The Sloan Digital Sky Survey Quasar Lens Search. I. Candidate Selection Algorithm
The Sloan Digital Sky Survey Quasar Lens Search. I. Candidate Selection Algorithm
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DOI：
10.1086/506019
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发表时间：
2006-05
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通讯作者：
M. Oguri;N. Inada;B. Pindor;M. Strauss;G. Richards;J. Hennawi;E. Turner;R. Lupton;D. Schneider;M. Fukugita;J. Brinkmann
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作者：
M. Oguri;N. Inada;B. Pindor;M. Strauss;G. Richards;J. Hennawi;E. Turner;R. Lupton;D. Schneider;M. Fukugita;J. Brinkmann
我们提出了一种算法，用于从斯隆数字巡天（SDSS）中光谱识别的亮度大于i = 19.1的低红移（0.6 < z < 2.2）类星体中选择引力透镜类星体候选者的均匀样本。我们的算法使用形态学和颜色选择，分别用于识别小间距和大间距透镜。我们的选择算法仅依赖于SDSS标准图像处理流水线生成的参数，可以轻松快速地选择候选镜头。该算法在SDSS模拟图像上进行了测试，该模拟图像采用SDSS真实数据中的场和类星体参数分布作为输入。此外，我们考虑了差异变红。我们发现我们的选择算法几乎是完整的，直到分离为1″和通量比为10-0.5。该算法同时选择双透镜和四透镜。在2″的间隔处，选择双精度和四边形具有相似的完备性，高于或低于2″的四边形的选择分别优于或劣于双精度。我们的形态学选择识别了单个类星体中不可忽略的一部分：为了去除这些类星体，我们将候选类星体的图像与两个点源的模型拟合，并拒绝那些在两个点源之间具有异常小的图像分离和/或大的星等差异的图像。我们估计，在图像间距小于2″的情况下，我们的选择算法的效率至少为8%，与无线电调查的效率相当。由于来自恒星的污染较大，当图像距离增加时，效率下降。我们还提出了透镜图像的放大系数作为图像分离的函数，这是精确计算放大偏差所需要的。
We present an algorithm for selecting a uniform sample of gravitationally lensed quasar candidates from low-redshift (0.6 < z < 2.2) quasars brighter than i = 19.1 that have been spectroscopically identified in the Sloan Digital Sky Survey (SDSS). Our algorithm uses morphological and color selections that are intended to identify small- and large-separation lenses, respectively. Our selection algorithm only relies on parameters that the SDSS standard image processing pipeline generates, allowing easy and fast selection of lens candidates. The algorithm has been tested against simulated SDSS images, which adopt distributions of field and quasar parameters taken from the real SDSS data as input. Furthermore, we take differential reddening into account. We find that our selection algorithm is almost complete down to separations of 1″ and flux ratios of 10-0.5. The algorithm selects both double and quadruple lenses. At a separation of 2″, doubles and quads are selected with similar completeness, and above or below 2″ the selection of quads is better or worse, respectively, than for doubles. Our morphological selection identifies a nonnegligible fraction of single quasars: to remove these we fit images of candidates with a model of two point sources and reject those with unusually small image separations and/or large magnitude differences between the two point sources. We estimate the efficiency of our selection algorithm to be at least 8% at image separations smaller than 2″, comparable to that of radio surveys. The efficiency declines as the image separation increases because of larger contamination from stars. We also present the magnification factor of lensed images as a function of the image separation, which is needed for accurate computation of magnification bias.