Euclid preparation. XI. Mean redshift determination from galaxy redshift probabilities for cosmic shear tomography

O. Ilbert (1) , S. de La Torre (1) , N. Martinet (1) , A. H. Wright (2) , S. Paltani (1) , C. Laigle (3) , I. Davidzon (4, 5) , E. Jullo (1) , H. Hildebrandt , D. C. Masters , A. Amara , C. J. Conselice , S. Andreon , N. Auricchio , R. Azzollini , C. Baccigalupi , A. Balaguera-Antolínez , M. Baldi , A. Balestra , S. Bardelli (6) , R. Bender , A. Biviano , C. Bodendorf , D. Bonino , S. Borgani , A. Boucaud (7) , E. Bozzo , E. Branchini , M. Brescia , C. Burigana , R. Cabanac (8) , S. Camera , V. Capobianco , A. Cappi (9) , C. Carbone , J. Carretero , C. S. Carvalho (10) , S. Casas , F. J. Castander , M. Castellano , G. Castignani , S. Cavuoti , A. Cimatti , R. Cledassou (11) , C. Colodro-Conde , G. Congedo , L. Conversi (12) , Y. Copin (13) , L. Corcione , A. Costille (1) , J. Coupon (14) , H. M. Courtois (13) , M. Cropper , J. Cuby (1) , A. da Silva , H. Degaudenzi , D. Di Ferdinando , F. Dubath , C. Duncan , X. Dupac (15) , S. Dusini , A. Ealet , M. Fabricius , S. Farrens (16) , P. G. Ferreira (17) , F. Finelli , P. Fosalba , S. Fotopoulou , E. Franceschi , P. Franzetti , S. Galeotta , B. Garilli , W. Gillard (18) , B. Gillis , C. Giocoli , G. Gozaliasl , J. Graciá-Carpio , F. Grupp , L. Guzzo , S. V. H. Haugan , W. Holmes , F. Hormuth , K. Jahnke , E. Keihanen , S. Kermiche (18) , A. Kiessling , C. C. Kirkpatrick , M. Kunz , H. Kurki-Suonio , S. Ligori , P. B. Lilje , I. Lloro , D. Maino , E. Maiorano , O. Marggraf , K. Markovic , F. Marulli , R. Massey , M. Maturi , N. Mauri , S. Maurogordato (19) , H. J. Mccracken (20) , E. Medinaceli , S. Mei (21) , R. Benton Metcalf , M. Moresco , B. Morin (22) , L. Moscardini , E. Munari , R. Nakajima , C. Neissner , S. Niemi , J. Nightingale , F. Pasian , L. Patrizii , K. Pedersen , R. Pello (1) , V. Pettorino (23) , S. Pires (24) , G. Polenta , M. Poncet (25) , L. Popa , D. Potter , L. Pozzetti , F. Raison , A. Renzi , J. Rhodes , G. Riccio , E. Romelli , M. Roncarelli , E. Rossetti , R. Saglia , A. G. Sánchez (26) , D. Sapone , P. Schneider , T. Schrabback , V. Scottez (20) , A. Secroun (18) , G. Seidel , S. Serrano , C. Sirignano , G. Sirri , L. Stanco , F. Sureau (23) , P. Tallada Crespá , M. Tenti , H. I. Teplitz , I. Tereno , R. Toledo-Moreo , F. Torradeflot , A. Tramacere , E. A. Valentijn , L. Valenziano , J. Valiviita , T. Vassallo , Y. Wang (27) , C. Padilla , N. Welikala , J. Weller , L. Whittaker , A. Zacchei , G. Zamorani (6) , J. Zoubian (18) , E. Zucca (6)

1 LAM - Laboratoire d'Astrophysique de Marseille
2 UNSW - University of New South Wales [Sydney]
3 IAP - Institut d'Astrophysique de Paris
4 CALTECH - California Institute of Technology
5 UCPH - University of Copenhagen = Københavns Universitet
6 OABO - INAF - Osservatorio Astronomico di Bologna
7 APC (UMR_7164) - AstroParticule et Cosmologie
8 LATT - Laboratoire Astrophysique de Toulouse-Tarbes
9 CASSIOPEE - Laboratoire de Cosmologie, Astrophysique Stellaire & Solaire, de Planétologie et de Mécanique des Fluides
10 UC Merced - University of California [Merced]
11 CNES - Centre National d'Études Spatiales [Toulouse]
12 Herschel Science Center [Madrid]
13 IP2I Lyon - Institut de Physique des 2 Infinis de Lyon
14 UNIGE - Université de Genève = University of Geneva
15 CESR - Centre d'étude spatiale des rayonnements
16 AIM (UMR_7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation
17 University of Oxford
18 CPPM - Centre de Physique des Particules de Marseille
19 LAGRANGE - Joseph Louis LAGRANGE
20 IAP - Institut d'Astrophysique de Paris
21 LERMA - Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres
22 EPOC - Environnements et Paléoenvironnements OCéaniques
23 AIM (UMR7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation
24 Fondation FondaMental [Créteil]
25 UP4 CELSA - Université Paris-Sorbonne - Paris 4 - École des hautes études en sciences de l'information et de la communication
26 LEPSA - Laboratoire Européen Performance Santé Altitude
27 Wuhan University [China]

The analysis of weak gravitational lensing in wide-field imaging surveys is considered to be a major cosmological probe of dark energy. Our capacity to constrain the dark energy equation of state relies on an accurate knowledge of the galaxy mean redshift ⟨z⟩. We investigate the possibility of measuring ⟨z⟩ with an accuracy better than 0.002 (1 + z) in ten tomographic bins spanning the redshift interval 0.2 < z < 2.2, the requirements for the cosmic shear analysis of Euclid. We implement a sufficiently realistic simulation in order to understand the advantages and complementarity, as well as the shortcomings, of two standard approaches: the direct calibration of ⟨z⟩ with a dedicated spectroscopic sample and the combination of the photometric redshift probability distribution functions (zPDFs) of individual galaxies. We base our study on the Horizon-AGN hydrodynamical simulation, which we analyse with a standard galaxy spectral energy distribution template-fitting code. Such a procedure produces photometric redshifts with realistic biases, precisions, and failure rates. We find that the current Euclid design for direct calibration is sufficiently robust to reach the requirement on the mean redshift, provided that the purity level of the spectroscopic sample is maintained at an extremely high level of > 99.8%. The zPDF approach can also be successful if the zPDF is de-biased using a spectroscopic training sample. This approach requires deep imaging data but is weakly sensitive to spectroscopic redshift failures in the training sample. We improve the de-biasing method and confirm our finding by applying it to real-world weak-lensing datasets (COSMOS and KiDS+VIKING-450).

Mots clés: dark energy; galaxies: distances and redshifts; methods: statistical

Domaines: Astrophysique [astro-ph]

O. Ilbert, S. de La Torre, N. Martinet, A. H. Wright, S. Paltani, et al.. Euclid preparation. XI. Mean redshift determination from galaxy redshift probabilities for cosmic shear tomography: XI. Mean redshift determination from galaxy redshift probabilities for cosmic shear tomography. Astronomy and Astrophysics - A&A, 2021, 647, pp.A117. ⟨10.1051/0004-6361/202040237⟩. ⟨hal-03174105⟩