Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey
Sheelu Abraham, Ninan Sajeeth Philip, Ajit Kembhavi, Yogesh G, Wadadekar, Rita Sinha

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 21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond 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…
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