Survey of approaches for targeting quasars

The study of quasars is of great importance to the formation and evolution of galaxies and the early history of the universe, especially high redshift quasars. With the development and employment of large sky spectroscopic survey projects (e.g. 2dF, SDSS), the number of quasars increases to more than 200,000. For improving the efficiency of high-cost telescopes, careful selecting observational targets is necessary. Therefore various targeting quasar algorithms are used and developed based on different data. We review them in detail. Some statistical approaches are based on photometric color, variability, UV-excess, BRX, radio properties, color-color cut and so on. Automated methods include support vector machines (SVMs), kernel density estimation (KDE), artificial neural networks (ANNs), extreme-deconvolution method, probabilistic principal surfaces (PPS) and the negative entropy clustering (NEC), etc. In addition, we touch upon some quasar candidate catalogues created by different algorithms.

[1]  Richard L. White,et al.  RADIO-SELECTED QUASARS IN THE SLOAN DIGITAL SKY SURVEY , 2009, 0909.4091.

[2]  Y. Zhao,et al.  Comparison of decision tree methods for finding active objects , 2007, 0708.4274.

[3]  A. C. Becker,et al.  QUASAR SELECTION BASED ON PHOTOMETRIC VARIABILITY , 2010, 1009.2081.

[4]  Yong-Heng Zhao,et al.  Classification in Multidimensional Parameter Space: Methods and Examples , 2003 .

[5]  Nanbo Peng,et al.  SDSS QUASARS IN THE WISE PRELIMINARY DATA RELEASE AND QUASAR CANDIDATE SELECTION WITH OPTICAL/INFRARED COLORS , 2012, 1204.6197.

[6]  Patrick Petitjean,et al.  Artificial neural networks for quasar selection and photometric redshift determination , 2010 .

[7]  Gregory Dobler,et al.  SELECTING QUASARS BY THEIR INTRINSIC VARIABILITY , 2010, 1002.2642.

[8]  Adam D. Myers,et al.  A SIMPLE LIKELIHOOD METHOD FOR QUASAR TARGET SELECTION , 2011, 1104.4995.

[9]  David Schiminovich,et al.  Statistical Properties of the GALEX-SDSS Matched Source Catalogs, and Classification of the UV Sources , 2006, astro-ph/0611926.

[10]  Richard G. McMahon,et al.  The Second APM UKST Colour Survey for z > 4 quasars , 2000, astro-ph/0012446.

[11]  A. Myers,et al.  THINK OUTSIDE THE COLOR BOX: PROBABILISTIC TARGET SELECTION AND THE SDSS-XDQSO QUASAR TARGETING CATALOG , 2010, 1011.6392.

[12]  A. Szalay,et al.  THE SLOAN DIGITAL SKY SURVEY QUASAR CATALOG. V. SEVENTH DATA RELEASE , 2010, 1004.1167.

[13]  B. A. Weaver,et al.  Variability selected high-redshift quasars on SDSS Stripe 82 , 2010, 1012.2391.

[14]  Alexander G. Gray,et al.  EFFICIENT PHOTOMETRIC SELECTION OF QUASARS FROM THE SLOAN DIGITAL SKY SURVEY. II. ∼1, 000, 000 QUASARS FROM DATA RELEASE 6 , 2004, The Astrophysical Journal Supplement Series.

[15]  Xue-Bing Wu,et al.  Quasar candidate selection and photometric redshift estimation based on SDSS and UKIDSS data , 2010, 1004.1756.

[16]  W. M. Wood-Vasey,et al.  The Sloan Digital Sky Survey quasar catalog: ninth data release , 2012, 1210.5166.

[17]  Pavlos Protopapas,et al.  QUASI-STELLAR OBJECT SELECTION ALGORITHM USING TIME VARIABILITY AND MACHINE LEARNING: SELECTION OF 1620 QUASI-STELLAR OBJECT CANDIDATES FROM MACHO LARGE MAGELLANIC CLOUD DATABASE , 2011 .

[18]  Alexander G. Gray,et al.  EIGHT-DIMENSIONAL MID-INFRARED/OPTICAL BAYESIAN QUASAR SELECTION , 2008, 0810.3567.

[19]  E. al.,et al.  The Sloan Digital Sky Survey: Technical summary , 2000, astro-ph/0006396.

[20]  Heidelberg,et al.  Finding rare objects and building pure samples: Probabilistic quasar classification from low resolution Gaia spectra , 2008, 0809.3373.

[21]  Jean Surdej,et al.  Identification and redshift determination of quasi-stellar objects with medium-band photometry: application to Gaia , 2006 .

[22]  Cambridge,et al.  Luminous K-band Selected Quasars from UKIDSS , 2008, 0802.3650.

[23]  Xiaohui Fan.  Simulation of Stellar Objects in SDSS Color Space , 1999 .

[24]  Technology,et al.  A small-area faint KX redshift survey for QSOs in the ESO Imaging Survey Chandra Deep Field South , 2001, astro-ph/0107451.

[25]  Yong-Heng Zhao,et al.  Support vector machines and kd-tree for separating quasars from large survey data bases , 2008 .

[26]  Chicago,et al.  Colors of 2625 Quasars at 0 < z < 5 Measured in the Sloan Digital Sky Survey Photometric System , 2000, astro-ph/0012449.

[27]  Nathaniel R. Butler,et al.  OPTIMAL TIME-SERIES SELECTION OF QUASARS , 2010, 1008.3143.

[28]  Alexander G. Gray,et al.  Efficient photometric selection of quasars from the sloan digital sky survey: 100,000 z < 3 quasars from data release one , 2004 .

[29]  Yanxia Zhang,et al.  Automated clustering algorithms for classification of astronomical objects , 2004, astro-ph/0403431.

[30]  Nanbo Peng,et al.  Selecting Quasar Candidates by a SVM Classification System , 2012, 1204.6354.

[31]  Alexander S. Szalay,et al.  RANDOM FORESTS FOR PHOTOMETRIC REDSHIFTS , 2010 .

[32]  Ran Wang,et al.  DISCOVERING THE MISSING 2.2 < z < 3 QUASARS BY COMBINING OPTICAL VARIABILITY AND OPTICAL/NEAR-INFRARED COLORS , 2011, 1107.0646.

[33]  N. Ross,et al.  NEAR-INFRARED PHOTOMETRIC PROPERTIES OF 130,000 QUASARS: AN SDSS–UKIDSS-MATCHED CATALOG , 2010, 1012.4187.

[34]  Yong-Heng Zhao,et al.  Random forest algorithm for classification of multiwavelength data , 2009 .

[35]  M. SubbaRao,et al.  Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Quasar Sample , 2002, astro-ph/0202251.

[36]  Ninan Sajeeth Philip,et al.  Photometric Determination of Quasar Candidates , 2009 .

[37]  David J. Miller,et al.  Objective Subclass Determination of Sloan Digital Sky Survey Spectroscopically Unclassified Objects , 2005, astro-ph/0512119.

[38]  R. Carballo,et al.  Use of Neural Networks for the Identification of New z > 3.6 QSOs from FIRST–SDSS DR5 , 2008, 0809.0547.

[39]  John B. Hutchings,et al.  A CATALOG OF 19,100 QUASI-STELLAR OBJECT CANDIDATES WITH REDSHIFT 0.5-1.5* , 2010 .

[40]  L. Bianchi,et al.  QSOs in the Combined SDSS/GALEX Database , 2008, 0803.1177.

[41]  R. Pello,et al.  Quasar Candidate Multicolor Selection Technique: a different approach , 2000 .