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KiDS and Euclid: Cosmological implications of a pseudo angular power spectrum analysis of KiDS-1000 cosmic shear tomography

Loureiro, A. and Whittaker, L. and Spurio Mancini, A. and Joachimi, B. and Cuceu, A. and Asgari, M. and Stölzner, B. and Tröster, T. and Wright, A. H. and Bilicki, M. and Dvornik, A. and Giblin, B. and Heymans, C. and Hildebrandt, H. and Shan, H. and Amara, A. and Auricchio, N. and Bodendorf, C. and Bonino, D. and Branchini, E. and Brescia, M. and Capobianco, V. and Carbone, C. and Carretero, J. and Castellano, M. and Cavuoti, S. and Cimatti, A. and Cledassou, R. and Congedo, G. and Conversi, L. and Copin, Y. and Corcione, L. and Cropper, M. and Da Silva, A. and Douspis, M. and Dubath, F. and Duncan, C. A. J. and Dupac, X. and Dusini, S. and Farrens, S. and Ferriol, S. and Fosalba, P. and Frailis, M. and Franceschi, E. and Fumana, M. and Garilli, B. and Gillis, B. and Giocoli, C. and Grazian, A. and Grupp, F. and Haugan, S. V. H. and Holmes, W. and Hormuth, F. and Jahnke, K. and Kümmel, M. and Kermiche, S. and Kiessling, A. and Kilbinger, M. and Kitching, T. and Kuijken, K. and Kunz, M. and Kurki-Suonio, H. and Ligori, S. and Lilje, P.B. and Lloro, I. and Mansutti, O. and Marggraf, O. and Markovic, K. and Marulli, F. and Massey, R. and Meneghetti, M. and Meylan, G. and Moresco, M. and Morin, B. and Moscardini, L. and Munari, E. and Niemi, S. M. and Padilla, C. and Paltani, S. and Pasian, F. and Pedersen, K. and Pettorino, V. and Pires, S. and Poncet, M. and Popa, L. and Raison, F. and Rhodes, J. and Rix, H. and Roncarelli, M. and Saglia, R. and Schneider, P. and Secroun, A. and Serrano, S. and Sirignano, C. and Sirri, G. and Stanco, L. and Starck, J. L. and Tallada-Crespí, P. and Taylor, A. N. and Tereno, I. and Toledo-Moreo, R. and Torradeflot, F. and Valentijn, E. A. and Wang, Y. and Welikala, N. and Weller, J. and Zamorani, G. and Zoubian, J. and Andreon, S. and Baldi, M. and Camera, S. and Farinelli, R. and Polenta, G. and Tessore, N. (2022) KiDS and Euclid: Cosmological implications of a pseudo angular power spectrum analysis of KiDS-1000 cosmic shear tomography. Astronomy and Astrophysics, 665 . Art. No. A56. ISSN 0004-6361. doi:10.1051/0004-6361/202142481.

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We present a tomographic weak lensing analysis of the Kilo Degree Survey Data Release 4 (KiDS-1000), using a new pseudo angular power spectrum estimator (pseudo-C_ℓ) under development for the ESA Euclid mission. Over 21 million galaxies with shape information are divided into five tomographic redshift bins, ranging from 0.1 to 1.2 in photometric redshift. We measured pseudo-C_ℓ using eight bands in the multipole range 76 < ℓ < 1500 for auto- and cross-power spectra between the tomographic bins. A series of tests were carried out to check for systematic contamination from a variety of observational sources including stellar number density, variations in survey depth, and point spread function properties. While some marginal correlations with these systematic tracers were observed, there is no evidence of bias in the cosmological inference. B-mode power spectra are consistent with zero signal, with no significant residual contamination from E/B-mode leakage. We performed a Bayesian analysis of the pseudo-C_ℓ estimates by forward modelling the effects of the mask. Assuming a spatially flat ΛCDM cosmology, we constrained the structure growth parameter S₈ = σ₈(Ωₘ/0.3)^(1/2) = 0.754_(−0.029)^(+0.027). When combining cosmic shear from KiDS-1000 with baryon acoustic oscillation and redshift space distortion data from recent Sloan Digital Sky Survey (SDSS) measurements of luminous red galaxies, as well as the Lyman-α forest and its cross-correlation with quasars, we tightened these constraints to S₈ = 0.771_(−0.032)^(+0.006). These results are in very good agreement with previous KiDS-1000 and SDSS analyses and confirm a ∼3σ tension with early-Universe constraints from cosmic microwave background experiments.

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Loureiro, A.0000-0002-4371-0876
Rhodes, J.0000-0002-4485-8549
Additional Information:The authors would like to thank Dr Lorne Whiteway for useful comments and discussions. We would also like to thank the reviewer for their helpful comments that helped enrich this work. The mixing matrix calculation used a publicly available Wigner 3-j symbol code developed by Dr Whiteway and it can be found in The K-Means code used for the jackknife covariances for systematics was built on top of a code developed by Dr Erin Sheldon, publicly available in The analysis performed in this work also made use of the following packages and software: NumPy (Harris et al. 2020), SciPy (Virtanen et al. 2020), Matplotlib (Hunter 2007), GetDist (Lewis 2019), Pandas (Wes McKinney 2010), AstroPy (Astropy Collaboration 2018) and Jupyter Lab (Kluyver et al. 2016). The Flask + Salmo mocks were generated and provided by Dr Chieh-An Lin. We thank Dr Edd Edmonson for technical support at the UCL HPC facilities. This work used computing equipment funded by the Research Capital Investment Fund (RCIF) provided by UKRI, and is partially funded by the UCL Cosmoparticle Initiative. M.A. and T.T. acknowledge support from the European Research Council under grant number 647112. A.H.W. and A.D. acknowledge support from the European Research Council Consolidator Grant (No. 770935). M.B. is supported by the Polish National Science Center through grants no. 2020/38/E/ST9/00395, 2018/30/E/ST9/00698 and 2018/31/G/ST9/03388, and by the Polish Ministry of Science and Higher Education through grant DIR/WK/2018/12. B.G. acknowledges support from the European Research Council under grant number 647112 and from the Royal Society through an Enhancement Award (RGF/EA/181006). C.H. acknowledges support from the European Research Council under grant number 647112, and support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research. 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). H.Y.S. acknowledges the support from NSFC of China under grant 11973070, the Shanghai Committee of Science and Technology grant No. 19ZR1466600 and Key Research Program of Frontier Sciences, CAS, Grant No. ZDBS-LY-7013. The KiDS-1000 data products in this paper are based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme IDs 177.A-3016, 177.A-3017 and 177.A-3018, and on data products produced by Target/OmegaCEN, INAF-OACN, INAF-OAPD and the KiDS production team, on behalf of the KiDS consortium. The authors acknowledge the Euclid Consortium, 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 French 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 National Astronomical Observatory of Japan, 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 web site ( Author Contributions: All authors contributed to the development and writing of this paper. The authorship list is given in several groups: the lead authors (A.L., L.W., A.S.M., B.J., A.C.), followed by an alphabetical group that includes those who are key contributors to both the scientific analysis and the KiDS data products, and a further alphabetical group covering those who have either made a significant contribution to the KiDS data products or to the scientific analysis. Additional alphabetical groups correspond to different authorship levels of the Euclid Consortium.
Group:Infrared Processing and Analysis Center (IPAC)
Funding AgencyGrant Number
United Kingdom Research and Innovation (UKRI)UNSPECIFIED
University College LondonUNSPECIFIED
European Research Council (ERC)647112
European Research Council (ERC)770935
National Science Centre (Poland)2020/38/E/ST9/00395
National Science Centre (Poland)2018/30/E/ST9/00698
National Science Centre (Poland)2018/31/G/ST9/03388
Ministerstwo Nauki i Szkolnictwa Wyższego (MNiSW)DIR/WK/2018/12
European Research Council (ERC)647112
Royal SocietyRGF/EA/181006
European Research Council (ERC)647112
Max Planck SocietyUNSPECIFIED
Alexander von Humboldt FoundationUNSPECIFIED
Bundesministerium für Bildung und Forschung (BMBF)UNSPECIFIED
Deutsche Forschungsgemeinschaft (DFG)Hi 1495/5-1
European Research Council (ERC)770935
National Natural Science Foundation of China11973070
Shanghai Committee of Science and Technology19ZR1466600
Chinese Academy of SciencesZDBS-LY-7013
Record Number:CaltechAUTHORS:20220928-285212100.19
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117172
Deposited By: Melissa Ray
Deposited On:04 Oct 2022 19:52
Last Modified:04 Oct 2022 19:52

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