Quasar candidates selection in the Virtual Observatory era
Quasar candidates selection in the Virtual Observatory era
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DOI：
10.1111/j.1365-2966.2009.14754.x
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发表时间：
2008-05
影响因子：
4.8
通讯作者：
R. D’abrusco;Giuseppe Longo;N. Walton
中科院分区：
文献类型：
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作者：
R. D’abrusco;Giuseppe Longo;N. Walton
提出了一种多波段巡天中候选类星体的光度选择方法。该方法利用从光谱确认的准恒星天体（qso）的子样本中获得的先验知识来映射参数空间。通过组合使用两种算法（概率主曲面和负熵聚类）在色彩空间中执行QSOs候选恒星和恒星的解纠缠，这是第一次在天文学背景下使用。这两种方法都已在Astrogrid虚拟天文台平台的神经软件包中实现。尽管它们属于无监督聚类工具的类别，但该方法的性能通过使用已确认类星体的可用样本进行优化，因此可以从可用的“知识库”中学习任何改进。该方法已在可见光斯隆数字巡天（SDSS）和红外英国红外深空巡天-大面积巡天公共数据库中提取的光学和光学加近红外数据上进行了应用和测试。在所有的情况下，实验导致高效率和完整性的高价值，如果不是比文献中已知的方法更好的话，也是相当的。从SDSS数据发布7遗留光度数据集中提取的光学候选qso目录已经制作完成，并可在URL http://voneural.na.infn.it/qso.html上公开获取。
We present a method for the photometric selection of candidate quasars in multiband surveys. The method makes use of a priori knowledge derived from a subsample of spectroscopic confirmed quasi-stellar objects (QSOs) to map the parameter space. The disentanglement of QSOs candidates and stars is performed in the colour space through the combined use of two algorithms, the probabilistic principal surfaces and the negative entropy clustering, which are for the first time used in an astronomical context. Both methods have been implemented in the voneural package on the Astrogrid Virtual Observatory platform. Even though they belong to the class of the unsupervised clustering tools, the performances of the method are optimized by using the available sample of confirmed quasars and it is therefore possible to learn from any improvement in the available ‘base of knowledge’. The method has been applied and tested on both optical and optical plus near-infrared data extracted from the visible Sloan Digital Sky Survey (SDSS) and infrared United Kingdom Infrared Deep Sky Survey-Large Area Survey public data bases. In all cases, the experiments lead to high values of both efficiency and completeness, comparable if not better than the methods already known in the literature. A catalogue of optical candidate QSOs extracted from the SDSS Data Release 7 Legacy photometric data set has been produced and is publicly available at the URL http://voneural.na.infn.it/qso.html.