Bayesian Inference for Identifying Solar Active Regions

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The solar chromosphere consists of three classes -- plage, network, background -- which contribute differently to ultraviolet radiation reaching the earth. Solar physicists are interested in relating plage area and intensity to UV irradiance, as well as understanding the spatial and temporal evolution of plage shapes. We describe a data set of solar images, means of segmenting the images into constituent classes, and a novel high-level representation for compact objects based on a spatial `membership function' defined via a triangulated planar graph. Segmentations are found using a discrete Markov random field setup, and the high-level representations are learned by a Markov chain Monte Carlo birth/death process on the triangulations.