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Catalogs of the Zurich Observatory contain positional information on sunspots, prominences, and faculae in the late 19th and early 20th centuries. This database is given in handwritten tabular form and was not systematically analyzed earlier. It is different from the sunspot-number time series made in Zurich and was obtained with a larger telescope. We trained a neural-network model for handwritten text recognition and present the database of reconstructed coordinates. The database obtained connects the earlier observations by Spörer with later programs of the 20th century and supplements the sunspot-group catalogs of the Royal Greenwich Observatory. We also expect that the presented machine-learning approach and its deep capabilities will motivate the processing of a wide bulk of astronomical data, which is still given in nondigitized form or as plain scanned images. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Identification of solar coronal holes (CHs) provides information both for operational space weather forecasting and long-term investigation of solar activity. Source data for the first problem are typically from the most recent solar disk observations, while for the second problem it is convenient to consider solar synoptic maps. Motivated by the idea that the concept of CHs should be similar for both cases we investigate universal models that can learn CH segmentation in disk images and reproduce the same segmentation in synoptic maps. We demonstrate that convolutional neural networks trained on daily disk images provide an accurate CH segmentation in synoptic maps and their pole-centric projections. Using this approach we construct a catalog of synoptic maps for the period of 2010–20 based on SDO/AIA observations in the 193 Ĺ wavelength. The obtained CH synoptic maps are compared with magnetic synoptic maps in the time-latitude and time-longitude diagrams. The initial results demonstrate that while in some cases the CHs are associated with magnetic flux-transport events there are other mechanisms contributing to the CH formation and evolution. To stimulate further investigations the catalog of synoptic maps is published in open access. [ABSTRACT FROM AUTHOR]