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A Comparison of Photometric Redshift Techniques for Large Radio Surveys

Norris, Ray P. and Salvato, M. and Longo, G. and Brescia, M. and Budavari, T. and Carliles, S. and Cavuoti, S. and Farrah, D. and Geach, J. and Luken, K. and Musaeva, A. and Polsterer, K. and Riccio, G. and Seymour, N. and Smolčić, V. and Vaccari, M. and Zinn, P. (2019) A Comparison of Photometric Redshift Techniques for Large Radio Surveys. Publications of the Astronomical Society of the Pacific, 131 (1004). Art. No. 108004. ISSN 0004-6280. https://resolver.caltech.edu/CaltechAUTHORS:20190919-113941154

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1538-3873/ab0f7bDOIArticle
https://arxiv.org/abs/1902.05188arXivDiscussion Paper
ORCID:
AuthorORCID
Norris, Ray P.0000-0002-4597-1906
Salvato, M.0000-0001-7116-9303
Longo, G.0000-0002-9182-8414
Brescia, M.0000-0001-9506-5680
Cavuoti, S.0000-0002-3787-4196
Farrah, D.0000-0003-1748-2010
Geach, J.0000-0003-4964-4635
Luken, K.0000-0002-6147-693X
Riccio, G.0000-0001-7020-1172
Seymour, N.0000-0003-3506-5536
Smolčić, V.0000-0002-3893-8614
Vaccari, M.0000-0002-6748-0577
Additional Information:© 2019. The Astronomical Society of the Pacific. Received 2018 May 29; accepted 2019 March 12; published 2019 August 30. Focus on Machine Intelligence in Astronomy and Astrophysics. S.C. acknowledges support from the project "Quasars at high redshift: physics and cosmology" financed by the ASI/INAF agreement 2017-14-H.0. M.B. acknowledges the INAF PRIN-SKA 2017 program 1.05.01.88.04 and the funding from MIUR Premiale 2016: MITIC. VS acknowledges support from the European Union's Seventh Frame-work program under grant agreement 337595 (ERC Starting grant, "CoSMass").
Funders:
Funding AgencyGrant Number
Agenzia Spaziale Italiana (ASI)2017-14-H.0
Istituto Nazionale di Astrofisica (INAF)1.05.01.88.04
Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR)UNSPECIFIED
European Research Council (ERC)337595
Subject Keywords:techniques: photometric – Galaxies – radio sources
Issue or Number:1004
Record Number:CaltechAUTHORS:20190919-113941154
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190919-113941154
Official Citation:Ray P. Norris et al 2019 PASP 131 108004
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98744
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:20 Sep 2019 14:30
Last Modified:03 Oct 2019 21:43

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