Software tool for automatic detection of solar plages in the Coimbra Observatory spectroheliograms

Abstract

Full-disk spectroheliograms have been taken in Coimbra on a daily basis since 1926 in the Ca II K-line (K1 and K3). Later, in 1989, with the upgrade of the equipment it was possible to start the observations in the H-alpha line. The spectroheliograms of Coimbra constitutes a huge dataset of solar images, which requires an efficient automatic tool to detect and analyse solar activity features. This work presents a mathematical morphology approach applied to the CaII K3 series. The objective is to create a tool based on the segmentation by watershed transform combined with other morphological operators to detect automatically and analyse chromospheric plages during the solar cycle 24. The tool is validated by comparing its results for cycle 23 with those presented by Dorotovic et al. (2007Dorotovic et al. ( , 2010. The results obtained are in very good agreement with those, including on images obtained in non-ideal meteorological conditions (eg. some clouds in sky). The results were also qualitatively compared with the results obtained through the application of ASSA model to SDO HMI magnetograms.

Relating 27-day Averages of Solar, Interplanetary Medium Parameters and Geomagnetic Activity Proxies in Solar Cycle 24

“…The sunspot numbers are obtained from the SILSO (Sunspot Index and Long-term Solar Observations) database at the Royal Observatory of Belgium. The facular areas were calculated by Barata et al (2018), using the computational tool developed by them. The 27-day-averaged data is shown at Figure 1.…”

“…total facular area (% of solar disk). Calculated byBarata et al (2018).2 FA-N, northern facular area (% of solar disk). 3 FA-S, southern facular area (% of solar disk).…”

Testing the Accuracy of Coimbra Astronomical Observatory Solar Filament Historical Series (1929–1941)

“…Until the present day, more than 50 000 spectroheliograms were acquired by the OGAUC spectroheliograph (Lourenço et al, 2019). Recently, software tools have been developed to automatically detect and analyse chromospheric plages (Barata et al, 2018) and sunspots (Carvalho et al, 2020) on solar images acquired at the OGAUC.…”

The application of machine learning in solar physics

“…[51] 、Zernike矩 [52] 、灰度共生矩阵 [69] 、复小波变 换 [69] 、数学形态学 [73] 等图像分析和模式识别技术, 被 [74] 和SHARP(Space-Weather HMI Active Region Patches)数据集 [75] , 以及从 [42] 2018 CME到达时间估计 SOHO LASCO C2之前观测到的182个 部分或全晕CME事件 SVM 预测误差约为5.9 h Inceoglu等人 [43] 2018 CME关联事件分析 2010-2018年的DONKI数据 SVM, MLP 基于18个活动区物理参数, 分析CME和SEPs的关联性, TSS约为0.91.…”