Automated Measurement of Quasar Redshift with a Gaussian Process

TL;DR

This paper presents an automated Gaussian process-based method for estimating quasar redshifts in SDSS data, providing probabilistic redshift uncertainties and improving DLA detection, with broad applicability in astrophysics research.

Contribution

It introduces an extension of Gaussian process techniques for redshift estimation that accounts for uncertainties and is applicable to large quasar datasets.

Findings

Broadly competitive with existing redshift estimators

Disagrees with PCA redshift in only 0.38% of spectra

Redshift uncertainty propagation improves DLA detection

Abstract

We develop an automated technique to measure quasar redshifts in the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey (SDSS). Our technique is an extension of an earlier Gaussian process method for detecting damped Lyman-alpha absorbers (DLAs) in quasar spectra with known redshifts. We apply this technique to a subsample of SDSS DR12 with BAL quasars removed and redshift larger than 2.15. We show that we are broadly competitive to existing quasar redshift estimators, disagreeing with the PCA redshift by more than 0.5 in only 0.38% of spectra. Our method produces a probabilistic density function for the quasar redshift, allowing quasar redshift uncertainty to be propagated to downstream users. We apply this method to detecting DLAs, accounting in a Bayesian fashion for redshift uncertainty. Compared to our earlier method with a known quasar redshift, we have a moderate decrease in our ability to detect DLAs, predominantly in the noisiest spectra. The area under curve drops from 0.96 to 0.91. Our code is publicly available.