Random Forests as a viable method to select and discover high redshift quasars

Abstract

We present a method of selecting quasars up to redshift $\approx$ 6 with random forests, a supervised machine learning method, applied to Pan-STARRS1 and WISE data. We find that, thanks to the increasing set of known quasars we can assemble a training set that enables supervised machine learning algorithms to become a competitive alternative to other methods up to this redshift. We present a candidate set for the redshift range 4.8 to 6.3 which includes the region around z = 5.5 where quasars are difficult to select due to photometric similarity to red and brown dwarfs. We demonstrate that under our survey restrictions we can reach a high completeness ($66\pm 7\%$ below redshift 5.6 / $83_{-9}^{+6}\%$ above redshift 5.6) while maintaining a high selection efficiency ($78_{-8}^{+10}\%$ / $94_{-8}^{+5}\%$). Our selection efficiency is estimated via a novel method based on the different distributions of quasars and contaminants on the sky. The final catalog of 515 candidates includes 225 known quasars. We predict the candidate catalog to contain an additional $148_{-33}^{+41}$ new quasars below redshift 5.6 and $45_{-8}^{+5}$ above and make the catalog publicly available. Spectroscopic follow-up observations of 37 candidates lead us to discover 20 new high redshift quasars (18 at $4.6\le z\le 5.5$, 2 $z\sim 5.7$). These observations are consistent with our predictions on efficiency. We argue that random forests can lead to higher completeness because our candidate set contains a number of objects that would be rejected by common color cuts, including one of the newly discovered redshift 5.7 quasars.

arXiv commentAccepted by AJ

References in corpus (52)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds
• Astropy: A Community Python Package for Astronomy
• The NumPy array: a structure for efficient numerical computation
• The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance
• Measuring Reddening with SDSS Stellar Spectra and Recalibrating SFD
• Binary companions of evolved stars in APOGEE DR14: Search method and catalog of ~5,000 companions
• The Baryon Oscillation Spectroscopic Survey of SDSS-III
• Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Quasar Sample
• Evidence for Reionization at z ~ 6: Detection of a Gunn-Peterson Trough in a z=6.28 Quasar
• Preliminary Results from NEOWISE: An Enhancement to the Wide-field Infrared Survey Explorer for Solar System Science
• The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Overview and Early Data
• astroquery: An Astronomical Web-Querying Package in Python
• Observational constraints on Cosmic Reionization
• The 2dF QSO Redshift Survey - XII. The spectroscopic catalogue and luminosity function
• The 2dF QSO Redshift Survey - I. The Optical QSO Luminosity Function
• The Sloan Digital Sky Survey Quasar Catalog: Sixteenth Data Release
• The Canada-France High-z Quasar Survey: nine new quasars and the luminosity function at redshift 6
• Model-independent evidence in favor of an end to reionization by z~6
• The Pan-STARRS1 distant z>5.6 quasar survey: more than 100 quasars within the first Gyr of the universe
• Efficient Photometric Selection of Quasars from the Sloan Digital Sky Survey: II. ~1,000,000 Quasars from Data Release Six
• The Final SDSS High-Redshift Quasar Sample of 52 Quasars at z>5.7
• The Formation and Evolution of Massive Black Holes
• Discovery of three z>6.5 quasars in the VISTA Kilo-degree Infrared Galaxy (VIKING) survey
• The Discovery of a Luminous z=5.80 Quasar from the Sloan Digital Sky Survey
• The z=5 Quasar Luminosity Function from SDSS Stripe 82
• Subaru High-z Exploration of Low-Luminosity Quasars (SHELLQs). V. Quasar Luminosity Function and Contribution to Cosmic Reionization at z = 6
• Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog
• The SDSS-III Baryon Oscillation Spectroscopic Survey: The Quasar Luminosity Function from Data Release Nine
• A Survey of z~6 Quasars in the SDSS Deep Stripe: I. a Flux-Limited Sample at z_{AB}<21
• Exploring Reionization-Era Quasars III: Discovery of 16 Quasars at $6.4\lesssim z\lesssim 6.9$ with DESI Legacy Imaging Surveys and UKIRT Hemisphere Survey and Quasar Luminosity Function at $z\sim 6.7$
• Subaru High-z Exploration of Low-Luminosity Quasars (SHELLQs). II. Discovery of 32 Quasars and Luminous Galaxies at 5.7 < z < 6.8
• Eight new luminous z > 6 quasars selected via SED model fitting of VISTA, WISE and Dark Energy Survey Year 1 Observations
• Constraining the radio-loud fraction of quasars at z>5.5
• The Subaru high-z quasar survey: discovery of faint z~6 quasars
• A survey of luminous high-redshift quasars with SDSS and WISE. I. target selection and optical spectroscopy
• Variability selected high-redshift quasars on SDSS Stripe 82
• The Mid-IR and X-ray Selected QSO Luminosity Function
• Preliminary Target Selection for the DESI Quasar (QSO) Sample
• A powerful radio-loud quasar at the end of cosmic reionization
• Quasar and galaxy classification in Gaia Data Release 2
• Probabilistic selection of high-redshift quasars
• KiDS-SQuaD II: Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars
• Discovery of 16 New z ~ 5.5 Quasars : Filling in the Redshift Gap of Quasar Color Selection
• Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning
• The Extremely Luminous Quasar Survey (ELQS) in the SDSS footprint I.: Infrared Based Candidate Selection
• Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4
• Catalog of quasars from the Kilo-Degree Survey Data Release 3
• Radio-Selected Quasars in the Sloan Digital Sky Survey
• The Extremely Luminous Quasar Survey in the Pan-STARRS 1 Footprint (PS-ELQS)
• Discovery of two quasars at $z=5$ from the OGLE Survey

Cited by in corpus (20)

• The X-shooter/ALMA Sample of Quasars in the Epoch of Reionization. II. Black Hole Masses, Eddington Ratios, and the Formation of the First Quasars
• Discovery of 24 radio-bright quasars at $4.9\le z\le 6.6$ using low-frequency radio observations
• The Pan-STARRS1 $\mathbf{z}>5.6$ Quasar Survey: III. The $\mathbf{z}\approx 6$ Quasar Luminosity Function
• The space density of ultra-luminous QSOs at the end of reionization epoch by the QUBRICS Survey and the AGN contribution to the hydrogen ionizing background
• CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
• Paving the Way for Euclid and JWST via Optimal Selection of High-z Quasars
• ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
• Discovery and characterization of 25 new quasars at 4.6 < z < 6.9 from wide-field multi-band surveys
• The host galaxies of radio-loud quasars at z>5 with ALMA
• When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data
• A New Mid-Infrared and X-ray Machine Learning Algorithm to Discover Compton-thick AGN
• High-$z$ radio Quasars in RACS I: Selection, identification, and multi-wavelength properties
• The probabilistic random forest applied to the QUBRICS survey: improving the selection of high-redshift quasars with synthetic data
• Euclid: The potential of slitless infrared spectroscopy: A z=5.4 quasar and new ultracool dwarfs
• Automated algorithms to build Active Galactic Nuclei classifiers
• Photometric classification of QSOs from ALHAMBRA survey using random forest
• QUEST (Quasar Unsupervised Encoder and Synthesis Tool): A machine learning framework to generate quasar spectra
• ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
• 4MOST ChANGES: Catalog of high-redshift quasar candidates (4.5 < $z$ < 7) selected with SED fitting
• Ultra-luminous high-redshift quasars from SkyMapper -- II. New quasars and the bright end of the luminosity function