Gaia DR3中碳星识别的深度学习可解释性分析
Deep learning interpretability analysis for carbon star identification in Gaia DR3
AI总结：
提出改进的一维卷积神经网络GaiaNet，结合SHAP可解释性分析，基于Gaia XP光谱高精度识别碳星并揭示关键CN分子带特征，同时获得451颗新候选碳星。
摘要：
背景。很大一部分渐近巨星支（AGB）恒星在演化过程中会形成富碳大气。根据颜色和光度，这些碳星可以很容易地与许多其他类型的恒星区分开来。然而，大量G型、K型和M型巨星在赫罗图上也占据与碳星相同的区域。尽管如此，它们的光谱仍表现出差异，尤其是在显著的CN分子带中。目标。我们旨在利用Gaia的XP光谱将碳星与其他类型恒星区分开来，同时对识别所需的关键特征提供归因解释，甚至发现额外的新光谱关键特征。方法。我们提出了一个名为“GaiaNet”的分类模型，这是一种专门为处理Gaia的XP光谱而设计的改进型一维卷积神经网络。我们利用SHAP可解释性模型来确定光谱中每个特征的SHAP值，从而能够解释“GaiaNet”模型的输出并提供进一步有意义的分析。与四种传统机器学习方法相比，“GaiaNet”模型在验证集上的平均分类准确率提高了约0.3%，最高准确率达到100%。利用SHAP模型，我们给出了清晰的光谱热图，突出显示了主要分布在CN773.3和CN895.0附近的分子带吸收特征，并总结了碳星识别的五个关键特征区域。将训练后的分类模型应用于具有Gaia“xp_sampled_mean”光谱的CSTAR样本后，我们作为副产品获得了451颗新的候选碳星。
英文摘要：
Context. A large fraction of Asymptotic Giant Branch (AGB) stars develop carbon-rich atmospheres during their evolution. Based on their color and luminosity, these carbon stars can be easily distinguished from many other kinds of stars. However, numerous G, K, and M giants also occupy the same region as carbon stars on the HR diagram. Despite this, their spectra exhibit differences, especially in the prominent CN molecular bands. Target. We aim to distinguish carbon stars from other kinds of stars using $Gaia$'s XP spectra, while providing attributional interpretations of key features necessary for identification, and even discovering additional new spectral key features. Method. We propose a classification model named `GaiaNet', an improved one-dimensional convolutional neural network specifically designed for handling $Gaia$'s XP spectra. We utilized the SHAP interpretability model to determine SHAP values for each feature in a spectrum, enabling us to explain the output of the `GaiaNet' model and provide further meaningful analysis. Compared to four traditional machine-learning methods, the `GaiaNet' model exhibits an average classification accuracy improvement of approximately 0.3% on the validation set, with the highest accuracy reaching 100%. Utilizing the SHAP model, we present a clear spectroscopic heatmap highlighting molecular band absorption features primarily distributed around CN773.3 and CN895.0, and summarize five key feature regions for carbon star identification. Upon applying the trained classification model to the CSTAR sample with Gaia `xp_sampled_mean' spectra, we obtained 451 new candidate carbon stars as a by-product.
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