Improved selection of extremely red quasars with boxy CIV lines in BOSS

TL;DR

This paper introduces an improved method for selecting extremely red quasars with peculiar CIV line profiles using a combination of statistical analyses, resulting in a more efficient identification of quasars with specific spectral features.

Contribution

The paper develops a new selection technique based on kernel density estimation and outlier detection in a 3D parameter space, enhancing the identification of core ERQs with peculiar CIV profiles.

Findings

Selected 133 quasars with improved likelihood of broad absorption lines.

New sample shows extreme properties in WISE colour-colour space.

Method validated for application to future large quasar surveys.

Abstract

Extremely red quasars (ERQs) are an interesting sample of quasars in the Baryon Oscillation Spectroscopic Sample (BOSS) in the redshift range of $2.0 - 3.4$ and have extreme red colours of $i-W3\ge4.6$. Core ERQs have strong CIV emission lines with rest equivalent width of $\ge100$\AA. Many core ERQs also have CIV line profiles with peculiar boxy shapes which distinguish them from normal blue quasars. We show, using a combination of kernel density estimation and local outlier factor analyses on a space of the $i-W3$ colour, CIV rest equivalent width and line kurtosis, that core ERQs likely represent a separate population rather than a smooth transition between normal blue quasars and the quasars in the tail of the colour-REW distribution. We apply our analyses to find new criteria for selecting ERQs in this 3D parameter space. Our final selection produces $133$ quasars, which are \emph{three} times more likely to have a visually verified CIV broad absorption line feature than the previous core ERQ sample. We further show that our newly selected sample are extreme objects in the intersection of the WISE AGN catalogue with the MILLIQUAS quasar catalogue in the colour-colour space of ($W1-W2$, $W2-W3$). This paper validates an improved selection method for red quasars which can be applied to future datasets such as the quasar catalogue from the Dark Energy Spectroscopic Instrument (DESI).