Title: QSO photometric redshifts from SDSS, WISE, and GALEX colours

Abstract

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, Δ$z$, 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 in Δ$z$ 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 in Δ$z$ 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 per cent) 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.

Authors:

Curran, S. J. ^[1]

---

+ Show Author Affiliations

1. School of Chemical and Physical Sciences, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand

Publication Date:

2020-01-21

Sponsoring Org.:

USDOE

OSTI Identifier:

1597544

Resource Type:

Published Article

Journal Name:

Monthly Notices of the Royal Astronomical Society: Letters

Additional Journal Information:

Journal Name: Monthly Notices of the Royal Astronomical Society: Letters Journal Volume: 493 Journal Issue: 1; Journal ID: ISSN 1745-3925

Publisher:

Oxford University Press

Country of Publication:

United Kingdom

Language:

English