Solar plages, which are bright regions on the Sun's surface, are an important indicator of solar activity. In this study, we propose an automated algorithm for identifying solar plages in Ca K wavelength solar data obtained from the Kodaikanal Solar Observatory. The algorithm successfully annotates all visually identifiable plages in an image and outputs the corresponding calculated plage index. We perform a time series analysis of the plage index (rolling mean) across multiple solar cycles to test the algorithm's reliability and robustness. The results show a strong correlation between the calculated plage index and those reported in a previous study. The correlation coefficients obtained for all the solar cycles are higher than 0.90, indicating the reliability of the model. We also suggest that adjusting the hyperparameters appropriately for a specific image using our web-based app can increase the model's efficiency. The algorithm has been deployed on the Streamlit Community Cloud platform, where users can upload images and customize the hyperparameters for desired results. The input data used in this study is freely available from the KSO data archive, and the code and the generated data are publicly available on our GitHub repository. Our proposed algorithm provides an efficient and reliable method for identifying solar plages, which can aid the study of solar activity and its impact on the Earth's climate, technology, and space weather.

翻译：太阳谱斑是太阳表面的明亮区域，作为太阳活动的重要指示因子。本研究提出了一种自动化算法，用于识别从柯代卡纳尔天文台获取的Ca K波段太阳数据中的太阳谱斑。该算法能够成功标注图像中所有视觉可辨的谱斑，并输出对应的计算谱斑指数。我们通过对跨多个太阳活动周的谱斑指数（滚动平均值）进行时间序列分析，检验算法的可靠性与鲁棒性。结果表明，计算得到的谱斑指数与既往研究报告的数值高度相关，所有太阳活动周的相关系数均高于0.90，验证了模型的可靠性。我们还提出，通过基于网页的应用程序针对特定图像适当调整超参数，可提升模型效率。该算法已部署于Streamlit社区云平台，用户可上传图像并自定义超参数以获取期望结果。本研究使用的输入数据可从KSO数据档案库免费获取，代码及生成数据已公开于我们的GitHub代码仓库。我们提出的算法为识别太阳谱斑提供了高效可靠的方法，可助力研究太阳活动及其对地球气候、技术与空间天气的影响。