Desprez, G. and Paltani, S. and Coupon, J. and Almosallam, I. and Alvarez-Ayllon, A. and Amaro, V. and Brescia, M. and Brodwin, M. and Cavuoti, S. and De Vicente-Albendea, J. and Fotopoulou, S. and Hatfield, P. W. and Hartley, W. G. and Ilbert, O. and Jarvis, M. J. and Longo, G. and Rau, M. M. and Saha, R. and Speagle, J. S. and Tramacere, A. and Castellano, M. and Dubath, F. and Galametz, A. and Kuemmel, M. and Laigle, C. and Merlin, E. and Mohr, J. J. and Pilo, S. and Salvato, M. and Andreon, S. and Auricchio, N. and Baccigalupi, C. and Balaguera-Antolínez, A. and Baldi, M. and Bardelli, S. and Bender, R. and Biviano, A. and Bodendorf, C. and Bonino, D. and Bozzo, E. and Branchini, E. and Brinchmann, J. and Burigana, C. and Cabanac, R. and Camera, S. and Capobianco, V. and Cappi, A. and Carbone, C. and Carretero, J. and Carvalho, C. S. and Casas, R. and Casas, S. and Castander, F. J. and Castignani, G. and Cimatti, A. and Cledassou, R. and Colodro-Conde, C. and Congedo, G. and Conselice, C. J. and Conversi, L. and Copin, Y. and Corcione, L. and Courtois, H. M. and Cuby, J.-G. and Da Silva, A. and de la Torre, S. and Degaudenzi, H. and Di Ferdinando, D. and Douspis, M. and Duncan, C. A. J. and Dupac, X. and Ealet, A. and Fabbian, G. and Fabricius, M. and Farrens, S. and Ferreira, P. G. and Finelli, F. and Fosalba, P. and Fourmanoit, N. and Frailis, M. and Franceschi, E. and Fumana, M. and Galeotta, S. and Garilli, B. and Gillard, W. and Gillis, B. and Giocoli, C. and Gozaliasl, G. and Graciá-Carpio, J. and Grupp, F. and Guzzo, L. and Hailey, M. and Haugan, S. V. H. and Holmes, W. and Hormuth, F. and Humphrey, A. and Jahnke, K. and Keihanen, E. and Kermiche, S. and Kilbinger, M. and Kirkpatrick, C. C. and Kitching, T. D. and Kohley, R. and Kubik, B. and Kunz, M. and Kurki-Suonio, H. and Ligori, S. and Lilje, P. B. and Lloro, I. and Maino, D. and Maiorano, E. and Marggraf, O. and Markovic, K. and Martinet, N. and Marulli, F. and Massey, R. and Maturi, M. and Mauri, N. and Maurogordato, S. and Medinaceli, E. and Mei, S. and Meneghetti, M. and Benton Metcalf, R. and Meylan, G. and Moresco, M. and Moscardini, L. and Munari, E. and Niemi, S. and Padilla, C. and Pasian, F. and Patrizii, L. and Pettorino, V. and Pires, S. and Polenta, G. and Poncet, M. and Popa, L. and Potter, D. and Pozzetti, L. and Raison, F. and Renzi, A. and Rhodes, J. and Riccio, G. and Rossetti, E. and Saglia, R. and Sapone, D. and Schneider, P. and Scottez, V. and Secroun, A. and Serrano, S. and Sirignano, C. and Sirri, G. and Stanco, L. and Stern, D. and Sureau, F. and Tallada Crespí, P. and Tavagnacco, D. and Taylor, A. N. and Tenti, M. and Tereno, I. and Toledo-Moreo, R. and Torradeflot, F. and Valenziano, L. and Valiviita, J. and Vassallo, T. and Viel, M. and Wang, Y. and Welikala, N. and Whittaker, L. and Zacchei, A. and Zamorani, G. and Zoubian, J. and Zucca, E. (2020) Euclid preparation. X. The Euclid photometric-redshift challenge. Astronomy and Astrophysics, 644 . Art. No. A31. ISSN 0004-6361. doi:10.1051/0004-6361/202039403. https://resolver.caltech.edu/CaltechAUTHORS:20201130-085817233
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Abstract
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.
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| 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 1.05.01.88.04. 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). | ||||||||||||||||||||||||||||||||||||||||||||
| Group: | Infrared Processing and Analysis Center (IPAC) | ||||||||||||||||||||||||||||||||||||||||||||
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| Subject Keywords: | galaxies: distances and redshifts – surveys – techniques: miscellaneous – catalogs | ||||||||||||||||||||||||||||||||||||||||||||
| DOI: | 10.1051/0004-6361/202039403 | ||||||||||||||||||||||||||||||||||||||||||||
| Record Number: | CaltechAUTHORS:20201130-085817233 | ||||||||||||||||||||||||||||||||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20201130-085817233 | ||||||||||||||||||||||||||||||||||||||||||||
| Official Citation: | Euclid preparation - X. The Euclid photometric-redshift challenge. Euclid Collaboration, G. Desprez, et. al., A&A, 644 (2020) A31; DOI: https://doi.org/10.1051/0004-6361/202039403 | ||||||||||||||||||||||||||||||||||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||||||||||||||||||||||||||||||||
| ID Code: | 106840 | ||||||||||||||||||||||||||||||||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||||||||||||||||||||||||||||||||
| Deposited By: | Tony Diaz | ||||||||||||||||||||||||||||||||||||||||||||
| Deposited On: | 02 Dec 2020 20:23 | ||||||||||||||||||||||||||||||||||||||||||||
| Last Modified: | 16 Nov 2021 18:57 |
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