Hybrid Detection Algorithm and Study on the Quantity and Brightness Evolution Characteristics of Photospheric Bright Point Groups

• Published in: The Astrophysical journal Vol. 956; no. 1; pp. 62 - 77
• Main Authors: Bai, Haicheng, Yang, Peng, Zhao, Limin, Gong, Xiaoying, Zhong, Libo, Yang, Yang, Rao, Changhui
• Format: Journal Article
• Language: English
• Published: Philadelphia The American Astronomical Society 01-10-2023; IOP Publishing
• Subjects: Algorithms; Astronomy data analysis; Astronomy image processing; Astrophysics; Brightness; Coronal heating; Evolution; Machine learning; Magnetic fields; Photosphere; Solar magnetic bright points; Solar photosphere
• ISSN: 0004-637X; 1538-4357
• DOI: 10.3847/1538-4357/aced94

Abstract
Photospheric bright points (BPs), the smallest magnetic elements in the photosphere, are constantly moving and changing. Studying the characteristics of these small-scale strong magnetic fields with kilogauss magnitudes could be of significant importance for investigating the coronal heating problem. Compared to the study of a few specific BPs, investigating the collective features of BP groups can provide us with a better understanding of the overall characteristics of BPs. However, there is still a lack of research on the evolution of BP groups, and the detection algorithm of BPs still has a lot of space for improvement. We propose a hybrid BP detection model (HBD-Model) that combines traditional algorithms and deep learning to improve detection accuracy. Using the HBD-Model, we focus on studying the evolution characteristics of the quantity and brightness of BP groups at different brightness levels and how these characteristics differ between quiet and active regions. Results show that the activity of BP groups is not random or disorderly. In different brightness levels and regions, their quantity and brightness evolution exhibit periodic or complex changes.