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A Refined QSO Selection Method Using Diagnostics

Published online by Cambridge University Press:  20 April 2012

Dae-Won Kim ,
Pavlos Protopapas ,
Markos Trichas ,
Michael Rowan-Robinson ,
Roni Khardon ,
Charles Alcock  and
Yong-Ik Byun
Dae-Won Kim
Affiliation:
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA Department of Astronomy, Yonsei University, Seoul, South Korea Institute for Applied Computational Science, Harvard University, Cambridge, MA, USA
Pavlos Protopapas
Affiliation:
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA Institute for Applied Computational Science, Harvard University, Cambridge, MA, USA
Markos Trichas
Affiliation:
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
Michael Rowan-Robinson
Affiliation:
Astrophysics Group, Imperial College, London, UK
Roni Khardon
Affiliation:
Department of Computer Science, Tufts University, Medford, MA 02155, USA
Charles Alcock
Affiliation:
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
Yong-Ik Byun
Affiliation:
Department of Astronomy, Yonsei University, Seoul, South Korea Yonsei University Observatory, Yonsei University, Seoul, South Korea
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Abstract

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We present 663 QSO candidates in the Large Magellanic Cloud (LMC) that were selected using multiple diagnostics. We started with a set of 2,566 QSO candidates selected using the methodology presented in our previous work based on time variability of the MACHO LMC light curves. We then obtained additional information for the candidates by cross-matching them with the Spitzer SAGE, the 2MASS, the Chandra, the XMM, and an LMC UBVI catalogues. Using that information, we specified diagnostic features based on mid-IR colours, photometric redshifts using SED template fitting, and X-ray luminosities, in order to discriminate more high-confidence QSO candidates in the absence of spectral information. We then trained a one-class Support Vector Machine model using those diagnostics features. We applied the trained model to the original candidates, and finally selected 663 high-confidence QSO candidates. We cross-matched those 663 QSO candidates with 152 newly-confirmed QSOs and 275 non-QSOs in the LMC fields, and found that the false positive rate was less than 1%.

Keywords

Magellanic Cloudsmethods: data analysisquasars: general
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 7 , Symposium S285: New Horizons in Time-Domain Astronomy , September 2011 , pp. 344 - 346
Copyright
Copyright © International Astronomical Union 2012

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