Selecting Quasar Candidates by a SVM Classification System

• Published in: arXiv.org
• Main Authors: Peng, Nanbo, Zhang, Yanxia, Zhao, Yongheng, Wu, Xuebing
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 28.04.2012
• Subjects: Classification; Physics - Cosmology and Nongalactic Astrophysics; Physics - Instrumentation and Methods for Astrophysics; Predictions; Quasars; Red shift; Sky surveys (astronomy); Support vector machines
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1204.6354

We develop and demonstrate a classification system constituted by several Support Vector Machines (SVM) classifiers, which can be applied to select quasar candidates from large sky survey projects, such as SDSS, UKIDSS, GALEX. How to construct this SVM classification system is presented in detail. When the SVM classification system works on the test set to predict quasar candidates, it acquires the efficiency of 93.21% and the completeness of 97.49%. In order to further prove the reliability and feasibility of this system, two chunks are randomly chosen to compare its performance with that of the XDQSO method used for SDSS-III's BOSS. The experimental results show that the high faction of overlap exists between the quasar candidates selected by this system and those extracted by the XDQSO technique in the dereddened i-band magnitude range between 17.75 and 22.45, especially in the interval of dereddened i-band magnitude < 20.0. In the two test areas, 57.38% and 87.15% of the quasar candidates predicted by the system are also targeted by the XDQSO method. Similarly, the prediction of subcategories of quasars according to redshift achieves a high level of overlap with these two approaches. Depending on the effectiveness of this system, the SVM classification system can be used to create the input catalog of quasars for the GuoShouJing Telescope (LAMOST) or other spectroscopic sky survey projects. In order to get higher confidence of quasar candidates, cross-result from the candidates selected by this SVM system with that by XDQSO method is applicable.