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Degradation analysis in the estimation of photometric redshifts from non-representative training sets

Rivera, J. D. and Moraes, B. and Merson, A. I. and Jouvel, S. and Abdalla, F. B. and Abdalla, M. C. B. (2018) Degradation analysis in the estimation of photometric redshifts from non-representative training sets. Monthly Notices of the Royal Astronomical Society, 477 (4). pp. 4330-4347. ISSN 0035-8711. http://resolver.caltech.edu/CaltechAUTHORS:20180705-140617558

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Abstract

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations and in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, using either magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/sty880DOIArticle
http://arxiv.org/abs/1804.03805arXivDiscussion Paper
Additional Information:© 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) Accepted 2018 April 3. Received 2018 March 28; in original form 2017 November 20. JDR thanks Coordenação de aperfeiçoamento de pessoal de nivel superior (CAPES) for financial support. MCBA also thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for partial support. This work was supported by the Science and Technology Facilities Council (ST/J501013/1, ST/L00075X/1). This work used the DiRAC Data Centric system at DurhamUniversity, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, and STFC DiRAC Operations grant ST/K003267/1, and Durham University. DiRAC is part of the National E-Infrastructure. FBA acknowledges the support of the Royal Society via an RSURF. BM acknowledges support from the European Community through the DEDALE grant (contract no. 665044) within the H2020 Framework Program of the European Commission.
Group:Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)UNSPECIFIED
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)UNSPECIFIED
Science and Technology Facilities Council (STFC)ST/J501013/1
Science and Technology Facilities Council (STFC)ST/L00075X/1
Science and Technology Facilities Council (STFC)ST/K00042X/1
Science and Technology Facilities Council (STFC)ST/H008519/1
Science and Technology Facilities Council (STFC)ST/K003267/1
Durham UniversityUNSPECIFIED
Royal SocietyUNSPECIFIED
European Research Council (ERC)665044
Subject Keywords:methods: data analysis, galaxies: distances and redshifts
Record Number:CaltechAUTHORS:20180705-140617558
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180705-140617558
Official Citation:J D Rivera, B Moraes, A I Merson, S Jouvel, F B Abdalla, M C B Abdalla; Degradation analysis in the estimation of photometric redshifts from non-representative training sets, Monthly Notices of the Royal Astronomical Society, Volume 477, Issue 4, 11 July 2018, Pages 4330–4347, https://doi.org/10.1093/mnras/sty880
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87560
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:06 Jul 2018 14:59
Last Modified:06 Jul 2018 14:59

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