如何使用监督机器学习算法从五波段photometry测量金属丰度
How to measure metallicity from five-band photometry with supervised machine learning algorithms
摘要：
我们证明，有可能使用监督机器学习算法从SDSS五波段光度测量金属丰度，优于0.1 dex。使用光谱估计的金属丰度地面真相，我们建立，优化和训练几个估计来预测金属丰度。我们使用所观察到的光度学，以及导出量，如恒星质量和光度红移，作为功能，我们建立两个样本数据集的中位数红移为0.103和0.218和中位数的r-波段震级分别为17.5和18.3。我们发现，集成方法，如随机森林的树和极端随机树，和支持向量机都表现良好，可以测量金属性的均方根误差（RMSE）为0.081和0.090的两个数据集时，所有对象都包括在内。离群值的分数（|Z_true - Z_pred|> 0.2 dex）分别为2.2和3.9%，如果排除这些对象，则RMSE降低到0.068和0.069。由于这些算法能够捕获数据和目标之间的复杂关系，我们的技术比以前提出的方法表现得更好，这些方法试图使用解析拟合公式拟合金属丰度，并且比基于SED拟合的方法具有3倍的约束能力。此外，该方法对训练集中的污染非常宽容，并且可以用于仅几百个对象的训练样本大小，并且具有非常令人满意的结果。我们分发所有的例程来重现我们的结果，并将其应用于其他数据集。
英文摘要：
We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3 respectively. We find that ensemble methods, such as Random Forests of Trees and Extremely Randomized Trees, and Support Vector Machines all perform comparably well and can measure metallicity with a Root Mean Square Error (RMSE) of 0.081 and 0.090 for the two data sets when all objects are included. The fraction of outliers (objects for which |Z_true - Z_pred| > 0.2 dex) is 2.2 and 3.9%, respectively and the RMSE decreases to 0.068 and 0.069 if those objects are excluded. Because of the ability of these algorithms to capture complex relationships between data and target, our technique performs better than previously proposed methods that sought to fit metallicity using an analytic fitting formula, and has 3x more constraining power than SED fitting-based methods. Additionally, this method is extremely forgiving of contamination in the training set, and can be used with very satisfactory results for training sample sizes of just a few hundred objects. We distribute all the routines to reproduce our results and apply them to other data sets.
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