Comparison Of Solar Surface Features Identified By The Autoclass Pattern Recognition Software From Mount Wilson Observatory Data To Solar Surface Feature Areas Measured By The San Fernando Observatory

Details Comparison Of Solar Surface Features Identified By The Autoclass Pattern Recognition Software From Mount Wilson Observatory Data To Solar Surface Feature Areas Measured By The San Fernando Observatory Comparison Of Solar Surface Features Identified By The Autoclass Pattern Recognition Software From Mount Wilson Observatory Data To Solar Surface Feature Areas Measured By The San Fernando Observatory

May 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009spd....40.1607p&link_type=abstract

American Astronomical Society, SPD meeting #40, #16.07; Bulletin of the American Astronomical Society, Vol. 41, p.840

Astronomy and Astrophysics

Astronomy

Scientific paper

In previous work, the AutoClass software, a Bayesian pattern recognition program based on a finite mixture model, developed by Cheeseman and Stutz (1996), has been used on Mount Wilson Solar Observatory (MWO) intensity and magnetogram images to identify spatially resolved areas on the solar surface associated with Total Solar Irradiance (TSI) and to classify the identified areas in terms of traditional categories-spot, plage, quiet, etc. Those results, were in turn used to (1) model TSI variations as measured by satellite and composite TSI observations, with a correlation of better than 0.96, for the period 1996-2008-most of Cycle 23, and (2) create solar images as they would be seen by a hypothetical TSI instrument able to capture resolved images. Here, we compare the same regions identified by AutoClass which were found to be associated with TSI, and the indices derived from them, with the following areas measured by the San Fernando Observatory (SFO): (1) sunspot area in red continuum; (2) facular area in red continuum; (3) sunspot area in wide Ca K-line (WK-line); (4) plage area in WK-line; and (5) plage plus network area in WK-line. The correlations of the AutoClass-MWO indices with the different SFO area measurements varies from better than 0.91 to over 0.98, depending on the type of feature. The comparison of the spatially resolved surface areas identified by AutoClass in the MWO images to the areas of the different feature observed at SFO, and the creation of spatially resolved images depicting those areas, should enable better identification of the types of surface features associated with TSI measurements and their evolution over a solar cycle. The comparison should also assist in validating the automated categorization of solar features found using the AutoClass automated pattern recognition software.

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