Machine Learning Uncovers the Universe's Hidden Gems: A Comprehensive Catalogue of CIV Absorption Lines in SDSS DR12

We assemble the largest CIV absorption line catalogue to date, leveraging machine learning, specifically Gaussian processes, to remove the need for visual inspection for detecting CIV 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 CIV 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 CIV catalogue, we validated our pipeline and obtained an 87% classification performance score. We found good purity and completeness values, both ~80%, when a probability of ~95% is used as the threshold. Our pipeline obtained similar CIV 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 CIV doublets with at least 95% confidence. Our catalogue provides maximum a posteriori values and credible intervals for CIV redshift, column density, and Doppler velocity dispersion. We detect CIV 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 A at more than 95% confidence. Our catalogue can be used to investigate the physical properties of the circumgalactic and intergalactic media.