COMPARISON OF THE DYNAMICS OF CORONAL HOLES IDENTIFIED BY TWO DETECTION METHODS IN SOLAR ACTIVITY CYCLES 24-25
Authors:
1.
Crimean Astrophysical Observatory of the Russian Academy of Sciences
Type:
Сonference article
Published:
27.12.2024
Subject area:
UDC 52
UDC 53
UDC 520
UDC 521
UDC 523
UDC 524
UDC 52-1
UDC 52-6
CSCSTI 41.00
CSCSTI 29.35
CSCSTI 29.31
CSCSTI 29.33
CSCSTI 29.27
CSCSTI 29.05
Russian Classification of Professions by Education 03.06.01
Russian Classification of Professions by Education 03.05.01
Russian Classification of Professions by Education 03.04.03
Russian Library and Bibliographic Classification 2
Russian Library and Bibliographic Classification 223
Russian Trade and Bibliographic Classification 614
Russian Trade and Bibliographic Classification 6135
BISAK SCI004000 Astronomy
BISAK SCI005000 Physics / Astrophysics
UDC 53
UDC 520
UDC 521
UDC 523
UDC 524
UDC 52-1
UDC 52-6
CSCSTI 41.00
CSCSTI 29.35
CSCSTI 29.31
CSCSTI 29.33
CSCSTI 29.27
CSCSTI 29.05
Russian Classification of Professions by Education 03.06.01
Russian Classification of Professions by Education 03.05.01
Russian Classification of Professions by Education 03.04.03
Russian Library and Bibliographic Classification 2
Russian Library and Bibliographic Classification 223
Russian Trade and Bibliographic Classification 614
Russian Trade and Bibliographic Classification 6135
BISAK SCI004000 Astronomy
BISAK SCI005000 Physics / Astrophysics
Language:
English
Keywords:
methods: data analysis; techniques: image processing; Sun: corona
Funding:
This work was supported by the Ministry of Science and Higher Education of the Russian Federation under the State Assignment (Theme No. 122022400224-7).
Abstract (English):
In this study, we analyze for the first time the long-term variations of coronal holes (CHs) identified by two different automatic detection schemes: Spatial Possibilistic Clustering Algorithm (SPoCA) and Convolutional Neural Network (CNN193). The source material was the observational data acquired by the Atmospheric Imaging Assembly instrument onboard the Solar Dynamics Observatory (AIA/SDO) in the Fe XII 19.3 nm line in the period from June 16, 2010, to May 13, 2021. An initial analysis comparing the long-term variations of the CH areas extracted by the SPoCA method and used by us in earlier works to study their evolution at different stages of the 24th and at the beginning of the 25th cycles showed fairly good agreement with the trend of the CH areas identified by the CNN193 algorithm for the same period. Both schemes reveal hemispheric asymmetry in the generation of CHs both in time and in amplitude.
In this study, we analyze for the first time the long-term variations of coronal holes (CHs) identified by two different automatic detection schemes: Spatial Possibilistic Clustering Algorithm (SPoCA) and Convolutional Neural Network (CNN193). The source material was the observational data acquired by the Atmospheric Imaging Assembly instrument onboard the Solar Dynamics Observatory (AIA/SDO) in the Fe XII 19.3 nm line in the period from June 16, 2010, to May 13, 2021. An initial analysis comparing the long-term variations of the CH areas extracted by the SPoCA method and used by us in earlier works to study their evolution at different stages of the 24th and at the beginning of the 25th cycles showed fairly good agreement with the trend of the CH areas identified by the CNN193 algorithm for the same period. Both schemes reveal hemispheric asymmetry in the generation of CHs both in time and in amplitude.
Keywords:
methods: data analysis; techniques: image processing; Sun: corona
methods: data analysis; techniques: image processing; Sun: corona
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