Statistical Pattern Recognition for Labeling Solar Active Regions: Application to SOHO/MDI Imagery

This paper presents a new application of statistical methods for identifying the various surface structures on the Sun that may contribute to observed changes in total and spectral solar irradiance. These structures are divided for our purposes into three types: quiet Sun, faculae, and sunspots (umbra and penumbra). Each region type is characterized by the observed data present at pixels of that type. Statistical models characterizing these observables are found from expert identification of a sample set of regions or unsupervised clustering. Information about the spatial continuity of regions is incorporated into the model via a prior distribution on the label image; the contribution of the prior can be interpreted as a regularizing term. Once the parameters defining the models are fixed, the inference procedure becomes to maximize the probability of an image labeling given the observed data. This allows objective and automated classification of a large set of images. We describe the application of these procedures to computing labelings from synchronized full-disk high-resolution magnetic-field and light-intensity maps from the Michelson Doppler Imager experiment on the Solar and Heliospheric Observatory.