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

Ilbert, O. and de la Torre, S. and Martinet, N. and Wright, A. H. and Paltani, S. and Laigle, C. and Davidzon, I. and Jullo, E. and Hildebrandt, H. and Masters, D. C. and Amara, A. and Conselice, C. J. and Andreon, S. and Auricchio, N. and Azzollini, R. and Baccigalupi, C. and Balaguera-Antolínez, A. and Baldi, M. and Balestra, A. and Bardelli, S. and Bender, R. and Biviano, A. and Bodendorf, C. and Bonino, D. and Borgani, S. and Boucaud, A. and Bozzo, E. and Branchini, E. and Brescia, M. 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, S. and Castander, F. J. and Castellano, M. and Castignani, G. and Cavuoti, S. and Cimatti, A. and Cledassou, R. and Colodro-Conde, C. and Congedo, G. and Conversi, L. and Copin, Y. and Corcione, L. and Costille, A. and Coupon, J. and Courtois, H. M. and Cropper, M. and Cuby, J. and Da Silva, A. and Degaudenzi, H. and Di Ferdinando, D. and Dubath, F. and Duncan, C. and Dupac, X. and Dusini, S. and Ealet, A. and Fabricius, M. and Farrens, S. and Ferreira, P. G. and Finelli, F. and Fosalba, P. and Fotopoulou, S. and Franceschi, E. and Franzetti, P. 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 Haugan, S. V. H. and Holmes, W. and Hormuth, F. and Jahnke, K. and Keihanen, E. and Kermiche, S. and Kiessling, A. and Kirkpatrick, C. C. 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 Marulli, F. and Massey, R. and Maturi, M. and Mauri, N. and Maurogordato, S. and McCracken, H. J. and Medinaceli, E. and Mei, S. and Benton Metcalf, R. and Moresco, M. and Morin, B. and Moscardini, L. and Munari, E. and Nakajima, R. and Neissner, C. and Niemi, S. and Nightingale, J. and Padilla, C. and Pasian, F. and Patrizii, L. and Pedersen, K. and Pello, R. 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 Romelli, E. and Roncarelli, M. and Rossetti, E. and Saglia, R. and Sánchez, A. G. and Sapone, D. and Schneider, P. and Schrabback, T. and Scottez, V. and Secroun, A. and Seidel, G. and Serrano, S. and Sirignano, C. and Sirri, G. and Stanco, L. and Sureau, F. and Tallada Crespá, P. and Tenti, M. and Teplitz, H. I. and Tereno, I. and Toledo-Moreo, R. and Torradeflot, F. and Tramacere, A. and Valentijn, E. A. and Valenziano, L. and Valiviita, J. and Vassallo, T. and Wang, Y. and Welikala, N. and Weller, J. and Whittaker, L. and Zacchei, A. and Zamorani, G. and Zoubian, J. and Zucca, E. (2021) Euclid preparation. XI. Mean redshift determination from galaxy redshift probabilities for cosmic shear tomography. Astronomy and Astrophysics, 647 . Art. No. A117. ISSN 0004-6361. https://resolver.caltech.edu/CaltechAUTHORS:20210323-083645114

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Abstract

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).


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1051/0004-6361/202040237DOIArticle
https://arxiv.org/abs/2101.02228arXivDiscussion Paper
ORCID:
AuthorORCID
Ilbert, O.0000-0002-7303-4397
Masters, D. C.0000-0001-5382-6138
Rhodes, J.0000-0002-4485-8549
Teplitz, H. I.0000-0002-7064-5424
Additional Information:© 2021 Euclid Collaboration. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received 24 December 2020; Accepted 2 January 2021; Published online 18 March 2021. We thank the OU-PHZ of Euclid for all the useful discussions along these years. OI acknowledges the funding of the French Agence Nationale de la Recherche for the project ‘SAGACE’. NM acknowledges support from a CNES fellowship. H. Hildebrandt is supported by a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1) as well as an ERC Consolidator Grant (No. 770935). A.H. Wright is supported by the ERC Consolidator Grant (No. 770935). This work relied on the HPC resources of CINES (Jade) under the allocation 2013047012 and c2014047012 made by GENCI and on the Horizon Cluster hosted by Institut d’Astrophysique de Paris. ID acknowledges that he received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 896225. We warmly thank S. Rouberol for running the cluster on which the simulation was post-processed. This research is also partly supported by the Centre National d’Etudes Spatiales (CNES). We would also like to recognise the contributions from all of the members of the COSMOS team who helped in obtaining and reducing the large amount of multi-wavelength and spectroscopic data. Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme IDs 177.A-3016, 177.A-3017, 177.A-3018, 179.A-2004, and on data products produced by the KiDS consortium. The KiDS production team acknowledges support from: Deutsche Forschungsgemeinschaft, ERC, NOVA and NWO-M grants; Target; the University of Padova, and the University Federico II (Naples). SA thank the support PRIN MIUR2015 “Cosmology and Fundamental Physics: Illuminating the Dark Universe with Euclid”. 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 Ciencia 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)
Funders:
Funding AgencyGrant Number
Agence Nationale pour la Recherche (ANR)UNSPECIFIED
Centre National d'Études Spatiales (CNES)UNSPECIFIED
Deutsche Forschungsgemeinschaft (DFG)Hi 1495/5-1
European Research Council (ERC)770935
Marie Curie Fellowship896225
Nederlandse Onderzoekschool Voor Astronomie (NOVA)UNSPECIFIED
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)UNSPECIFIED
TargetUNSPECIFIED
University of PadovaUNSPECIFIED
University Federico IIUNSPECIFIED
PRIN-MIUR 2015UNSPECIFIED
European Space Agency (ESA)UNSPECIFIED
Academy of FinlandUNSPECIFIED
Agenzia Spaziale Italiana (ASI)UNSPECIFIED
Belgian Science Policy Office (BELSPO)UNSPECIFIED
Canadian Euclid ConsortiumUNSPECIFIED
Deutsches Zentrum für Luft- und Raumfahrt (DLR)UNSPECIFIED
Danish Space Research InstituteUNSPECIFIED
Fundação para a Ciência e a Tecnologia (FCT)UNSPECIFIED
Ministerio de Economía y Competitividad (MINECO)UNSPECIFIED
NASAUNSPECIFIED
Norwegian Space AgencyUNSPECIFIED
Romanian Space AgencyUNSPECIFIED
State Secretariat for Education, Research and Innovation (SERI)UNSPECIFIED
Swiss Space Office (SSO)UNSPECIFIED
United Kingdom Space Agency (UKSA)UNSPECIFIED
Subject Keywords:dark energy – galaxies: distances and redshifts – methods: statistical
Record Number:CaltechAUTHORS:20210323-083645114
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210323-083645114
Official Citation:Euclid preparation - XI. Mean redshift determination from galaxy redshift probabilities for cosmic shear tomography. Euclid Collaboration, O. Ilbert, S. de la Torre, N. Martinet, A. H. Wright, S. Paltani, C. Laigle, I. Davidzon, E. Jullo, 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, R. Bender, A. Biviano, C. Bodendorf, D. Bonino, S. Borgani, A. Boucaud, E. Bozzo, E. Branchini, M. Brescia, C. Burigana, R. Cabanac, S. Camera, V. Capobianco, A. Cappi, C. Carbone, J. Carretero, C. S. Carvalho, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, R. Cledassou, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, L. Corcione, A. Costille, J. Coupon, H. M. Courtois, M. Cropper, J. Cuby, A. Da Silva, H. Degaudenzi, D. Di Ferdinando, F. Dubath, C. Duncan, X. Dupac, S. Dusini, A. Ealet, M. Fabricius, S. Farrens, P. G. Ferreira, F. Finelli, P. Fosalba, S. Fotopoulou, E. Franceschi, P. Franzetti, S. Galeotta, B. Garilli, W. Gillard, 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, 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, H. J. McCracken, E. Medinaceli, S. Mei, R. Benton Metcalf, M. Moresco, B. Morin, L. Moscardini, E. Munari, R. Nakajima, C. Neissner, S. Niemi, J. Nightingale, C. Padilla, F. Pasian, L. Patrizii, K. Pedersen, R. Pello, V. Pettorino, S. Pires, G. Polenta, M. Poncet, 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, D. Sapone, P. Schneider, T. Schrabback, V. Scottez, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, F. Sureau, 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, N. Welikala, J. Weller, L. Whittaker, A. Zacchei, G. Zamorani, J. Zoubian and E. Zucca. A&A, 647 (2021) A117; DOI: https://doi.org/10.1051/0004-6361/202040237
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ID Code:108525
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 Mar 2021 17:25
Last Modified:23 Mar 2021 17:25

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