A Multi-wavelength Analysis of Active Regions and Sunspots by Comparison of Automatic Detection Algorithms

Abstract

The algorithms involved in this study are as follows:1. The Solar Monitor Active Region Tracker (SMART) extracts, characterises, and tracks the evolution of active regions across the solar disk using line-of-sight magnetograms and a combination of image processing techniques. 2. The Automated Solar Activity Prediction code (ASAP) converts continuum images from heliocentric coordinates to Carrington heliographic coordinates, detects and tracks sunspots using thresholding and morphological methods. 3. The Sunspot Tracking And Recognition Algorithm (STARA) is used to detect and track sunspots from continuum images using a technique known as the top-hat transform. 4. The Spatial Possibilistic Clustering Algorithm (SPoCA) is a multi-channel unsupervised spatiallyconstrained fuzzy clustering method that automatically segments solar EUV images into active regions, coronal holes and quiet Sun. In the present paper, it is used to detect, characterise and track coronal active regions.We describe the fundamental properties of each algorithm along with a detailed comparison of outputs obtained from the analysis of about one month of data from the SOHO-MDI and SOHO-EIT instruments during 12 May -23 June, 2003. We track two active regions over time to study their properties in detail, and exploit the entire dataset to investigate correlations between physical properties determined by the algorithms. This study allows us to prepare the algorithms in the best possible way for robust analysis of the large SDO data-stream.The detection rates of the algorithms are compared with findings of the National Oceanic and Atmospheric Administration (NOAA) and the Solar Influences Data Analysis Centre (SIDC). By performing an inter-comparison of the algorithms, the physical properties of the solar features detected are measured at different heights of the solar atmosphere. Solar Physics DOI: 10.1007/•••••-•••-•••-••••-•A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithmsThe launch of the Solar Dynamics Observatory (SDO) in early 2010 has provided the solar physics community with the most detailed view of the Sun to date. However, this presents new challenges for the analysis of solar data. Currently, SDO sends over 1 terabyte of data per day back to Earth and methods for fast and reliable analysis are more important than ever. This article details four algorithms developed separately at the Universities of Bradford and Glasgow, the Royal Observatory of Belgium and Trinity College Dublin for the purposes of automated detection of solar active regions (ARs) and sunspots at different levels of the solar atmosphere.The algorithms involved in this study are as follows:1. The Solar Monitor Active Region Tracker (SMART) extracts, characterises, and tracks the evolution of active regions across the solar disk using line-ofsight magnetograms and a combination of image processing techniques. 2. The Automated Solar Activity Prediction code (ASAP) converts continuum images from heliocentric coordin...

Long-period oscillations of active region patterns: least-squares mapping on second-order curves

“…Martens et al (2012) produced software modules that detect, trace, and analyze the emergence and evolution of ARs, magnetic elements and other solar features, such as flares, sigmoids, filaments, coronal dimmings, polarity inversion lines, sunspots and other magnetic structures. Verbeeck et al (2013) described two algorithms: the solar monitor active region tracker (SMART), which automatically extracts, characterizes, and tracks active regions (Higgins et al 2011); and the automated solar activity prediction (ASAP), which represents a set of algorithms that detect sunspots, faculae, and active regions (Colak & Qahwaji 2008). Moreover, Verbeeck et al (2014) described another method called the spatial possibility clustering algorithm (SPoCA-suite).…”

The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images

“…Verbeeck et al (2011) provide a detailed comparison of outputs from four automatic detection algorithms that detect sunspots, magnetic, and coronal ARs using six weeks of SOHO-EIT data. At the chromospheric level, network and plage regions are separated using thresholding methods (Steinegger et al 1998;Worden et al 1999), which are possibly combined with region-growing techniques (Benkhalil et al 2006).…”

Progress in space weather modeling in an operational environment

“…The prediction performance is assessed using standard forecast-verification measures and compared with the prediction measures of ASAP (Ahmed et al 2013). d ASAP's team at University of Bradford carried out joint research with Royal Observatory of Belgium (ROB), Glasgow University, and Trinity College Dublin (Verbeeck et al 2013). The aim of this work was to compare the performance of established solar imaging systems (ASAP, Spatial Possibilistic Clustering Algorithm (SPoCA), Sunspot Tracking and Recognition Algorithm, and SMART) when processing SDO data.…”