English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Future radio surveys will generate catalogues of tens of millions of radiosources, for which redshift estimates will be essential to achieve many of thescience goals. However, spectroscopic data will be available for only a smallfraction of these sources, and in most cases even the optical and infraredphotometry will be of limited quality. Furthermore, radio sources tend to be athigher redshift than most optical sources and so a significant fraction ofradio sources hosts differ from those for which most photometric redshifttemplates are designed. We therefore need to develop new techniques forestimating the redshifts of radio sources. As a starting point in this process,we evaluate a number of machine-learning techniques for estimating redshift,together with a conventional template-fitting technique. We pay specialattention to how the performance is affected by the incompleteness of thetraining sample and by sparseness of the parameter space or by limitedavailability of ancillary multi-wavelength data. As expected, we find that thequality of the photometric-redshift degrades as the quality of the photometrydecreases, but that even with the limited quality of photometry available forall sky-surveys, useful redshift information is available for the majority ofsources, particularly at low redshift. We find that a template-fittingtechnique performs best with high-quality and almost complete multi-bandphotometry, especially if radio sources that are also X-ray emitting aretreated separately. When we reduced the quality of photometry to match thatavailable for the EMU all-sky radio survey, the quality of the template-fittingdegraded and became comparable to some of the machine learning methods. Machinelearning techniques currently perform better at low redshift than at highredshift, because of incompleteness of the currently available training data athigh redshifts.

A Comparison of Photometric Redshift Techniques for Large Radio Surveys