Speaker
Description
Context. The solar and stellar magnetic activity can cause spots and faculae on the photosphere that imprints variability signals on its brightness. Many different approaches have been proposed in the literature to reconstruct the signals of magnetic activity on the stellar surface from the brightness measurements, such as Doppler imaging, photometric surface mapping, and planetary transit mapping. Gaussian Process frameworks have been proposed as statistical models for solar and stellar variability, primarily as tools to aid in the mitigation of their contamination on Sun-as-a-star and stellar photometric and spectroscopic observations.
Aims. We aim to model the long-term modulation in brightness of the Sun and magnetic active stars, to derive quantitative physical properties for the surface features such as spots and faculae.
Methods. In this work, we applied a physics-based Gaussian Process framework called FENRIR to model solar and stellar activity as a stochastic process, approximated by a Gaussian distribution. We derive marginalized distributions for physical parameters, like: rotational period, latitude, and lifetime of the spots and faculae, and the solar magnetic cycle.
Results. We applied our method to total solar irradiance data from unresolved solar observations from the SORCE/TIM satellite and to stellar photometric observations from the CoRoT space mission as a proof of concept for “statistical photometric surface mapping”. This approach provides reasonable average values for the properties of solar and stellar starspots and faculae. In particular, by using only the brightness variation of the targets, we derived the rotational period at the average latitude of the spots, and managed to obtain periods in accordance with the literature for the Sun and the star CoRoT-2.