Automated Detection of Chromospheric Swirls Based on Their Morphological Characteristics
Abstract High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candida...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dakanalis, Ioannis [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Solar physics - Springer Netherlands, 1967, 296(2021), 1 vom: Jan. |
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volume:296 ; year:2021 ; number:1 ; month:01 |
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| DOI / URN: |
10.1007/s11207-020-01748-3 |
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| 520 | |a Abstract High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candidates for providing an alternative mechanism for the heating of the chromosphere and corona. Therefore, an accurate determination of their number and a statistical study of their physical properties are deemed necessary. In this work we present a novel, automated swirl detection method, which utilizes image pre-processing, curved structure tracing and machine learning techniques that allow for the detection of swirling events based on their morphological features as they appear in chromosphere filtergrams. The method is applied to H$\alpha $ chromospheric spectral line images obtained by the CRisp Imaging Spectropolarimeter (CRISP) at the Swedish 1-m Solar Telescope (SST). It is also tested on grayscale images of vortical sea current flows represented/visualized by synthetic streamlines from the NASA/Goddard Space Flight Center Scientific Visualization Studio. The results are rather encouraging since swirling events are successfully identified. Further improvements of the algorithm, its prospects for the detection and statistical studies of the properties of these events using a wide range of imaging data and its potential application in other scientific fields for the detection of rotating motions are discussed. | ||
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10.1007/s11207-020-01748-3 doi (DE-627)OLC2122688645 (DE-He213)s11207-020-01748-3-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Dakanalis, Ioannis verfasserin aut Automated Detection of Chromospheric Swirls Based on Their Morphological Characteristics 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 Abstract High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candidates for providing an alternative mechanism for the heating of the chromosphere and corona. Therefore, an accurate determination of their number and a statistical study of their physical properties are deemed necessary. In this work we present a novel, automated swirl detection method, which utilizes image pre-processing, curved structure tracing and machine learning techniques that allow for the detection of swirling events based on their morphological features as they appear in chromosphere filtergrams. The method is applied to H$\alpha $ chromospheric spectral line images obtained by the CRisp Imaging Spectropolarimeter (CRISP) at the Swedish 1-m Solar Telescope (SST). It is also tested on grayscale images of vortical sea current flows represented/visualized by synthetic streamlines from the NASA/Goddard Space Flight Center Scientific Visualization Studio. The results are rather encouraging since swirling events are successfully identified. Further improvements of the algorithm, its prospects for the detection and statistical studies of the properties of these events using a wide range of imaging data and its potential application in other scientific fields for the detection of rotating motions are discussed. Chromosphere, quiet Turbulence Tsiropoula, Georgia aut Tziotziou, Kostas aut Koutroumbas, Konstantinos aut Enthalten in Solar physics Springer Netherlands, 1967 296(2021), 1 vom: Jan. (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:296 year:2021 number:1 month:01 https://doi.org/10.1007/s11207-020-01748-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_47 AR 296 2021 1 01 |
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10.1007/s11207-020-01748-3 doi (DE-627)OLC2122688645 (DE-He213)s11207-020-01748-3-p DE-627 ger DE-627 rakwb eng 530 VZ 16,12 ssgn Dakanalis, Ioannis verfasserin aut Automated Detection of Chromospheric Swirls Based on Their Morphological Characteristics 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 Abstract High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candidates for providing an alternative mechanism for the heating of the chromosphere and corona. Therefore, an accurate determination of their number and a statistical study of their physical properties are deemed necessary. In this work we present a novel, automated swirl detection method, which utilizes image pre-processing, curved structure tracing and machine learning techniques that allow for the detection of swirling events based on their morphological features as they appear in chromosphere filtergrams. The method is applied to H$\alpha $ chromospheric spectral line images obtained by the CRisp Imaging Spectropolarimeter (CRISP) at the Swedish 1-m Solar Telescope (SST). It is also tested on grayscale images of vortical sea current flows represented/visualized by synthetic streamlines from the NASA/Goddard Space Flight Center Scientific Visualization Studio. The results are rather encouraging since swirling events are successfully identified. Further improvements of the algorithm, its prospects for the detection and statistical studies of the properties of these events using a wide range of imaging data and its potential application in other scientific fields for the detection of rotating motions are discussed. Chromosphere, quiet Turbulence Tsiropoula, Georgia aut Tziotziou, Kostas aut Koutroumbas, Konstantinos aut Enthalten in Solar physics Springer Netherlands, 1967 296(2021), 1 vom: Jan. (DE-627)129856010 (DE-600)281593-X (DE-576)015160033 0038-0938 nnns volume:296 year:2021 number:1 month:01 https://doi.org/10.1007/s11207-020-01748-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-AST SSG-OPC-AST GBV_ILN_47 AR 296 2021 1 01 |
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Abstract High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candidates for providing an alternative mechanism for the heating of the chromosphere and corona. Therefore, an accurate determination of their number and a statistical study of their physical properties are deemed necessary. In this work we present a novel, automated swirl detection method, which utilizes image pre-processing, curved structure tracing and machine learning techniques that allow for the detection of swirling events based on their morphological features as they appear in chromosphere filtergrams. The method is applied to H$\alpha $ chromospheric spectral line images obtained by the CRisp Imaging Spectropolarimeter (CRISP) at the Swedish 1-m Solar Telescope (SST). It is also tested on grayscale images of vortical sea current flows represented/visualized by synthetic streamlines from the NASA/Goddard Space Flight Center Scientific Visualization Studio. The results are rather encouraging since swirling events are successfully identified. Further improvements of the algorithm, its prospects for the detection and statistical studies of the properties of these events using a wide range of imaging data and its potential application in other scientific fields for the detection of rotating motions are discussed. © The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 |
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Abstract High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candidates for providing an alternative mechanism for the heating of the chromosphere and corona. Therefore, an accurate determination of their number and a statistical study of their physical properties are deemed necessary. In this work we present a novel, automated swirl detection method, which utilizes image pre-processing, curved structure tracing and machine learning techniques that allow for the detection of swirling events based on their morphological features as they appear in chromosphere filtergrams. The method is applied to H$\alpha $ chromospheric spectral line images obtained by the CRisp Imaging Spectropolarimeter (CRISP) at the Swedish 1-m Solar Telescope (SST). It is also tested on grayscale images of vortical sea current flows represented/visualized by synthetic streamlines from the NASA/Goddard Space Flight Center Scientific Visualization Studio. The results are rather encouraging since swirling events are successfully identified. Further improvements of the algorithm, its prospects for the detection and statistical studies of the properties of these events using a wide range of imaging data and its potential application in other scientific fields for the detection of rotating motions are discussed. © The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract High-resolution observations have revealed that rotating structures known as “chromospheric swirls” are ubiquitous in the solar chromosphere. These structures have circular or spiral shapes, are present across a broad range of spatial and temporal scales and are considered as viable candidates for providing an alternative mechanism for the heating of the chromosphere and corona. Therefore, an accurate determination of their number and a statistical study of their physical properties are deemed necessary. In this work we present a novel, automated swirl detection method, which utilizes image pre-processing, curved structure tracing and machine learning techniques that allow for the detection of swirling events based on their morphological features as they appear in chromosphere filtergrams. The method is applied to H$\alpha $ chromospheric spectral line images obtained by the CRisp Imaging Spectropolarimeter (CRISP) at the Swedish 1-m Solar Telescope (SST). It is also tested on grayscale images of vortical sea current flows represented/visualized by synthetic streamlines from the NASA/Goddard Space Flight Center Scientific Visualization Studio. The results are rather encouraging since swirling events are successfully identified. Further improvements of the algorithm, its prospects for the detection and statistical studies of the properties of these events using a wide range of imaging data and its potential application in other scientific fields for the detection of rotating motions are discussed. © The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021 |
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Automated Detection of Chromospheric Swirls Based on Their Morphological Characteristics |
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Tsiropoula, Georgia Tziotziou, Kostas Koutroumbas, Konstantinos |
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