Authors: J.M. Pap; M. Turmon, B. Tsurutani; R. Bogart; D. Judge
Affiliation: GEST/University of Maryland, Baltimore County; NASA Jet Propulsion
Laboratory; Stanford University; University of Southern California
Study of the Sun's variability has been of high interest for both astrophysics and solar-terrestrial physics. The Sun, a fairly typical star, has the special advantage of proximity which allows the detailed study of a variety of phenomena important for stellar physics. Also, the Sun is the fundamental source of energy that sustains life on Earth, establishes the Earth's radiative environment, and controls its temperature and atmospheric composition. Therefore, accurate knowledge of the solar radiation received by Earth, as well as an understanding of its variability, are critical for environmental science. Since irradiance monitoring experiments observe the Sun as a star, it is necessary to examine high-resolution spatially resolved data in order to account for the variations observed over the entire solar spectrum. For this purpose, automated image processing software, based on a Bayesian object classification technique, has been developed and applied to the SOHO/MDI images. In this paper we describe this analysis technique and demonstrate its usefulness when analyzing the SOHO/CELIAS/SEM EUV irradiance variations. These results are directly related to the SDO/HMI and EVE experiments, and both the image and data analysis techniques can be used when analyzing SDO data.