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We assemble the largest C iv absorption line catalogue to date, leveraging machine learning, specifically Gaussian processes, to remove the need for visual inspection for detecting C iv absorbers. The catalogue contains probabilities classifying the reliability of the absorption system within a quasar spectrum. Our training set was a sub-sample of DR7 spectra that had no detectable C iv absorption in a large visually inspected catalogue. We used Bayesian model selection to decide between our continuum model and our absorption-line models. Using a random hold-out sample of 1301 spectra from all of the 26 030 investigated spectra in DR7 C iv catalogue, we validated our pipeline and obtained an 87 per cent classification performance score. We found good purity and completeness values, both $\sim 80{{\ \rm per\ cent}}$, when a probability of $\sim 95{{\ \rm per\ cent}}$ is used as the threshold. Our pipeline obtained similar C iv redshifts and rest equivalent widths to our training set. Applying our algorithm to 185 425 selected quasar spectra from SDSS DR12, we produce a catalogue of 113 775 C iv doublets with at least 95 per cent confidence. Our catalogue provides maximum a posteriori values and credible intervals for C iv redshift, column density, and Doppler velocity dispersion. We detect C iv absorption systems with a redshift range of 1.37–5.1, including 33 systems with a redshift larger than 5 and 549 absorbers systems with a rest equivalent width greater than 2 Å at more than 95 per cent confidence. Our catalogue can be used to investigate the physical properties of the circumgalactic and intergalactic media.

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 ≥ 4.6. Core ERQs have strong C  iv emission lines with rest equivalent width of ≥100 Å. Many core ERQs also have C  iv 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, C iv 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 three times more likely to have a visually verified C iv 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 data sets such as the quasar catalogue from the Dark Energy Spectroscopic Instrument.