1. A Comparison of Photometric Redshift Techniques for Large Radio Surveys
- Author
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James E. Geach, Giuseppe Longo, Kai Lars Polsterer, Ray P. Norris, Kieran J. Luken, Stefano Cavuoti, Vernesa Smolčić, Nick Seymour, Mattia Vaccari, Peter-Christian Zinn, Duncan Farrah, Giuseppe Riccio, Massimo Brescia, Tamás Budavári, Samuel Carliles, A. Musaeva, Mara Salvato, ITA, USA, GBR, DEU, AUS, Norris, R. P., Salvato, M., Longo, G., Brescia, M., Budavari, T., Carliles, S., Cavuoti, S., Farrah, D., Geach, J., Luken, K., Musaeva, A., Polsterer, K., Riccio, G., Seymour, N., Smolcic, V., Vaccari, M., and Zinn, P.
- Subjects
techniques: photometric ,Galaxies ,radio sources ,Training set ,010504 meteorology & atmospheric sciences ,Computer science ,FOS: Physical sciences ,Astronomy and Astrophysics ,Astrophysics::Cosmology and Extragalactic Astrophysics ,Astrophysics ,01 natural sciences ,Galaxie ,Redshift ,Photometry (optics) ,Radio source ,Space and Planetary Science ,0103 physical sciences ,Astrophysics - Instrumentation and Methods for Astrophysics ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,010303 astronomy & astrophysics ,Techniques: photometric ,0105 earth and related environmental sciences ,Photometric redshift - Abstract
Future radio surveys will generate catalogues of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating 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 special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multi-wavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best with high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts., Comment: Submitted to PASP
- Published
- 2019
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