Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 419, Issue 1, Pages 80-94Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.1365-2966.2011.19674.x
Keywords
methods: statistical; techniques: photometric; astronomical data bases: miscellaneous; catalogues; surveys
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Funding
- ISRO [ISRO/RES/2/339/2007-08]
- Alfred P. Sloan Foundation
- National Aeronautics and Space Administration
- National Science Foundation
- US Department of Energy
- Japanese Monbukagakusho and the Max Planck Society
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We present a catalogue of about six million unresolved photometric detections in the Sloan Digital Sky Survey (SDSS) Seventh Data Release, classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS i band. Our catalogue consists of 2 430 625 quasars, 3 544 036 stars and 63 586 unresolved galaxies from 14th to 24th magnitude in the SDSS i band. Our algorithm recovers 99.96 per cent of spectroscopically confirmed quasars and 99.51 per cent of stars to i similar to 21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond i = 21.3 is highly uncertain and all mention of completeness and contamination in the paper are valid only for objects brighter than this magnitude. However, a comparison of the predicted number of quasars with the theoretical number counts shows reasonable agreement.
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