A digital method to calculate the true areas of sunspot groups

Çakmak, Hikmet

doi:10.1007/s10686-014-9381-6

Cited by 10 publications

(3 citation statements)

References 13 publications

(16 reference statements)

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“…A área de uma mancha solar é calculada tendo como base o hemisfério visível do Sol, ou seja, a metade de sua superfície total, sua unidade é MH ("Milionésimos do Hemisfério" visível) [16]. O valor de 1 MH corresponde a aproximadamente 3,04 milhões de Km².…”

Section: As Manchas Solaresunclassified

Acompanhando e registrando a Região Ativa de Manchas Solares AR 3363 por 14 dias consecutivos

Queiroz,

Tolentino

2024

Cad. Astro.

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O foco central deste artigo é detalhar, por meio de observações, o desenvolvimento da mancha solar denominadaAR 3363 ao longo de 14 dias. Manchas solares emergem na fotosfera, uma região que representa a fronteiraentre o interior do Sol e sua atmosfera. Ao utilizar telescópios equipados com filtros H-Alpha para a observaçãosolar, conseguimos capturar imagens de AR3363, desde o instante em que emerge no lado leste do Sol até omomento em que desaparece no lado oeste. Isso nos possibilitou capturar fotografias, documentando toda atransição dessa região ativa na superfície solar. Além disso, o artigo explora as diferentes camadas do Sol.

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Section: As Manchas Solaresunclassified

Acompanhando e registrando a Região Ativa de Manchas Solares AR 3363 por 14 dias consecutivos

Queiroz,

Tolentino

2024

Cad. Astro.

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“…The sunspot numbers and sunspot areas are used to represent the level of solar activities, thus becoming the most foundational indexes, especially in the field of historical long-term solar activity cycles research (Zirin, 1988;Hathaway and Wilson, 2004;Li et al, 2004;de Toma et al, 2013;C ¸akmak, 2014a;Clette et al, 2014;Lefevre and Clette, 2014;Tang, 2014Tang, , 2015Lin et al, 2019). In addition, the sunspot numbers and sunspot areas in the northern and southern (N-S) hemispheres provide important evidences to support N-S asymmetry, which the magnetic field systems originating in N-S hemispheres and their evolution in the course of solar cycles are only weakly coupled (Newton and Milsom, 1955;Antonucci et al, 1990;Vizoso and Ballester, 1990;Carbonell et al, 1993;Oliver and Ballester, 1994;Krivova, N. A. and Solanki, S. K., 2002;Vernova et al, 2002;Ballester, J. L. et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Sunspot extraction and hemispheric statistics of YNAO sunspot drawings using deep learning

Yang

Yang²,

Feng³

et al. 2023

Astrophys Space Sci

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The sunspot drawings around the globe provide long historical records for understanding the long-term trends in solar activity cycle. Yunnan Astronomical Observatory (YNAO) in China contributes the relatively continuous sunspot drawings from 1957 to 2015. This paper proposes a new deep learning method named as SPR-Mask to extract pores, spots, umbrae and penumbrae in the YNAO sunspot drawings. SPR-Mask consists of three parts: backbone, shared head and mask branch. Especially, it adopts a scale-aware attention network (SAAN) and a PointRend module in the mask branch to improve the accuracy of target edge segmentation. Besides that, each sunspot belonging to northern or southern (N-S) hemisphere is determined by transforming its cartesian coordinates to spherical coordinates after extracting P , B0 and L0 handwritten in sunspot drawings using a revised Lenet-5 deep learning method. The precision, recall and AP of SPR-Mask are 0.92, 0.93, and 0.92, respectively. The test results show the SPR-Mask method has a good performance. The numbers and areas of pores, spots, umbrae and penumbrae for N-S hemisphere are presented and analyzed separately. The YNAO data are also compared with Royal Greenwich Observatory (RGO), Kanzelhöhe Observatory (KSO) and Purple Mountain Astronomical Observatory (PMO) data. The results show they have similar trends, high correlations and similar N-S asymmetries. All data of YNAO are public shared at https://github.com/yzs64/YNAO sd/, which are abundant complementary to the other sunspot catalogues in the world.

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“…The number and area of sunspots are the most important indices of the level of solar activities (Zirin 1988;Hathaway & Wilson 2004;Li et al 2004;de Toma et al 2013;Çakmak 2014b;Clette et al 2014;Lefevre & Clette 2014). The sunspot number is the daily number of sunspots produced by the solar dynamo.…”

Section: Introductionmentioning

confidence: 99%

Sunspots Extraction in PMO Sunspot Drawings Based on Deep Learning

Yang

Zhou

et al. 2021

PASP

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Sunspot numbers and sunspot areas are the most fundamental indices of long-term solar activity levels and the solar magnetic dynamo. This paper presents a deep-learning method for segmenting the components of sunspots in the Purple Mountain Astronomical Observatory (PMO) historical hand drawings spanning from 1954 to 2011. A total of 44568 samples were labeled as the following four types to build the training set and the test set at a ratio of 9:1. They are (1) pores without penumbrae, (2) spots with penumbrae, (3) umbrae within spots, and (4) holes within spots. A Hybrid Task Cascade Region-based Convolutional Neural Networks (HTC RCNN) is adopted; it is designed as three cascade stages adapted to increasingly higher Intersection over Unions to obtain increasing detector quality. The features of sunspots are extracted and fused by a backbone combining residual network 50 and a feature pyramid network. After training the network using the training set, the method is tested by the test set. The precision, recall, and mean Average Precision are 0.90, 0.90, and 0.89, respectively, indicating that the method has a satisfying performance. The components of each sunspot are extracted separately, yielding the numbers and areas of pores, spots, umbrae and penumbrae. Detailed data of the PMO drawings from 1954 to 2011 are shared in public (http://61.166.157.71/HTCSD.html). It is another piece of the puzzle of long-term solar activity cycles and solar magnetic dynamo research.

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A digital method to calculate the true areas of sunspot groups

Cited by 10 publications

References 13 publications

Acompanhando e registrando a Região Ativa de Manchas Solares AR 3363 por 14 dias consecutivos

Acompanhando e registrando a Região Ativa de Manchas Solares AR 3363 por 14 dias consecutivos

Sunspot extraction and hemispheric statistics of YNAO sunspot drawings using deep learning

Sunspots Extraction in PMO Sunspot Drawings Based on Deep Learning

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