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DC Field | Value | Language |
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dc.contributor.author | Sreedevi, Anu | - |
dc.contributor.author | Jha, Bibhuti K | - |
dc.contributor.author | Karak, Bidya Binay | - |
dc.contributor.author | Banerjee, D | - |
dc.date.accessioned | 2024-01-04T05:18:52Z | - |
dc.date.available | 2024-01-04T05:18:52Z | - |
dc.date.issued | 2023-10 | - |
dc.identifier.citation | The Astrophysical Journal Supplement Series, Vol. 268, No. 2, 58 | en_US |
dc.identifier.issn | 0067-0049 | - |
dc.identifier.uri | http://hdl.handle.net/2248/8303 | - |
dc.description | Open Access | en_US |
dc.description | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | - |
dc.description.abstract | Bipolar magnetic regions (BMRs) provide crucial information about solar magnetism. They exhibit varying morphology and magnetic properties throughout their lifetime, and studying these properties can provide valuable insights into the workings of the solar dynamo. The majority of previous studies have counted every detected BMR as a new one and have not been able to study the full life history of each BMR. To address this issue, we have developed Automatic Tracking Algorithm for BMRs (AutoTAB) that tracks the BMRs for their entire lifetime or throughout their disk passage. AutoTAB uses the binary maps of detected BMRs and their overlapping criterion to automatically track the regions. In this first article of this project, we provide a detailed description of the working of the algorithm and evaluate its strengths and weaknesses by comparing it with existing algorithms. AutoTAB excels in tracking even for the small BMRs (with a flux of ∼1020 Mx), and it has successfully tracked 9152 BMRs over the last two solar cycles (1996–2020), providing a comprehensive data set that depicts the evolution of various properties for each BMR. The tracked BMRs exhibit the well-known butterfly diagram and 11 yr solar cycle variation, except for small BMRs, which appear at all phases of the solar cycle and show a weak latitudinal dependence. Finally, we discuss the possibility of adapting our algorithm to other data sets and expanding the technique to track other solar features in the future. | en_US |
dc.language.iso | en | en_US |
dc.publisher | American Astronomical Society | en_US |
dc.relation.uri | https://doi.org/10.3847/1538-4365/acec47 | - |
dc.rights | © 2023. The Author(s) | - |
dc.subject | Solar magnetic fields | en_US |
dc.subject | Solar active regions | en_US |
dc.subject | Sunspots | en_US |
dc.subject | Bipolar sunspot groups | en_US |
dc.subject | Solar cycle | en_US |
dc.subject | Solar magnetic flux emergence | en_US |
dc.subject | Sunspot groups | en_US |
dc.subject | Sunspot cycle | en_US |
dc.title | AutoTAB: Automatic Tracking Algorithm for Bipolar Magnetic Regions | en_US |
dc.type | Article | en_US |
Appears in Collections: | IIAP Publications |
Files in This Item:
File | Description | Size | Format | |
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AutoTAB0 Automatic Tracking Algorithm for Bipolar Magnetic Regions.pdf | 2.85 MB | Adobe PDF | View/Open |
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