QSO photometric redshifts from SDSS, WISE and GALEX colours
arXiv:2001.06514 · doi:10.1093/mnrasl/slaa012
Abstract
Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spectroscopic and photometric redshifts is close to zero, the distribution of these differences remains wide and distinctly non-Gaussian. As per our previous empirical estimate of photometric redshifts, we find that the predictions can be significantly improved by adding colours from other wavebands, namely the near-infrared and ultraviolet. Self-testing this, by using half of the 33 643 strong QSO sample to train the algorithm, results in a significantly narrower spread for the remaining half of the sample. Using the whole QSO sample to train the algorithm, the same set of magnitudes return a similar spread for a sample of radio sources (quasars). Although the matching coincidence is relatively low (739 of the 3663 sources having photometry in the relevant bands), this is still significantly larger than from the empirical method (2%) and thus may provide a method with which to obtain redshifts for the vast number of continuum radio sources expected to be detected with the next generation of large radio telescopes.
arXiv commentAccepted by MNRAS
References in corpus (31)
- The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance
- LOFAR: The LOw-Frequency ARray
- The Galaxy Evolution Explorer: A Space Ultraviolet Survey Mission
- The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
- EMU: Evolutionary Map of the Universe
- GALEX-SDSS-WISE Legacy Catalog (GSWLC): Star Formation Rates, Stellar Masses and Dust Attenuations of 700,000 Low-redshift Galaxies
- Science with ASKAP - the Australian Square Kilometre Array Pathfinder
- WISE photometry for 400 million SDSS sources
- Photometric redshifts and quasar probabilities from a single, data-driven generative model
- Photometric Redshifts of Quasars
- A survey of luminous high-redshift quasars with SDSS and WISE. I. target selection and optical spectroscopy
- 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
- Photometric redshifts for Quasars in multi band Surveys
- Photometric redshifts for the next generation of deep radio continuum surveys - I: Template fitting
- AGN Populations in Large Volume X-ray Surveys: Photometric Redshifts and Population Types found in the Stripe 82X Survey
- Bayesian High-Redshift Quasar Classification from Optical and Mid-IR Photometry
- A survey for redshifted molecular and atomic absorption lines I
- The large area KX quasar catalogue: I. Analysis of the photometric redshift selection and the complete quasar catalogue
- Recalibrating the Wide-field Infrared Survey Explorer (WISE) W4 Filter
- Complete ionisation of the neutral gas: why there are so few detections of 21-cm hydrogen in high redshift radio galaxies and quasars
- Quasar Photometric Redshifts and Candidate Selection: A New Algorithm Based on Optical and Mid-Infrared Photometric Data
- A new version of the OCARS catalog of Optical Characteristics of Astrometric Radio Sources
- Ionisation of the atomic gas in redshifted radio sources
- A Comparison of Photometric Redshift Techniques for Large Radio Surveys
- Broadband spectral energy distributions of SDSS-selected quasars and of their host galaxies: intense activity at the onset of AGN feedback
- Preliminary results of using k-Nearest Neighbours Regression to estimate the redshift of radio selected datasets
- Photometric Redshift Estimation for Quasars by Integration of KNN and SVM
- WISE data as a photometric redshift indicator for radio AGN
- QSO redshift estimates from optical, near-infrared and ultraviolet colours
- Ultra-violet photo-ionisation in far-infrared selected sources
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- Predicting the redshift of gamma-ray loud AGNs using Supervised Machine Learning: Part 2
- Predicting the redshift of gamma-ray loud AGNs using supervised machine learning
- Mixture Models for Photometric Redshifts
- Intervening or associated? Machine learning classification of redshifted H I 21-cm absorption
- Quasar photometric redshifts from incomplete data using Deep Learning
- Redshifts of radio sources in the Million Quasars Catalogue from machine learning