摘要: 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.