Automatic Detection and Extraction of Coronal Dimmings from SDO/AIA Data
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The 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 recognized in the 1970s, it is now well established that coronal dimmings are closely associated with coronal mass ejections (CMEs), and they are particularly noted as a reliable indicator of front-side (halo) CMEs, which can be difficult to detect in white-light coronagraph data.
Existing work clearly demonstrates that several properties derived from the analysis of coronal dimmings can give useful information about the associated CME. The development and implementation of an automated coronal-dimming region detection and extraction algorithm removes visual observer bias, however unintentional, from the determination of physical quantities such as spatial location, area, and volume. This allows for reproducible, quantifiable results to be mined from very large data sets. 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 geo-effectiveness of CMEs, coupled with analysis of their associated coronal dimming signatures.
In this paper we present examples of both simple and complex dimming events extracted using our algorithm, which will be run as a module for the SDO/Computer Vision Centre. Contrasting and well-studied events at both the minimum and maximum of solar cycle 23 are identified in Solar and Heliospheric Observatory/Extreme ultra-violet Imaging Telescope (SOHO/EIT) data. A more recent example extracted from Solar and Terrestrial Relations Observatory/Extreme Ultra-Violet Imager (STEREO/EUVI) data is also presented, demonstrating the potential for the anticipated application to SDO/AIA data. The detection part of our algorithm is based largely on the principle of operation of the NEMO software, namely the detection of significant variation in the statistics of the EUV image pixels (Podladchikova and Berghmans in Solar Phys. 228, 265 – 284, 2005). As well as running on historic data sets, the presented algorithm is capable of detecting and extracting coronal dimmings in near real-time.
KeywordsCorona, quiet Coronal mass ejections, low coronal signatures Instrumentation and data management
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