Parametrization of Sunspot Groups Based on Machine-Learning Approach

Sunspot groups observed in white light appear as complex structures. Analysis of these structures is usually based on simple morphological descriptors that only capture generic properties and miss information about fine details. We present a machine-learning approach to introduce a complete yet compact description of sunspot groups. The idea is to map sunspot-group images into an appropriate lower-dimensional (latent) space. We apply a combination of Variational Autoencoder and Principal Component Analysis to obtain a set of 285 latent descriptors. We demonstrate that the standard descriptors are embedded into the latent ones. Thus, latent features can be considered as an extended description of sunspot groups and, in our opinion, can expand the possibilities for research on sunspot groups. In particular, we demonstrate an application for the estimation of the sunspot-group complexity. The proposed parametrization model is generic and can be applied to investigation of other traces of solar activity observed in various spectral lines.