Parametrization of sunspot groups based on machine learning approach

TL;DR

This paper introduces a machine learning-based method using Variational Autoencoders and PCA to create a compact, detailed parametrization of sunspot groups, enhancing analysis capabilities beyond traditional descriptors.

Contribution

It presents a novel approach combining VAE and PCA to generate a comprehensive set of latent descriptors for sunspot groups, capturing both standard and fine details.

Findings

Latent descriptors embed standard morphological features.

The model can estimate sunspot group complexity.

The approach is applicable to other solar activity data.

Abstract

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.