  Sunspot groups observed in white-light appear as complex structures. Analysis
of these structures is usually based on simple morphological descriptors which
capture only 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 the research on sunspot groups. In particular, we demonstrate an
application for 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 spectrum lines. Key components of
this work, which are the parametrization model, dataset of sunspot groups and
latent vectors, are available in the public GitHub repository
github.com/observethesun/sunspot_groups and can be used to reproduce the
results and for further research.
