses a detection with the actual sigmoid bounded by the blue contour, and the circle provides the detection limits on the solar disk.

Flux emergence and magnetic feature tracking (SWAMIS)

SWAMIS emerging flux image

SWAMIS emerging flux detection module movie

SWAMIS (Lamb, DeForest): Identifies all magnetic features on the Sun, their origin and demise, from HMI magnetograms. Swamis provides alerts for emerging flux. The white box in background image highlights the zoomed in area, which shows the poles of a detected emerging flux region as it grows for eight hours.

FFT at the 220th AAS Meeting Anchorage, AK

The SDO Feature Finding Team hosted a booth at the 220th AAS Meeting in Anchorage, AK June 10-14 2012. For a full presentation see FFT-Slide-Show.pptx Petrus Martens organized a complete session on Solar Information Processing and presented the FFT. See this link for more details. Petrus Martens also spoke about the FFT as a panel …

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The Sixth Solar Information Processing Workshop (SIPWork VI)

The Sixth Solar Information Processing Workshop (SIPWork VI) was held 13-16 August, 2012 at Montana State University, Bozeman. The focus of the workshop was “Challenges in Solar Image and Information Processing”. Science Organizing Committee: Petrus Martens, Montana State University Veronique Delouille, Royal Observatory of Belgium. Thierry Dudok de Wit, CNRS-Orleans. Jack Ireland, ADNET Systems, Inc./ …

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