Statistical Analyses of Solar Active Regions in SDO/HMI Magnetograms Detected by the Unsupervised Machine Learning Method DSARD

Abstract Solar active regions (ARs) host the majority of solar eruptions. Studying the evolution and morphological features of ARs is significant for understanding the physical mechanisms of solar eruptions and beneficial for forecasting hazardous space weather. This work presents an automated DBSCAN-based solar active region detection (DSARD) method for ARs observed in magnetograms. DSARD is based on an unsupervised machine learning algorithm called density-based spatial clustering of applications with noise (DBSCAN). This method is employed to identify ARs in magnetograms observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory from 2010 to 2023. To avoid duplicate detections and minimize projection effects, we focus on a longitudinal range of ±6° from the central meridian of the solar disk. Within this range, we obtain the distributions of the number, area, magnetic flux, tilt angle, and butterfly diagram of bipolar ARs in latitudes and time intervals during solar cycle 24, as well as their drift velocities. Most of these statistical results align with previous studies, which validates our method. The asymmetry indices of the number of ARs, cumulative area, and total unsigned magnetic flux indicate that the northern hemisphere dominated in terms of AR activity during most of solar cycle 24, except near solar maximum. Additionally, we analyze the dipole tilt angles of ARs in solar cycle 24 and the rising phase of solar cycle 25, revealing that 13% and 16% of ARs, respectively, violate Hale’s law.