Automated Detection/Characterization of EUV Waves in SDO/AIA Data
Abstract
Although EUV waves in the solar corona (also called coronal bright fronts or "EIT waves") were first observed in 1996, many questions still remain about their nature and their association with other phenomena such as flares, CMEs, and Moreton waves. The high-resolution, high-cadence data from the Atmospheric Imaging Assembly (AIA) instrument on the Solar Dynamics Observatory (SDO) allows for unprecedented analysis of the kinematics and morphology of EUV waves. This information can be crucial for constraining and differentiating between theoretical models. While this analysis can be performed "by hand", the large volume of AIA data is well-suited for automated algorithms to detect and characterize these waves. We are developing such algorithms, which will generate a comprehensive catalog that can be used for statistical studies, and the biases of the algorithms can be well-studied using simulated data. We take advantage of imaging processing methods developed in Python, a general-purpose scientific computing language widely used used by multiple communities, as well as the SunPy Python library. We compare the results of our automated algorithms with other efforts that use more traditional, human-powered methods to identify and characterize EUV waves.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2012
- Bibcode:
- 2012AGUFMSH11C..08S
- Keywords:
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- 7509 SOLAR PHYSICS;
- ASTROPHYSICS;
- AND ASTRONOMY / Corona;
- 7549 SOLAR PHYSICS;
- ASTROPHYSICS;
- AND ASTRONOMY / Ultraviolet emissions;
- 7594 SOLAR PHYSICS;
- ASTROPHYSICS;
- AND ASTRONOMY / Instruments and techniques