Automated Detection and Extraction of Coronal Dimmings from SDO/AIA Data

Details Automated Detection and Extraction of Coronal Dimmings from SDO/AIA Data Automated Detection and Extraction of Coronal Dimmings from SDO/AIA Data

May 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21640212d&link_type=abstract

American Astronomical Society, AAS Meeting #216, #402.12; Bulletin of the American Astronomical Society, Vol. 41, p.875

Astronomy and Astrophysics

Astronomy

Scientific paper

The sheer volume of data anticipated from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) highlights the necessity for the development of automatic detection methods for various types of solar activity. Initially recognised in the 1970s, it is now well established that coronal dimmings are closely associated with coronal mass ejections (CMEs), and are particularly recognised as an indicator of front-side (halo) CMEs, which can be difficult to detect in white-light coronagraph data.
An automated coronal dimming region detection and extraction algorithm removes visual observer bias from determination of physical quantities such as spatial location, area and volume. This allows reproducible, quantifiable results to be mined from very large datasets. The information derived may facilitate more reliable early space weather detection, as well as offering the potential for conducting large-sample studies focused on determining the geoeffectiveness of CMEs, coupled with analysis of their associated coronal dimmings.
We present examples of dimming events extracted using our algorithm from existing EUV data, demonstrating the potential for the anticipated application to SDO/AIA data. Metadata returned by our algorithm include: location, area, volume, mass and dynamics of coronal dimmings. As well as running on historic datasets, this algorithm is capable of detecting and extracting coronal dimmings in near real-time. The coronal dimming detection and extraction algorithm described in this poster is part of the SDO/Computer Vision Center effort hosted at SAO (Martens et al., 2009).
We acknowledge NASA grant NNH07AB97C.

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