Abstract
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 ∼ 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.
Author
Sheelu Abraham, Ninan Sajeeth Philip, Ajit Kembhavi, Yogesh G. Wadadekar and Rita Sinha
Journal
Mon. Not. R. Astron. Soc. 419, 80–94 (2012)
Paper Publication Date
2012
Paper Type
Astroinformatics