19 July 2010 A simple and effective algorithm for quasar candidate selection
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K-Nearest Neighbor (kNN) algorithm is one of the simplest and most flexible and effective classification algorithms, which has been widely used in many fields. Using the multi-band samples extracted from large surveys of SDSS DR7 and UKIDSS DR3, we investigate the performance of kNN with different combinations of colors to select quasar candidates. The color histograms of quasars and stars is helpful to select the optimal input pattern for the classifier of kNN. The best input pattern is (u-g, g-r, r-i, i-z, z-Y, Y-J, J-H, H-K, Y-K, g-z). In our case, the performance of kNN is assessed by different performance metrics, which indicate kNN has rather high performance for discriminating quasars from stars. As a result, kNN is an applicable and effective method to select quasar candidates for large sky survey projects.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nanbo Peng, Nanbo Peng, Yanxia Zhang, Yanxia Zhang, Tong Pei, Tong Pei, Yongheng Zhao, Yongheng Zhao, } "A simple and effective algorithm for quasar candidate selection", Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402X (19 July 2010); doi: 10.1117/12.856766; https://doi.org/10.1117/12.856766


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