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arXiv 2209.02553astro-ph.GA
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巨恒星光度金属丰度的机器学习方法

A machine learning approach to photometric metallicities of giant stars

Connor P. Fallows, Jason L. Sanders

备注：
18 pages, 14 figures, 3 tables, accepted for publication in MNRAS
发表信息：
MNRAS Volume 516, Issue 4, November 2022, Pages 5521-5537
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摘要：

尽管大规模光度测量取得了进展，但恒星的特征--如金属丰度--通常仍然局限于光谱观测，通常是明亮的、附近的低消光恒星。为了纠正这一点，我们提出了一种神经网络方法，用于估计红巨星的金属丰度和距离，8波段测光和来自盖亚EDR 3和2 MASS和WISE调查的反射。该算法考虑了预测中的不确定性，这些不确定性来自每个输入的可能输出范围以及与训练集兼容的模型范围（通过退出）。一个两阶段的过程中采用的初始网络，以估计摄影天体测量视差训练使用大量的样本的嘈杂视差数据从盖亚EDR 3，然后一个二级网络训练使用光谱金属丰度从APOGEE和LAMOST调查和增强的特征空间利用第一阶段的视差估计。该算法产生的金属丰度预测的平均不确定性为$\pm0.19$ dex。该方法适用于恒星内的银河酒吧/凸起，特别注重样本的169万个对象与盖亚径向速度。我们证明了我们的方法的使用和有效性，通过检查空间和运动学梯度与金属量在银河系酒吧/隆起恢复以前的结果上的垂直金属量梯度（$-0.528 \pm 0.002$德克斯/kpc）和酒吧的顶点偏差（$-21.29 \pm 2.74$度）。

英文摘要：

Despite the advances provided by large-scale photometric surveys, stellar features - such as metallicity - generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a neural network approach for estimating the metallicities and distances of red giant stars with 8-band photometry and parallaxes from Gaia EDR3 and the 2MASS and WISE surveys. The algorithm accounts for uncertainties in the predictions arising from the range of possible outputs at each input and from the range of models compatible with the training set (through drop-out). A two-stage procedure is adopted where an initial network to estimate photo-astrometric parallaxes is trained using a large sample of noisy parallax data from Gaia EDR3 and then a secondary network is trained using spectroscopic metallicities from the APOGEE and LAMOST surveys and an augmented feature space utilising the first-stage parallax estimates. The algorithm produces metallicity predictions with an average uncertainty of $\pm0.19$ dex. The methodology is applied to stars within the Galactic bar/bulge with particular focus on a sample of 1.69 million objects with Gaia radial velocities. We demonstrate the use and validity of our approach by inspecting both spatial and kinematic gradients with metallicity in the Galactic bar/bulge recovering previous results on the vertical metallicity gradient ($-0.528 \pm 0.002$ dex/kpc) and the vertex deviation of the bar ($-21.29 \pm 2.74$ deg).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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