Automated sunspot detection and the evolution of sunspot magnetic fields during solar cycle 23

TL;DR

This paper presents an automated method for detecting and tracking sunspots over solar cycle 23, enabling detailed analysis of sunspot magnetic field evolution using a large, consistent dataset.

Contribution

It introduces the Sunspot Tracking And Recognition Algorithm (STARA) for automated sunspot detection and tracking, facilitating comprehensive analysis of sunspot magnetic fields over a full solar cycle.

Findings

Created a detailed sunspot catalogue covering 1996-2010

Analyzed magnetic field evolution during solar cycle 23

Demonstrated the effectiveness of automated detection methods

Abstract

The automated detection of solar features is a technique which is relatively underused but if we are to keep up with the flow of data from spacecraft such as the recently launched Solar Dynamics Observatory, then such techniques will be very valuable to the solar community. Automated detection techniques allow us to examine a large set of data in a consistent way and in relatively short periods of time allowing for improved statistics to be carried out on any results obtained. This is particularly useful in the field of sunspot study as catalogues can be built with sunspots detected and tracked without any human intervention and this provides us with a detailed account of how various sunspot properties evolve over time. This article details the use of the Sunspot Tracking And Recognition Algorithm (STARA) to create a sunspot catalogue. This catalogue is then used to analyse the magnetic fields in sunspot umbrae from 1996-2010, taking in the whole of solar cycle 23.