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Automatic Detection and Extraction of Coronal Dimmings from SDO/AIA Data View all abstracts by submitter

Gemma Attrill   Submitted: 2009-08-18 07:40

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 recognised in the 1970s it is now well established that coronal dimmings are closely associated with coronal mass ejections (CMEs), and 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 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 determination of physical quantities such as spatial location, area, and volume. This allows for 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 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 detection of significant variation in the statistics of the EUV image pixels (Podladchikova & Berghmans, 2005). As well as running on historic datasets, the presented algorithm is capable of detecting and extracting coronal dimmings in near real-time.

Authors: G. D. R. Attrill and M. J. Wills-Davey

Publication Status: Solar Physics (in press)
Last Modified: 2009-08-18 08:39
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