多波段光学成像中的恒星-星系分类
Star-Galaxy Classification in Multi-Band Optical Imaging
摘要：
地面光学巡天，如PanSTARRS、DES和LSST，将产生大星表，限制星等r > 24。星-星系分离对这样的调查提出了一个重大挑战，因为星系-即使是非常紧凑的星系-在这些深度的数量超过了晕星。我们研究恒星和星系的光度分类技术的内在FWHM < 0.2弧秒。我们考虑无监督的光谱能量分布模板拟合和监督，数据驱动的支持向量机（SVM）。对于模板拟合，我们使用极大似然（ML）方法和一种新的分层贝叶斯（HB）方法，它从数据中学习模板概率的先验分布。SVM需要训练数据来分类未知源; ML和HB不需要。我们认为i.）最佳情况场景（SVM_best），其中训练数据是（不现实地）在信噪比和人口统计两者中的数据的随机采样，以及ii.）一个更现实的场景，其中训练是在更亮的表观幅度上对更高的信噪比数据（SVM_real）进行的。使用COSMOS ugriz数据进行测试，我们发现HB优于ML，提供约80%的完整性，恒星和星系的纯度分别为约60-90%。我们发现，没有算法提供完美的性能，和金属贫主序星关闭恒星的研究可能会受到挑战，穷人的恒星星系分离。使用受试者操作特征曲线，我们可以找到SVM_best、HB、ML和SVM_real的最佳到最差排名。因此，我们得出结论，一个训练有素的SVM将优于模板拟合方法。然而，正常训练的SVM表现更差。因此，分层贝叶斯模板拟合可能被证明是未来调查的最佳分类方法。
英文摘要：
Ground-based optical surveys such as PanSTARRS, DES, and LSST, will produce large catalogs to limiting magnitudes of r > 24. Star-galaxy separation poses a major challenge to such surveys because galaxies---even very compact galaxies---outnumber halo stars at these depths. We investigate photometric classification techniques on stars and galaxies with intrinsic FWHM < 0.2 arcsec. We consider unsupervised spectral energy distribution template fitting and supervised, data-driven Support Vector Machines (SVM). For template fitting, we use a Maximum Likelihood (ML) method and a new Hierarchical Bayesian (HB) method, which learns the prior distribution of template probabilities from the data. SVM requires training data to classify unknown sources; ML and HB don't. We consider i.) a best-case scenario (SVM_best) where the training data is (unrealistically) a random sampling of the data in both signal-to-noise and demographics, and ii.) a more realistic scenario where training is done on higher signal-to-noise data (SVM_real) at brighter apparent magnitudes. Testing with COSMOS ugriz data we find that HB outperforms ML, delivering ~80% completeness, with purity of ~60-90% for both stars and galaxies, respectively. We find no algorithm delivers perfect performance, and that studies of metal-poor main-sequence turnoff stars may be challenged by poor star-galaxy separation. Using the Receiver Operating Characteristic curve, we find a best-to-worst ranking of SVM_best, HB, ML, and SVM_real. We conclude, therefore, that a well trained SVM will outperform template-fitting methods. However, a normally trained SVM performs worse. Thus, Hierarchical Bayesian template fitting may prove to be the optimal classification method in future surveys.
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