Automatic Tracking of Active Regions and Detection of Solar Flares in Solar EUV Images

Caballero, Carlos; Aranda, M. Carmen

doi:10.1007/s11207-013-0415-4

Cited by 16 publications

(11 citation statements)

References 27 publications

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“…Other approaches to automatic flare detection in EUV images include global-thresholding methods combined with fuzzy methods (Aranda & Caballero 2010), and Global Thresholding based on the Geometry of the Histogram (Caballero & Aranda 2014). …”

Section: Flaresmentioning

confidence: 99%

Solar Demon – an approach to detecting flares, dimmings, and EUV waves on SDO/AIA images

Kraaikamp

2015

J. Space Weather Space Clim.

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Flares, dimmings, and extreme ultraviolet (EUV) waves are three types of eruptive phenomena on the Sun, which are main drivers of space weather. Fast and reliable detection of these phenomena helps augment space weather predictions. In the current paper, we introduce Solar Demon, the first software that detects all three phenomena, using a modular design to exploit synergies. While Solar Demon runs in near real-time on SDO/AIA synoptic quick-look images to provide fast detections of flares, dimmings, and EUV waves for space weather purposes, it also processes new Atmospheric Imaging Assembly (AIA) synoptic science images on a regular basis to build dedicated science quality catalogs. An overview of Solar Demon is given, with a focus on the algorithms for EUV wave detection and characterization. Several first results, such as flare and dimming butterfly diagrams for the rising part of Solar Cycle 24, are presented. The main advantages, challenges, and future prospects for Solar Demon are outlined in the Section 5.

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Section: Flaresmentioning

confidence: 99%

Solar Demon – an approach to detecting flares, dimmings, and EUV waves on SDO/AIA images

Kraaikamp

2015

J. Space Weather Space Clim.

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“…10 3. METHODOLOGY Flare detection schemes are often built upon (1) time series data and/or (2) image data, for automatic detection (Fernandez Borda et al 2002;Qu et al 2003;Kano et al 2008;Grigis et al 2009;Aranda & Caballero 2010;Bonte et al 2013;Caballero & Aranda 2014;Kraaikamp & Verbeeck 2015). For this work, we utilized Solarsoft's Flare Locator algorithm (Freeland & Handy 1998) that works on the second principle-that is, image-based data.…”

Section: Data Selectionmentioning

confidence: 99%

A Catalog of Solar Flare Events Observed by the SOHO/EIT

Rotti,

Martens,

Aydin

2020

Preprint

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We have compiled a catalog of solar flares as observed by the Extreme ultraviolet Imaging Telescope (EIT) aboard the Solar and Heliospheric Observatory (SOHO) spacecraft and the GOES spacecraft over a span from 1997 to 2010. During mid-1998, the cadence of EIT images was revised from two images per day to 12 minutes. However, the low temporal resolution causes significant data gaps in capturing much of the flaring phenomenon. Therefore, we monitor possible errors in flare detection by flare parameters such as temporal overlap, observational wavelength, and considering full field of view (FOV) images. We consider the GOES flare catalog as the primary source. We describe the technique used to enhance the GOES detected flares using the Extreme Ultraviolet (EUV) image captured by the EIT instrument. In order to detect brightenings, we subtract the images with a maximum cadence of 25 minutes. We have downloaded and analyzed the EIT data via the Virtual Solar Observatory (VSO). This flare dataset from the SOHO/EIT period proves indispensable to the process of the solar flare predictions as the instrument has covered most of Solar Cycle 23.

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“…For example, the first stage forms track fragments by linking object detection reports that are highly likely to belong to the same track. The determination of being highly likely to belong to the same track was accomplished by use of the differential rotation equation of the sun, as was used in Caballero and Aranda (2014) and developed in Howard et al (1990), as a way to predict the location of a given detection at a later time. A depiction return tl 19: end procedure of the search area projected forward for a Coronal Hole was shown in Fig.…”

Section: Tracking Implementationmentioning

confidence: 99%

Tracking Solar Events through Iterative Refinement

Kempton

Angryk

2015

Astronomy and Computing

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In this paper, we combine two approaches to multiple-target tracking: the first is a hierarchical approach to iteratively growing track fragments across gaps in detections; the second is a network flow based optimization method for data association. The network flow based optimization method is utilized for data association in an iteratively growing manner. This process is applied to solar data, retrieved from the Heliophysics Event Knowledge base (HEK) and utilizes precomputed image parameter values. These precomputed image parameter values are used to compare visual similarity of detected events, to determine the best matching track fragment associations, which leads to a globally optimal track fragment association hypothesis.

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Automatic Tracking of Active Regions and Detection of Solar Flares in Solar EUV Images

Cited by 16 publications

References 27 publications

Solar Demon – an approach to detecting flares, dimmings, and EUV waves on SDO/AIA images

Solar Demon – an approach to detecting flares, dimmings, and EUV waves on SDO/AIA images

A Catalog of Solar Flare Events Observed by the SOHO/EIT

Tracking Solar Events through Iterative Refinement

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