http://arxiv.org/abs/2210.02635
Context. Photospheric bright points (BPs), as the smallest magnetic element of the photosphere and the footpoint tracer of the magnetic flux tube, are of great significance to the study of BPs. Compared with the study of the characteristics and evolution of a few specific BPs, the study of BPs groups can provide us with a better understanding of the characteristics and overall activities of BPs groups. Aims. We aim to find out the evolution characteristics of the brightness and number of BPs groups at different brightness levels, and how these characteristics differ between quiet and active regions. Methods. We propose a hybrid BPs detection model (HBD Model) combining traditional technology and neural network. The Model is used to detect and calculate the BPs brightness characteristics of each frame of continuous high resolution image sequences of active and quiet regions in TiO-band of a pair of BBSO. Using machine learning clustering method, the PBs of each frame was divided into four levels groups (level1-level4) according to the brightness from low to high. Finally, Fourier transform and inverse Fourier transform are used to analyze the evolution of BPs brightness and quantity in these four levels groups. Results. The activities of BPs groups are not random and disorderly. In different levels of brightness, their quantity and brightness evolution show complex changes. Among the four levels of brightness, BPs in the active region were more active and intense than those in the quiet region. However, the quantity and brightness evolution of BPs groups in the quiet region showed the characteristics of large periodic changes and small periodic changes in the medium and high brightness levels (level3 and level4). The brightness evolution of PBs group in the quiet region has obvious periodic changes, but the active region is in a completely random and violent fluctuation state.
H. Bai
Fri, 7 Oct 22
17/62
Comments: N/A