Other
Scientific paper
May 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009spd....40.1517i&link_type=abstract
American Astronomical Society, SPD meeting #40, #15.17; Bulletin of the American Astronomical Society, Vol. 41, p.838
Other
Scientific paper
Recently observed oscillations in the solar atmosphere have been interpreted and modeled as magnetohydrodynamic wave modes. This has allowed the estimation of parameters that are otherwise hard to derive, such as the coronal magnetic field strength. This work crucially relies on the initial detection of the waves, which is commonly done manually. The volume of Solar Dynamics Observatory (SDO) data will make manual detection inefficient for detecting all the oscillating regions. An algorithm is presented which automates the detection of areas of the solar surface that support spatially extended oscillations. The method is based on Bayesian spectral analysis of time series and image filtering. A Bayesian approach sidesteps the need for an a priori noise estimate to calculate rejection criteria for the observed signal, and also provides estimates of oscillation frequency, amplitude and noise, and the error in all these quantities, in a self-consistent way. The algorithm also introduces the notion of quality measures to those regions for which a positive detection is claimed, allowing simple post-detection discrimination by the user. The algorithm is demonstrated on Transition Region and Coronal Explorer (TRACE) datasets, and comments regarding its suitability for oscillation detection in SDO are made.
Ireland Jack
Kucera Therese A.
Marsh Michael S.
Young Alex
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