论文标题
使用高斯混合模型和图形神经网络从光度数据中识别热分子恒星
Identifying hot subdwarf stars from photometric data using Gaussian mixture model and graph neural network
论文作者
论文摘要
热的细分恒星对于理解恒星进化，恒星天体物理学和二元恒星系统非常重要。识别更多这样的恒星可以帮助我们更好地了解其统计分布，性质和进化。在本文中，我们提出了一种使用机器学习算法，图形神经网络和高斯混合物模型中的光度数据（B，Y，G，R，I，Z）中搜索热分子恒星的新方法。我们使用高斯混合物模型和马尔可夫距离来构建图形结构，在图形结构上，我们使用图神经网络来识别从86 084星的热点恒星，当召回，精度和F1评分最大化原始，权重和合成少数族裔超级摄像技术数据集时。最后，从21个885个候选人开始，我们选择了大约6 000星，这些星星最相似，这与热门恒星最相似。
Hot subdwarf stars are very important for understanding stellar evolution, stellar astrophysics, and binary star systems. Identifying more such stars can help us better understand their statistical distribution, properties, and evolution. In this paper, we present a new method to search for hot subdwarf stars in photometric data (b, y, g, r, i, z) using a machine learning algorithm, graph neural network, and Gaussian mixture model. We use a Gaussian mixture model and Markov distance to build the graph structure, and on the graph structure, we use a graph neural network to identify hot subdwarf stars from 86 084 stars, when the recall, precision, and f1 score are maximized on the original, weight and synthetic minority oversampling technique datasets. Finally, from 21 885 candidates, we selected approximately 6 000 stars that were the most similar to the hot subdwarf star.
