QSO photometric redshifts from SDSS, WISE and GALEX colours. (arXiv:2001.06514v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Curran_S/0/1/0/all/0/1">S. J. Curran</a>
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.
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.
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