Automated measurement of quasar redshift with a Gaussian process

Author:

Fauber Leah1,Ho Ming-Feng1,Bird Simeon1,Shelton Christian R1,Garnett Roman2,Korde Ishita1

Affiliation:

1. University of California Riverside, Department of Physics and Astronomy and Department of Computer Science, Riverside, CA 90703, USA

2. Washington University in St. Louis, Department of Computer Science and Engineering, One Brookings Drive, St. Louis, MO 63130, USA

Abstract

ABSTRACT We develop an automated technique to measure quasar redshifts in the Baryon Oscillation Spectroscopic Survey of the Sloan Digital Sky Survey (SDSS). Our technique is an extension of an earlier Gaussian process method for detecting damped Lyman α 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 $0.38{{\ \rm per\ cent}}$ 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.

Funder

National Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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