Modeling Total Solar Irradiance Variations Using Automated Classification Software on Mount Wilson Data

Astronomy and Astrophysics – Astronomy

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Scientific paper

We present the results of using the AutoClass software, a Bayesian finite mixture model based pattern recognition program developed by Cheeseman and Stutz(1996), 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). Using indices based on the resolved solar surface patterns identified by AutoClass from MWO magnetogram and intensity ratio images, and a linear regression fit of those indices to satellite observations of TSI from the Virgo satellite, we are able to model the TSI data from the MWO images with a correlation of better than 0.96 for the period 1996 to 2007. The association of the spatially resolved surface patterns identified by AutoClass with the indices developed from them also allows construction of spatially resolved images of the Sun as it would be "seen" by TSI measuring instruments like Virgo if they were able to capture resolved images. The spatial resolution of these "images" should assist in identifying with greater accuracy the particular solar surface regions associated with TSI variations. Also, since the particular set of MWO data on which this analysis is based is available on a daily basis back to at least 1985, and on an intermittent basis before then, it may be possible to construct an independent estimate of TSI emission at several solar minima.

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