Automated detection of oscillations in extreme ultraviolet imaging data

Details Automated detection of oscillations in extreme ultraviolet imaging data Automated detection of oscillations in extreme ultraviolet imaging data

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufmsh13a1507i&link_type=abstract

American Geophysical Union, Fall Meeting 2008, abstract #SH13A-1507

Mathematics

Probability

7509 Corona, 7524 Magnetic Fields, 7594 Instruments And Techniques, 7836 Mhd Waves And Instabilities (2149, 2752, 6050)

Scientific paper

The corona is now known to support many different types of oscillation. Initial detection of these oscillations currently relied on manual labor. With the advent of much higher cadence EUV (extreme ultraviolet) data at better spatial resolution, sifting through the data manually to look for oscillatory material becomes an onerous task. Further, different observers tend to see different behavior in the data. To overcome these problems, we introduce a Bayesian probability-based automated method to detect areas in EUV images that support oscillations. The method is fast and can handle time series data with even or uneven cadences. Interestingly, the Bayesian approach allows us to generate a probability that a given frequency is present without the need for an estimate of the noise in the data. We also generate simple and intuitive "quality measures" for each detected oscillation. This will allow users to select the "best" examples in a given dataset automatically. The method is demonstrated on existing datasets (EIT, TRACE, STEREO). Its application to Solar Dynamics Observatory data is also discussed. We also discuss some of the problems in detecting oscillations in the presence of a significant background trend which can pollute the frequency spectrum.

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