Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey

TL;DR

This paper presents a large photometric catalogue of about 6 million unresolved sources from SDSS DR7, classifying them into stars, galaxies, and quasars using machine learning, with high accuracy for objects brighter than i=21.3.

Contribution

The paper introduces a machine learning-based classification method applied to SDSS data, producing a comprehensive quasar and star catalogue with high recovery rates.

Findings

Recovered 99.96% of spectroscopically confirmed quasars

Achieved 99.51% recovery of stars up to i~21.3

Catalogue contains over 6 million unresolved sources

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.