Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey by Sheelu Abraham, Ninan Sajeeth Philip, Ajit Kembhavi, Yogesh G Wadadekar, and Rita Sinha. (Submitted on 9 Nov 2010 (v1), last revised 25 Aug 2011 (this version, v3))
Abstract:
We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey 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 {\it 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 {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$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.
OK, admittedly more interest to me than probably anyone else that reads this blog.
Still, every machine learning technique and data requirement that you learn has potential application in other fields.