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Published December 2020 | Accepted Version + Published
Journal Article Open

Euclid preparation. X. The Euclid photometric-redshift challenge

Desprez, G.
Paltani, S.
Coupon, J.
Almosallam, I.
Alvarez-Ayllon, A.
Amaro, V.
Brescia, M.
Brodwin, M.
Cavuoti, S.
De Vicente-Albendea, J.
Fotopoulou, S.
Hatfield, P. W.
Hartley, W. G.
Ilbert, O.
Jarvis, M. J.
Longo, G.
Rau, M. M.
Saha, R.
Speagle, J. S.
Tramacere, A.
Castellano, M.
Dubath, F.
Galametz, A.
Kuemmel, M.
Laigle, C.
Merlin, E.
Mohr, J. J.
Pilo, S.
Salvato, M.
Andreon, S.
Auricchio, N.
Baccigalupi, C.
Balaguera-Antolínez, A.
Baldi, M.
Bardelli, S.
Bender, R.
Biviano, A.
Bodendorf, C.
Bonino, D.
Bozzo, E.
Branchini, E.
Brinchmann, J.
Burigana, C.
Cabanac, R.
Camera, S.
Capobianco, V.
Cappi, A.
Carbone, C.
Carretero, J.
Carvalho, C. S.
Casas, R.
Casas, S.
Castander, F. J.
Castignani, G.
Cimatti, A.
Cledassou, R.
Colodro-Conde, C.
Congedo, G.
Conselice, C. J.
Conversi, L.
Copin, Y.
Corcione, L.
Courtois, H. M.
Cuby, J.-G.
Da Silva, A.
de la Torre, S.
Degaudenzi, H.
Di Ferdinando, D.
Douspis, M.
Duncan, C. A. J.
Dupac, X.
Ealet, A.
Fabbian, G.
Fabricius, M.
Farrens, S.
Ferreira, P. G.
Finelli, F.
Fosalba, P.
Fourmanoit, N.
Frailis, M.
Franceschi, E.
Fumana, M.
Galeotta, S.
Garilli, B.
Gillard, W.
Gillis, B.
Giocoli, C.
Gozaliasl, G.
Graciá-Carpio, J.
Grupp, F.
Guzzo, L.
Hailey, M.
Haugan, S. V. H.
Holmes, W.
Hormuth, F.
Humphrey, A.
Jahnke, K.
Keihanen, E.
Kermiche, S.
Kilbinger, M.
Kirkpatrick, C. C.
Kitching, T. D.
Kohley, R.
Kubik, B.
Kunz, M.
Kurki-Suonio, H.
Ligori, S.
Lilje, P. B.
Lloro, I.
Maino, D.
Maiorano, E.
Marggraf, O.
Markovic, K.
Martinet, N.
Marulli, F.
Massey, R.
Maturi, M.
Mauri, N.
Maurogordato, S.
Medinaceli, E.
Mei, S.
Meneghetti, M.
Benton Metcalf, R.
Meylan, G.
Moresco, M.
Moscardini, L.
Munari, E.
Niemi, S.
Padilla, C.
Pasian, F.
Patrizii, L.
Pettorino, V.
Pires, S.
Polenta, G.
Poncet, M.
Popa, L.
Potter, D.
Pozzetti, L.
Raison, F.
Renzi, A.
Rhodes, J. ORCID icon
Riccio, G.
Rossetti, E.
Saglia, R.
Sapone, D.
Schneider, P.
Scottez, V.
Secroun, A.
Serrano, S.
Sirignano, C.
Sirri, G.
Stanco, L.
Stern, D. ORCID icon
Sureau, F.
Tallada Crespí, P.
Tavagnacco, D.
Taylor, A. N.
Tenti, M.
Tereno, I.
Toledo-Moreo, R.
Torradeflot, F.
Valenziano, L.
Valiviita, J.
Vassallo, T.
Viel, M.
Wang, Y.
Welikala, N.
Whittaker, L.
Zacchei, A.
Zamorani, G.
Zoubian, J.
Zucca, E.
Euclid Collaboration


Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2 − 2.6 redshift range that the Euclid mission will probe. We designed a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-z deviates by more than 0.15(1 + z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for example z >  1. However they generally perform better than template-fitting methods at low redshift (z <  0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-z precision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear from Euclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority.

Additional Information

© ESO 2020. Article published by EDP Sciences. Received 11 September 2020; Accepted 20 October 2020; Published online 25 November 2020. GD thanks Douglas Scott for his very helpful comments on the manuscript. GD and AG acknowledge the support from the Sinergia program of the Swiss National Science Foundation. Part of this work was supported by the German Deutsche Forschungsgemeinschaft, DFG project number Ts 17/2–1. MB acknowledges the financial contribution from the agreement ASI/INAF 2018-23-HH.0, Euclid ESA mission – Phase D and the INAF PRIN-SKA 2017 program SC acknowledges the financial contribution from FFABR 2017. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the Centre National d'Etudes Spatiales, the Deutsches Zentrum für Luft- und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid website (http://www.euclid-ec.org).

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Accepted Version - 2009.12112.pdf


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