Identification of Carbon Stars in LAMOST DR9 Based on Deep Learning

YiMing He, Zhong Cao, Hui Deng, Feng Wang, Ying Mei, Lei Tan
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Abstract

Carbon stars play a crucial role in astronomical research and are significant for understanding stellar evolution, measuring cosmic distances, and studying galaxy kinematics. In recent years, identifying carbon stars using machine learning methods and traditional line-index methods has become a research hotspot, but there are still limitations regarding accuracy and automation. In this study, we propose to build a five-class model to identify carbon stars using spectral data from LAMOST DR9. The model achieved 99.45% precision and 91.21% recall on the carbon star testing set. We conducted independent tests using a sample of 1333 known carbon stars that were not used in the training and testing phases, and our model ultimately identified 1199 carbon stars. On this basis, we used this model to screen 11,226,252 spectra of LAMOST DR9 and identified 4383 carbon stars, including 1197 newly discovered carbon stars. To gain a more comprehensive understanding of the characteristics of the 4383 carbon stars obtained, further visual inspection of these spectra was performed to provide more detailed carbon star subtypes.
基于深度学习识别 LAMOST DR9 中的碳星
碳星在天文研究中起着至关重要的作用,对了解恒星演化、测量宇宙距离和研究星系运动学意义重大。近年来,利用机器学习方法和传统的线指数方法识别碳星已成为研究热点,但在准确性和自动化方面仍存在局限。在本研究中,我们提出利用LAMOST DR9的光谱数据建立一个五类模型来识别碳星。该模型在碳星测试集上达到了 99.45% 的精确度和 91.21% 的召回率。我们使用训练和测试阶段未使用的 1333 个已知碳星样本进行了独立测试,我们的模型最终识别出了 1199 个碳星。在此基础上,我们使用该模型筛选了 LAMOST DR9 的 11,226,252 条光谱,识别出 4383 颗碳星,其中包括 1197 颗新发现的碳星。为了更全面地了解所获得的 4383 颗碳星的特征,我们对这些光谱进行了进一步的目视检查,以提供更详细的碳星亚型。
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