AutoTAB: Automatic Tracking Algorithm for Bipolar Magnetic Regions

• DOI: 10.48550/arxiv.2304.06615
• Publication Date: 2023-10-01
• Authors: Anu Sreedevi; Bibhuti Kumar Jha; Bidya Binay Karak; Dipankar Banerjee
• Keywords: Sunspots; Solar magnetic fields; FOS: Physical sciences; Solar magnetic flux emergence; Solar cycle; Astrophysics; 7. Clean energy; Bipolar sunspot groups; 01 natural sciences; Space Physics (physics.space-ph); QB460-466; Solar active regions; Astrophysics - Solar and Stellar Astrophysics; Physics - Space Physics; 0103 physical sciences; Astrophysics - Instrumentation and Methods for Astrophysics; Instrumentation and Methods for Astrophysics (astro-ph.IM); Solar and Stellar Astrophysics (astro-ph.SR)

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.