The application of Gaussian Mixture and Histogram-based Bayesian methods to NSO/Kitt Peak VT data.

Details The application of Gaussian Mixture and Histogram-based Bayesian methods to NSO/Kitt Peak VT data. The application of Gaussian Mixture and Histogram-based Bayesian methods to NSO/Kitt Peak VT data.

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufmsh13a1519m&link_type=abstract

American Geophysical Union, Fall Meeting 2008, abstract #SH13A-1519

Mathematics

Logic

7524 Magnetic Fields, 7536 Solar Activity Cycle (2162)

Scientific paper

We applied Gaussian Mixture and Histogram-based Bayesian methods to recognize several solar features using Kitt Peak Vacuum Telescope (VT) observations from 1992-2003. We used 5D observations in the 868.8 nm line including LoS magnetic field, continuum intensity, radial velocity, line depth, and EqW. We applied the analysis for recognition of active regions, magnetic network, and sunspots, for the purpose of automatic recognition of solar activity, and linking solar activity to irradiance changes. The success of such a feature recognition process strongly depends on separation and sensitivity of observable and derivative parameters for different features. For some features it works very well for two kind of data, but in some other cases the probability of correct recognition of a feature is low without the adding complementary data. We discuss the advantages and limitations of these statistical methods, review the importance and possibility of using the complementary data, and compare our results with other methods which derive feature areas. This methodological review will help to create the strategy for new SDO/HMI analysis.

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