Statistical Analyses of Solar Prominences and Active Region Features in 304 Å Filtergrams Detected via Deep Learning

Abstract

Solar active regions (ARs) are areas on the Sun with very strong magnetic fields where various activities take place. Prominences are one of the typical solar features in the solar atmosphere, whose eruptions often lead to solar flares and coronal mass ejections. Therefore, studying their morphological features and their relationship with solar activity is useful in predicting eruptive events and in understanding the long-term evolution of solar activities. A huge amount of data have been collected from various ground-based telescopes and satellites. The massive amounts of data make human inspection difficult. For this purpose, we developed an automated detection method for prominences and ARs above the solar limb based on deep-learning techniques. We applied it to process the 304 Å data obtained by SDO/AIA from 2010 May 13 to 2020 December 31. Besides the butterfly diagrams and latitudinal migrations of the prominences and ARs during solar cycle 24, the variations of their morphological features (such as the locations, areas, heights, and widths) with the calendar years and the latitude bands were analyzed. Most of these statistical results based on our new method are in agreement with previous studies, which also guarantees the validity of our method. The N–S asymmetry indices of the prominences and ARs show that the northern hemisphere is the dominant hemisphere in solar cycle 24, except for 2012–2015, and 2020 for ARs. The high-latitude prominences show much stronger N–S asymmetry, where the northern hemisphere is dominant in ∼2011 and ∼2015, and the southern hemisphere is dominant from 2016–2019.

The Effect of the Method of Processing Long Series of Observations on the Display of Cyclic Variations of Regular and Irregular Active Regions

“…The noticeable hemispheric imbalance is confirmed by numerous observations (see, e.g., reviews by Hathaway 2015;Usoskin 2017). This phenomenon is constantly in the focus of modern research on sunspot number, areas (Badalyan & Obridko 2017;Chowdhury et al 2019;Veronig et al 2021;Javaraiah 2022;Batista et al 2023;Carrasco et al 2023), and other indicies of the global activity (Xie et al 2018;Vokhmyanin & Zolotova 2022;Taran et al 2022;Zhang et al 2023Zhang et al , 2024Mursula 2023). The understanding of the N-S asymmetry mechanisms is essential for the prediction models (Hathaway & Upton 2016;Lekshmi et al 2019;Labonville et al 2019;Javaraiah 2021;Nandy 2021;Bhowmik et al 2023).…”