Spectroscopic QUasar Extractor and redshift (z) EstimatorSQUEzE II: Universality of the results

Abstract

In this paper we study the universality of the results of SQUEzE, a software package to classify quasar spectra and estimate their redshifts. The code is presented in \cite{Perez-Rafols+2019}. We test the results against changes on signal-to-noise, spectral resolution, wavelength coverage, and quasar brightness. We find that SQUEzE levels of performance (quantified with purity and completeness) are stable to spectra that have a noise dispersion 4 times that of our standard test sample, BOSS. We also find that the performance remains unchanged if pixels of width 25Åare considered, and decreases by $\sim 2\%$ for pixels of width 100Å. We see no effect when analyzing subsets of different quasar brightness, and we establish that the blue part (up to 7000Å) of the spectra is sufficient for the classification. Finally, we compare our suite of tests with samples of spectra expected from WEAVE-QSO and DESI, and narrow-band imaging from J-PAS. We conclude that SQUEzE will perform similarly when confronted with the demands of these future surveys as when applied to current BOSS (and eBOSS) data. {\it Keywords: cosmology: observations - quasar: emission lines - quasar: absorption lines}

arXiv comment9 pages, 11 figures, 3 tables; accepted to MNRAS

References in corpus (8)

• SDSS-III: Massive Spectroscopic Surveys of the Distant Universe, the Milky Way Galaxy, and Extra-Solar Planetary Systems
• The Baryon Oscillation Spectroscopic Survey of SDSS-III
• The DESI Experiment Part I: Science,Targeting, and Survey Design
• Spectral Classification and Redshift Measurement for the SDSS-III Baryon Oscillation Spectroscopic Survey
• The Sloan Digital Sky Survey Quasar Catalog: twelfth data release
• J-PAS: The Javalambre-Physics of the Accelerated Universe Astrophysical Survey
• QuasarNET: Human-level spectral classification and redshifting with Deep Neural Networks
• Spectroscopic QUasar Extractor and redshift (z) EstimatorSQUEzE I: Methodology

Cited by in corpus (6)

• The wide-field, multiplexed, spectroscopic facility WEAVE: Survey design, overview, and simulated implementation
• The DESI Survey Validation: Results from Visual Inspection of the Quasar Survey Spectra
• Optimal strategies for identifying quasars in DESI
• Deep Learning in Searching the Spectroscopic Redshift of Quasars
• The miniJPAS survey quasar selection IV: Classification and redshift estimation with SQUEzE
• The miniJPAS survey quasar selection V: combined algorithm