Preliminary results of using k-Nearest Neighbours Regression to estimate the redshift of radio selected datasets

Abstract

In the near future, all-sky radio surveys are set to produce catalogues of tens of millions of sources with limited multi-wavelength photometry. Spectroscopic redshifts will only be possible for a small fraction of these new-found sources. In this paper, we provide the first in-depth investigation into the use of k-Nearest Neighbours Regression for the estimation of redshift of these sources. We use the Australia Telescope Large Area Survey radio data, combined with the Spitzer Wide-Area Infrared Extragalactic Survey infra-red, the Dark Energy Survey optical and the Australian Dark Energy Survey spectroscopic survey data. We then reduce the depth of photometry to match what is expected from upcoming Evolutionary Map of the Universe survey, testing against both data sets. To examine the generalisation of our methods, we test one of the sub-fields of Australia Telescope Large Area Survey against the other. We achieve an outlier rate of ~10% across all tests, showing that the k-Nearest Neighbours regression algorithm is an acceptable method of estimating redshift, and would perform better given a sample training set with uniform redshift coverage.

arXiv comment7 pages, 12 figures, submitted to the Publications of the Astronomical Society of the Pacific special edition "Focus on Machine Intelligence in Astronomy and Astrophysics"

References in corpus (26)

• SWIRE: The SIRTF Wide-area InfraRed Extragalactic Survey
• ANNz: estimating photometric redshifts using artificial neural networks
• EMU: Evolutionary Map of the Universe
• SkyMapper Southern Survey: First Data Release (DR1)
• Dissecting Photometric redshift for Active Galactic Nuclei using XMM- and Chandra-COSMOS samples
• Estimating photometric redshifts with artificial neural networks
• ANNz2 - photometric redshift and probability distribution function estimation using machine learning
• Deep ATLAS radio observations of the CDFS-SWIRE field
• OzDES multi-fibre spectroscopy for the Dark Energy Survey: first-year operation and results
• OzDES multifibre spectroscopy for the Dark Energy Survey: Three year results and first data release
• The Taipan Galaxy Survey: Scientific Goals and Observing Strategy
• Photometric redshifts for Quasars in multi band Surveys
• Photometric redshifts for the next generation of deep radio continuum surveys - I: Template fitting
• METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts
• ATLAS - I. Third Release of 1.4 GHz Mosaics and Component Catalogues
• Extragalactic Radio Continuum Surveys and the Transformation of Radio Astronomy
• Deep learning Approach for Classifying, Detecting and Predicting Photometric Redshifts of Quasars in the Sloan Digital Sky Survey Stripe 82
• Neural Networks and Photometric Redshifts
• Photometric redshift estimation based on data mining with PhotoRApToR
• Photometric redshifts with Quasi Newton Algorithm (MLPQNA). Results in the PHAT1 contest
• The Wide Area VISTA Extra-galactic Survey (WAVES)
• Stacking for machine learning redshifts applied to SDSS galaxies
• Finding New High-Redshift Quasars by Asking the Neighbours
• A Comparison of Photometric Redshift Techniques for Large Radio Surveys
• Estimating Photometric Redshifts for X-ray sources in the X-ATLAS field, using machine-learning techniques
• Estimating Spectroscopic Redshifts by Using k Nearest Neighbors Regression I. Description of Method and Analysis

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• QSO photometric redshifts using machine learning and neural networks
• Probing $\Lambda$CDM cosmology with the Evolutionary Map of the Universe survey
• QSO photometric redshifts from SDSS, WISE and GALEX colours
• PL-kNN: A Parameterless Nearest Neighbors Classifier
• PICZL: Image-based Photometric Redshifts for AGN
• Selection of powerful radio galaxies with machine learning
• Quasar photometric redshifts from incomplete data using Deep Learning
• Photometric Redshift Estimation Using Scaled Ensemble Learning