论文标题
数据驱动的恒星模型
Data-Driven Stellar Models
论文作者
论文摘要
我们开发了一个数据驱动的模型，以精确而精确地绘制恒星参数（有效温度，表面重力和金属性），以绘制恒星恒星光度法。该模型必须并且确实可以同时以银河系方式约束特定于通带的尘埃红色向量。该模型使用神经网络来学习一条频段中的一个频段（脱落）绝对幅度，并在许多频段中学习了来自光谱调查的出色参数和GAIA的视差约束。为了证明这种方法的有效性，我们在具有Lamost，Apogee和Galah，Gaia Parallaxes的光谱参数的数据集上训练我们的模型，以及来自Gaia的光学和近红外光度法，Pan-Starrs〜1，2 Mass and Wise。测试这些数据集上的模型可实现出色的拟合度，并通过构造精确的（通过构造精确）预测许多频段中的颜色磁性图。这种灵活的方法严格连接光谱和光度测量，也导致改善，恒星依赖的红色载体。因此，它提供了一种简单准确的方法来预测恒星进化模型中的光度法。我们的模型将构成从光度数据中推断出恒星特性，距离和尘埃灭绝的基础，这应该在银河系的3D映射中得到极大的利用。我们训练有素的模型可以在https://doi.org/10.5281/zenodo.3902382获得。
We develop a data-driven model to map stellar parameters (effective temperature, surface gravity and metallicity) accurately and precisely to broad-band stellar photometry. This model must, and does, simultaneously constrain the passband-specific dust reddening vector in the Milky Way. The model uses a neural network to learn the (de-reddened) absolute magnitude in one band and colors across many bands, given stellar parameters from spectroscopic surveys and parallax constraints from Gaia. To demonstrate the effectiveness of this approach, we train our model on a dataset with spectroscopic parameters from LAMOST, APOGEE and GALAH, Gaia parallaxes, and optical and near-infrared photometry from Gaia, Pan-STARRS~1, 2MASS and WISE. Testing the model on these datasets leads to an excellent fit and a precise - and by construction accurate - prediction of the color-magnitude diagrams in many bands. This flexible approach rigorously links spectroscopic and photometric surveys, and also results in an improved, stellar-temperature-dependent reddening vector. As such, it provides a simple and accurate method for predicting photometry in stellar evolutionary models. Our model will form a basis to infer stellar properties, distances and dust extinction from photometric data, which should be of great use in 3D mapping of the Milky Way. Our trained model may be obtained at https://doi.org/10.5281/zenodo.3902382.
