Euclid preparation. X. The Euclid photometric-redshift challenge

G. Desprez , S. Paltani , J. Coupon , I. Almosallam , A. Alvarez-Ayllon , V. Amaro , M. Brescia , M. Brodwin , S. Cavuoti , J. de Vicente-Albendea , S. Fotopoulou , P. W. Hatfield , W. G. Hartley , O. Ilbert (1) , M. J. Jarvis , G. Longo , R. Saha , J. S. Speagle , A. Tramacere , M. Castellano , F. Dubath , A. Galametz , M. Kuemmel , C. Laigle (2) , E. Merlin , J. J. Mohr , S. Pilo , M. Salvato , M. M. Rau , S. Andreon , N. Auricchio , C. Baccigalupi , A. Balaguera-Antolínez , M. Baldi , S. Bardelli , R. Bender , A. Biviano , C. Bodendorf , D. Bonino , E. Bozzo , E. Branchini , J. Brinchmann , C. Burigana , R. Cabanac (3) , S. Camera , V. Capobianco , A. Cappi (4) , C. Carbone , J. Carretero , C. S. Carvalho , R. Casas , S. Casas (5) , F. J. Castander , G. Castignani , A. Cimatti , R. Cledassou (6) , C. Colodro-Conde , G. Congedo , C. J. Conselice , L. Conversi , Y. Copin (7) , L. Corcione , H. M. Courtois (7) , J. -G. Cuby (1) , A. da Silva , S. de La Torre (1) , H. Degaudenzi , D. Di Ferdinando , M. Douspis (8) , C. A. J. Duncan , X. Dupac , A. Ealet (7) , G. Fabbian , M. Fabricius , S. Farrens (5) , P. G. Ferreira , F. Finelli , P. Fosalba , N. Fourmanoit (7) , M. Frailis , E. Franceschi , M. Fumana , S. Galeotta , B. Garilli , W. Gillard (9) , B. Gillis , C. Giocoli , G. Gozaliasl , J. Graciá-Carpio , F. Grupp , L. Guzzo , M. Hailey , S. V. H. Haugan , W. Holmes , F. Hormuth , A. Humphrey , K. Jahnke , E. Keihanen , S. Kermiche (9) , M. Kilbinger (5, 2) , C. C. Kirkpatrick , T. D. Kitching , R. Kohley , B. Kubik (7) , M. Kunz , H. Kurki-Suonio , S. Ligori , P. B. Lilje , I. Lloro , D. Maino , E. Maiorano , O. Marggraf , K. Markovic , N. Martinet (1) , F. Marulli , R. Massey , M. Maturi , N. Mauri , S. Maurogordato (4) , E. Medinaceli , S. Mei (10) , M. Meneghetti , R. Benton Metcalf , G. Meylan , M. Moresco , L. Moscardini , E. Munari , S. Niemi , C. Padilla , F. Pasian , L. Patrizii , V. Pettorino (5) , S. Pires (5) , G. Polenta , M. Poncet (6) , L. Popa , D. Potter , L. Pozzetti , F. Raison , A. Renzi , J. Rhodes , G. Riccio , E. Rossetti , R. Saglia , D. Sapone , P. Schneider , V. Scottez (2) , A. Secroun (9) , S. Serrano , C. Sirignano , G. Sirri , L. Stanco , D. Stern , F. Sureau (5) , P. Tallada Crespí , D. Tavagnacco , A. N. Taylor , M. Tenti , I. Tereno , R. Toledo-Moreo , F. Torradeflot , L. Valenziano , J. Valiviita , T. Vassallo , M. Viel , Y. Wang , N. Welikala , L. Whittaker , A. Zacchei , G. Zamorani , J. Zoubian (9) , E. Zucca

1 LAM - Laboratoire d'Astrophysique de Marseille
2 IAP - Institut d'Astrophysique de Paris
3 IRAP - Institut de recherche en astrophysique et planétologie
4 LAGRANGE - Joseph Louis LAGRANGE
5 AIM (UMR_7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation
6 CNES - Centre National d'Études Spatiales [Toulouse]
7 IP2I Lyon - Institut de Physique des 2 Infinis de Lyon
8 IAS - Institut d'astrophysique spatiale
9 CPPM - Centre de Physique des Particules de Marseille
10 LERMA - Laboratoire d'Etudes et Recherche en Mathématiques Appliquées

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-$z$s 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 design a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data are divided into two samples: one calibration sample for which photometry and redshifts are 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 sources they consider unfit for use in cosmological analyses. The performance of each method is 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, 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. [abridged]

Mots clés: galaxies; distances and redshifts -surveys -techniques; miscellaneous -catalogs

Domaines: Planète et Univers [physics]; Cosmologie et astrophysique extra-galactique [astro-ph.CO]; Astrophysique galactique [astro-ph.GA]

G. Desprez, S. Paltani, J. Coupon, I. Almosallam, A. Alvarez-Ayllon, et al.. Euclid preparation. X. The Euclid photometric-redshift challenge: X. The Euclid photometric-redshift challenge. Astronomy and Astrophysics - A&A, 2020, 644, pp.A31. ⟨10.1051/0004-6361/202039403⟩. ⟨hal-03026946⟩