• methods: statistical;
  • techniques: photometric;
  • astronomical data bases: miscellaneous;
  • catalogues;
  • surveys


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.