Use of neural networks for the identification of new z>=3.6 QSOs from FIRST-SDSS DR5

Abstract

We aim to obtain a complete sample of redshift > 3.6 radio QSOs from FIRST sources having star-like counterparts in the SDSS DR5 photometric survey (r<=20.2). We found that simple supervised neural networks, trained on sources with SDSS spectra, and using optical photometry and radio data, are very effective for identifying high-z QSOs without spectra. The technique yields a completeness of 96 per cent and an efficiency of 62 per cent. Applying the trained networks to 4415 sources without DR5 spectra we found 58 z>=3.6 QSO candidates. We obtained spectra of 27 of them, and 17 are confirmed as high-z QSOs. Spectra of 13 additional candidates from the literature and from SDSS DR6 revealed 7 more z>=3.6 QSOs, giving and overall efficiency of 60 per cent. None of the non-candidates with spectra from NED or DR6 is a z>=3.6 QSO, consistently with a high completeness. The initial sample of z>=3.6 QSOs is increased from 52 to 76, i.e. by a factor 1.46. From the new identifications and candidates we estimate an incompleteness of SDSS for the spectroscopic classification of FIRST 3.6<=z<=4.6 QSOs of 15 percent for r<=20.2.

arXiv comment16 pages, 9 figures accepted for publication in MNRAS

References in corpus (34)

• Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds
• The Sloan Digital Sky Survey: Technical Summary
• The Demography of Massive Dark Objects in Galaxy Centres
• Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Quasar Sample
• The SDSS Quasar Survey: Quasar Luminosity Function from Data Release Three
• The Fifth Data Release of the Sloan Digital Sky Survey
• The 2dF QSO Redshift Survey - XII. The spectroscopic catalogue and luminosity function
• ANNz: estimating photometric redshifts using artificial neural networks
• The Sloan Digital Sky Survey Quasar Catalog IV. Fifth Data Release
• Efficient Photometric Selection of Quasars from the Sloan Digital Sky Survey: II. ~1,000,000 Quasars from Data Release Six
• The Radio-Loud Fraction of Quasars is a Strong Function of Redshift and Optical Luminosity
• Efficient Photometric Selection of Quasars from the Sloan Digital Sky Survey: 100,000 z<3 Quasars from Data Release One
• Estimating photometric redshifts with artificial neural networks
• Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks
• Conference Summary: AGN Physics with the Sloan Digital Sky Survey
• An Artificial Neural Network Approach to Classification of Galaxy Spectra
• Automated Classification of Stellar Spectra. II: Two-Dimensional Classification with Neural Networks and Principal Components Analysis
• Neural computation as a tool for galaxy classification : methods and examples
• Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees
• Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
• An Empirical Algorithm for Broad-band Photometric Redshifts of Quasars from the Sloan Digital Sky Survey
• Double Lobed Radio Quasars from the Sloan Digital Sky Survey
• Robust Machine Learning Applied to Astronomical Datasets II: Quantifying Photometric Redshifts for Quasars Using Instance-Based Learning
• A Census of Object Types and Redshift Estimates in the SDSS Photometric Catalog from a Trained Decision-Tree Classifier
• Discovery of Radio-loud Quasars with Redshifts above 4 from the PMN sample
• Decline of the space density of quasars between z=2 and z=4
• A search for distant radio-loud quasars in the CLASS survey: three new radio-selected quasars at z>4
• Applying Machine Learning to Catalogue Matching in Astrophysics
• On the radio properties of the highest redshift quasars
• High-redshift QSOs in the FIRST survey
• A sample of radio-loud QSOs at redshift ~ 4
• A FIRST-APM-SDSS survey for high-redshift radio QSOs
• Selection of quasar candidates from combined radio and optical surveys using neural networks
• Objective Subclass Determination of Sloan Digital Sky Survey Unknown Spectral Objects

Cited by in corpus (14)

• Data Mining and Machine Learning in Astronomy
• Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy
• BayeSED: A General Approach to Fitting the Spectral Energy Distribution of Galaxies
• Decoding spectral energy distributions of dust-obscured starburst-AGN
• Selecting Quasar Candidates by a SVM Classification System
• Radio-Selected Quasars in the Sloan Digital Sky Survey
• Active galactic nuclei synapses: X-ray versus optical classifications using artificial neural networks
• Classification of field dwarfs and giants in RAVE and its use in stellar stream detection
• Broad-band spectral energy distribution of 3000 Angstroem break quasars from the Sloan Digital Sky Survey
• Neural-network selection of high-redshift radio quasars, and the luminosity function at z~4
• A multi-wavelength continuum characterization of high-redshift broad absorption line quasars
• Exploring local fNL estimators based on the binned bispectrum
• Constraints on fNL from Wilkinson Microwave Anisotropy Probe 7-year data using a neural network classifier
• Identifying AGN host galaxies by Machine Learning with HSC+WISE