Automatic Detection and Characterization of EIT waves observed by AIA Data

Details Automatic Detection and Characterization of EIT waves observed by AIA Data Automatic Detection and Characterization of EIT waves observed by AIA Data

Dec 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011agufmsh13b1958c&link_type=abstract

American Geophysical Union, Fall Meeting 2011, abstract #SH13B-1958

Astronomy and Astrophysics

Astrophysics

[7509] Solar Physics, Astrophysics, And Astronomy / Corona, [7549] Solar Physics, Astrophysics, And Astronomy / Ultraviolet Emissions, [7594] Solar Physics, Astrophysics, And Astronomy / Instruments And Techniques

Scientific paper

EIT waves were first observed by SOHO-EIT in 1996. Many questions still remain about their relationship to other phenomena as such as CMEs, Moreton waves, and transverse coronal loop oscillations. This is likely due to the limitation of past observations (e.g. limited time cadence). With the new Atmospheric Imaging Assembly (AIA) onboard SDO, we now have access to an unprecedented uninterrupted data set with a full-Sun field of view, high dynamic range, and most importantly a high time cadence (~10 s). AIA observations have the potential to revolutionize the study of wave phenomena in the solar corona. In order to fully utilize the vast data archive of AIA, we are developing an automated algorithm to detect EIT waves. Such an algorithm would permit a standardized statistical analysis to be performed on these mysterious waves providing important constraints on models. We present and compare results using different numerical methods (e.g. wavelets, watershed) developed in Python, a general purpose scientific computing language widely used by multiple communities. We validate these algorithms against traditional human-powered methods. This analysis makes use of the SunPy python library.

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