斯隆数字巡天中类星体和其它点源的测光星表
Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey
摘要：
我们提出了一个约600万个未解决的光度检测在斯隆数字巡天第七次数据发布分类成恒星，星系和类星体的目录。我们使用在SDSS { \it i}波段中从14到22级光谱确认对象的子集上训练的机器学习分类器。我们的星表包括2，430，625个类星体、3，544，036颗恒星和63，586个在SDSS { \it i}波段中从14等到24等的未分辨星系。我们的算法恢复99.96%的光谱确认的类星体和99.51%的恒星i $\sim$ 21.3的颜色窗口，我们研究。污染程度，由于数据文物的对象超过$i=21.3$是高度不确定的，所有提到的完整性和污染的文件是有效的，只有对象比这个亮度。然而，类星体的预测数量与理论数量的比较显示出合理的一致性。
英文摘要：
We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036 stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond $i=21.3$ is highly uncertain and all mention of completeness and contamination in the paper are valid only for objects brighter than this magnitude. However, a comparison of the predicted number of quasars with the theoretical number counts shows reasonable agreement.
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