Year: 2019
Type: article
Abstract: Future radio surveys will generate catalogs 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 (most radio surveys have a median redshift greater than 1) 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 multiwavelength 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 in the presence of high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately, using specific templates and priors. 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.
Authors: Ray P. Norris, M. Salvato, G. Longo, M. Brescia, T. Budavari, S. Carliles, S. Cavuoti, D. Farrah, J. Geach, K. Luken, A. Musaeva, K. Polsterer, G. Riccio, N. Seymour, V. Smolčić, M. Vaccari, P. Zinn
Institutions: Commonwealth Scientific and Industrial Research Organisation, Western Sydney University, Max Planck Institute for Extraterrestrial Physics, California Institute of Technology, University of Naples Federico II, Astronomical Observatory of Capodimonte, Johns Hopkins University, Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, University of Hawaiʻi at Mānoa, University of Hawaii System, University of Hertfordshire, The University of Sydney, ARC Centre of Excellence for All-sky Astrophysics, Heidelberg Institute for Theoretical Studies, Curtin University, University of Zagreb, Istituto di Radioastronomia di Bologna, University of the Western Cape, Ruhr University Bochum
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Award: G7894511721
OpenAlex ID: W2914881586