A Multi-Wavelength Analysis of Active Regions and Sunspots by Comparison of Automated Detection Algorithms

TL;DR

This study compares four automated algorithms for detecting solar features like active regions and sunspots using multi-wavelength data, evaluating their performance, correlations, and stability for tracking solar activity.

Contribution

It provides a comprehensive benchmark of four different solar feature detection algorithms on the same dataset, highlighting their strengths and limitations.

Findings

SMART shows good agreement with NOAA/SIDC sunspot area data.

Principal Component Analysis distinguishes between photospheric and coronal properties.

Magnetic flux and sunspot area are key indicators of active-region emergence.

Abstract

Since the Solar Dynamics Observatory (SDO) began recording ~ 1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH) and quiet Sun (QS). One month of data from the SOHO/MDI and SOHO/EIT instruments during 12 May - 23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oceanic and Atmospheric Administration (NOAA) and Solar Influences Data Analysis Centre (SIDC) catalogues using various feature properties such as total sunspot area, which shows good agreement, and the number of features detected, which shows poor agreement. Principal Component Analysis indicates a clear distinction between photospheric properties, which are highly correlated to the first component and account for 52.86% of variability in the data set, and coronal properties, which are moderately correlated to both the first and second principal components. Finally, case studies of NOAA 10377 and 10365 are conducted to determine algorithm stability for tracking the evolution of individual features. We find that magnetic flux and total sunspot area are the best indicators of active-region emergence.