标题： 数据驱动的恒星固有颜色和尘埃红化对于光谱测光数据：从蓝边法到机器学习方法 显示英文标题

标题： Data-driven stellar intrinsic colors and dust reddenings for spectro-photometric data: From the blue-edge method to a machine-learning approach

摘要： 恒星的固有颜色（ICs）对于恒星物理和尘埃红化的研究都是至关重要的。 在这项工作中，我们开发了一个XGBoost模型，利用大气参数$T_{\rm eff}$、${\rm log}\,g$和$\rm [M/H]$来预测ICs。 该模型在Gaia和2MASS波段的三种颜色上进行了训练和测试，使用了1,040,446个低红化源。 大气参数由Gaia DR3 GSP-phot模块确定，并通过与APOGEE和LAMOST的比较进行了验证。 我们进一步确认了GSP-phot参数中的偏差，特别是对于$\rm [M/H]$，对IC预测没有显著影响。 通过测试集估计的模型泛化误差为$(G_{\rm BP}\,{-}\,G_{\rm RP})_0$的0.014星等，$(G_{\rm BP}\,{-}\,K_{\rm S})_0$的0.050星等，以及$(J\,{-}\,K_{\rm S})_0$的0.040星等。 该模型被应用于包含5,714,528个红化恒星的样本，这些恒星的参数来自Andrae等人（2023），用于计算ICs和红化程度。 在我们的结果与文献值之间对$E(J\,{-}\,K_{\rm S})$的比较具有高度一致性，进一步验证了 XGBoost 模型的准确性。 $E(G_{\rm BP}\,{-}\,K_{\rm S})/E(G_{\rm BP}\,{-}\,G_{\rm RP})$的变化，作为消光定律的表示，与银河经度的关系在大尺度上被发现。 本工作初步展示了机器学习方法在星际尘埃和尘埃红化计算中的可行性和准确性，其成果可广泛应用于光谱测光数据。 数据集和训练好的模型可通过\url{https://doi.org/10.5281/zenodo.12787594}访问。 更多波段的模型将在后续工作中完成。 显示英文摘要

摘要： Intrinsic colors (ICs) of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the ICs with the atmospheric parameters $T_{\rm eff}$, ${\rm log}\,g$, and $\rm [M/H]$. The model was trained and tested for three colors at Gaia and 2MASS bands with 1,040,446 low-reddening sources. The atmospheric parameters were determined by the Gaia DR3 GSP-phot module and were validated by comparing with APOGEE and LAMOST. We further confirmed that the biases in GSP-phot parameters, especially for $\rm [M/H]$, do not present a significant impact on the IC prediction. The generalization error of the model estimated by the test set is 0.014 mag for $(G_{\rm BP}\,{-}\,G_{\rm RP})_0$, 0.050 mag for $(G_{\rm BP}\,{-}\,K_{\rm S})_0$, and 0.040 mag for $(J\,{-}\,K_{\rm S})_0$. The model was applied to a sample containing 5,714,528 reddened stars with stellar parameters from Andrae et al. (2023) to calculate ICs and reddenings. The high consistency in the comparison of $E(J\,{-}\,K_{\rm S})$ between our results and literature values further validates the accuracy of the XGBoost model. The variation of $E(G_{\rm BP}\,{-}\,K_{\rm S})/E(G_{\rm BP}\,{-}\,G_{\rm RP})$, a representation of the extinction law, with Galactic longitude is found on large scales. This work preliminarily presents the feasibility and the accuracy of the machine-learning approach for IC and dust reddening calculation, whose products could be widely applied to spectro-photometric data. The data sets and trained model can be accessed via \url{https://doi.org/10.5281/zenodo.12787594}. The models for more bands will be completed in the following works.