Spectroscopic QUasar Extractor and redshift (z) EstimatorSQUEzE I: Methodology

Abstract

We present SQUEzE, a software package to classify quasar spectra and estimate their redshifts. SQUEzE is a random forest classifier operating on the parameters of candidate emission peaks identified in the spectra. We test the performance of the algorithm using visually inspected data from BOSS as a truth table. Only 4\% of the sample ($\sim$6,800 quasars and $\sim$11,520 contaminants) is needed for converged training in recommended choices of the confidence threshold ($0.2<p_{\min }<0.7$). For an operational mode which balances purity and completeness ($p_{\min }=0.28$) we recover a purity of $96.81\pm 0.39\%$ ($99.30\pm 0.14\%$ for quasars with $z\ge 2.1$) and a completeness of $96.83\pm 0.30\%$ ($98.42\pm 0.15\%$ for quasars with $z\ge 2.1$). SQUEzE can be used to obtain a $\approx$100\% pure sample of $z\ge 2.1$. quasars (with $\approx$96\% completeness) by using a confidence threshold of $p_{\min }=0.7$. The estimated redshift error is $1,500km/s$ and we recommend that SQUEzE be used in conjunction with an additional step of redshift tuning to achieve maximum precision. We find that SQUEzE achieves the necessary performance to replace visual inspection in BOSS-like spectroscopic surveys of quasars with subsequent publications in this series exploring expectations for future surveys and alternative methods. Keywords: cosmology: observations - quasar: emission lines - quasar: absorption lines

11 pages, 9 figures, 5 tables, accepted to MNRAS

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• The miniJPAS survey quasar selection IV: Classification and redshift estimation with SQUEzE
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