Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images

Colak, Tufan; Qahwaji, Rami

doi:10.1007/s11207-007-9094-3

Cited by 85 publications

(52 citation statements)

References 12 publications

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“…The classification was further developed by McIntosh (1990) and its version is still in use widely today. Later the classification was automated by (Colak & Qahwaji 2008 number of classes (Bornmann & Shaw 1994).…”

Section: Complexity Properties Of Al Sunspotsmentioning

confidence: 99%

Active Longitude and Coronal Mass Ejection Occurrences

Gyenge

Singh

Kiss

et al. 2017

ApJ

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The spatial inhomogeneity of the distribution of coronal mass ejection (CME) occurrences in the solar atmosphere could provide a tool to estimate the longitudinal position of the most probable CME-capable active regions in the Sun. The anomaly in the longitudinal distribution of active regions themselves is often referred to as active longitude (AL). In order to reveal the connection between the AL and CME spatial occurrences, here we investigate the morphological properties of active regions. The first morphological property studied is the separateness parameter, which is able to characterise the probability of the occurrence of an energetic event, such as solar flare or CME. The second morphological property is the sunspot tilt angle. The tilt angle of sunspot groups allows us to estimate the helicity of active regions. The increased helicity leads to a more complex built-up of the magnetic structure and also can cause CME eruption. We found that the most complex active regions appear near to the AL and the AL itself is associated with the most tilted active regions. Therefore, the number of CME occurrences is higher within the AL. The origin of the fast CMEs is also found to be associated with this region. We concluded that the source of the most probably CME-capable active regions is at the AL. By applying this method we can potentially forecast a flare and/or CME source several Carrington rotations in advance. This finding also provides new information for solar dynamo modelling.

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Section: Complexity Properties Of Al Sunspotsmentioning

confidence: 99%

Active Longitude and Coronal Mass Ejection Occurrences

Gyenge

Singh

Kiss

et al. 2017

ApJ

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“…Moreover, the Mount Wilson classification is generally carried out manually which results in human bias. Several papers (Colak & Qahwaji 2008Stenning et al 2013) have used supervised techniques to reproduce the Mount Wilson and other schemes which has resulted in a reduction in human bias.…”

Section: Contextmentioning

confidence: 99%

Image patch analysis of sunspots and active regions

Moon

Delouille

et al. 2016

J. Space Weather Space Clim.

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Context. Separating active regions that are quiet from potentially eruptive ones is a key issue in Space Weather applications. Traditional classification schemes such as Mount Wilson and McIntosh have been effective in relating an active region large scale magnetic configuration to its ability to produce eruptive events. However, their qualitative nature prevents systematic studies of an active region's evolution for example. Aims. We introduce a new clustering of active regions that is based on the local geometry observed in Line of Sight magnetogram and continuum images. Methods. We use a reduced-dimension representation of an active region that is obtained by factoring the corresponding data matrix comprised of local image patches. Two factorizations can be compared via the definition of appropriate metrics on the resulting factors. The distances obtained from these metrics are then used to cluster the active regions. Results. We find that these metrics result in natural clusterings of active regions. The clusterings are related to large scale descriptors of an active region such as its size, its local magnetic field distribution, and its complexity as measured by the Mount Wilson classification scheme. We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the R-value. Conclusions. Matrix factorization of image patches is a promising new way of characterizing active regions. We provide some recommendations for which metrics, matrix factorization techniques, and regions of interest to use to study active regions.

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“…These cover algorithms for detecting sunspots and active regions (Colak & Qahwaji 2008), solarflare prediction (Colak & Qahwaji 2009) and sunspot detection and tracking . For the purposes of the work described here, new segmentation algorithms were developed and integrated into ASAP to enable the detection and identification of faculae and network thought to be important for irradiance reconstruction.…”

Section: Segmentationmentioning

confidence: 99%

“…This is carried out in a similar manner to that described in Colak & Qahwaji (2008) using automatically calculated threshold values, based on image type and the solar features that need to be detected. In this work, we adopted a number of predefined parameters, which are derived from analysing the MDI solar cycle data, as described in Section 3, and use them systematically in the segmentation approach, in order to achieve consistent segmentations.…”

Section: Initial Feature Detectionmentioning

confidence: 99%

“…In this paper we discuss and present the detection methodology of solar features from SOHO/MDI intensitygrams and magnetograms and their area coverage. For this purpose, we use an extended version of the Automated Solar Activity Prediction tool (ASAP; Colak & Qahwaji 2008, which was used in previous research on the detection of sunspots and active regions. We detect magnetically active solar features in magnetograms and locally dark solar features in intensitygrams and provide active regions, sunspot umbra and penumbra, faculae, and network detections as well as the quiet Sun at daily intervals for the available MDI intensitygrams and magnetograms.…”

Section: Introductionmentioning

confidence: 99%

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Identification of photospheric activity features from SOHO/MDI data using the ASAP tool

Ashamari

Qahwaji

Ipson

et al. 2015

J. Space Weather Space Clim.

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The variation of solar irradiance is one of the natural forcing mechanisms of the terrestrial climate. Hence, the time-dependent solar irradiance is an important input parameter for climate modelling. The solar surface magnetic field is a powerful proxy for solar irradiance reconstruction. The analyses of data obtained with the Michelson Doppler Imager (MDI) on board the SOHO mission are therefore useful for the identification of solar surface magnetic features to be used in solar irradiance reconstruction models. However, there is still a need for automated technologies that would enable the identification of solar activity features from large databases. To achieve this we present a series of enhanced segmentation algorithms developed to detect and calculate the area coverages of specific magnetic features from MDI intensitygrams and magnetograms. These algorithms are part of the Automated Solar Activity Prediction (ASAP) tool. The segmentation algorithms allow us to identify the areas on the solar disk covered by magnetic elements inside and outside boundaries of active regions. Depending on their contrast properties, magnetic features within an active region boundary are classified as sunspot umbra and penumbra, or faculae. Outside an active region boundary magnetic elements are identified as network. We present the detailed steps involved in the segmentation process and provide the area coverages of the segmented MDI intensitygrams and magnetograms. The feature segmentation was carried out on daily intensitygrams and magnetograms from April 21, 1996 to April 11, 2011. This offers an exciting opportunity to undertake further investigations that benefit from solar features segmentations, such as solar irradiance reconstruction, which we plan to investigate in the future.

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Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images

Cited by 85 publications

References 12 publications

Active Longitude and Coronal Mass Ejection Occurrences

Active Longitude and Coronal Mass Ejection Occurrences

Image patch analysis of sunspots and active regions

Identification of photospheric activity features from SOHO/MDI data using the ASAP tool

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