Detecting Quasars in Large-Scale Astronomical Surveys

TL;DR

This paper introduces a classification method that leverages spectroscopic data to improve the detection of quasars in large astronomical surveys, complementing existing photometric approaches.

Contribution

It demonstrates that spectroscopic features significantly enhance quasar classification accuracy beyond photometric-only methods.

Findings

Spectroscopic features improve classification performance

Method is compatible with existing catalog schemes

Provides a basis for mutual accuracy assessment

Abstract

We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.