A1 Refereed original research article in a scientific journal
Euclid preparation: LXXXIX. Accurate and precise data-driven angular power spectrum covariances
Authors: Naidoo, K.; Ruiz-Zapatero, J.; Tessore, N.; Joachimi, B.; Loureiro, A.; Aghanim, N.; Altieri, B.; Amara, A.; Amendola, L.; Andreon, S.; Auricchio, N.; Baccigalupi, C.; Bagot, D.; Baldi, M.; Bardelli, S.; Battaglia, P.; Biviano, A.; Branchini, E.; Brescia, M.; Camera, S.; Capobianco, V.; Carbone, C.; Cardone, V. F.; Carretero, J.; Castellano, M.; Castignani, G.; Cavuoti, S.; Chambers, K. C.; Cimatti, A.; Colodro-Conde, C.; Congedo, G.; Conversi, L.; Copin, Y.; Courbin, F.; Courtois, H. M.; Da Silva, A.; Degaudenzi, H.; De Lucia, G.; Dubath, F.; Dupac, X.; Dusini, S.; Escoffier, S.; Farina, M.; Farinelli, R.; Farrens, S.; Faustini, F.; Ferriol, S.; Finelli, F.; Fosalba, P.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; George, K.; Gillis, B.; Giocoli, C.; Gracia-Carpio, J.; Grazian, A.; Grupp, F.; Holmes, W.; Hormuth, F.; Hornstrup, A.; Jahnke, K.; Jhabvala, M.; Keihanen, E.; Kermiche, S.; Kiessling, A.; Kilbinger, M.; Kubik, B.; Kummel, M.; Kunz, M.; Kurki-Suonio, H.; Le Brun, A. M. C.; Ligori, S.; Lilje, P. B.; Lindholm, V.; Lloro, I.; Mainetti, G.; Maino, D.; Maiorano, E.; Mansutti, O.; Marcin, S.; Marggraf, O.; Martinelli, M.; Martinet, N.; Marulli, F.; Massey, R.; Medinaceli, E.; Mei, S.; Mellier, Y.; Meneghetti, M.; Merlin, E.; Meylan, G.; Mora, A.; Moscardini, L.; Neissner, C.; Niemi, S. -M.; Padilla, C.; Paltani, S.; Pasian, F.; Pedersen, K.; Percival, W. J.; Pettorino, V.; Pires, S.; Polenta, G.; Poncet, M.; Popa, L. A.; Raison, F.; Rebolo, R.; Renzi, A.; Rhodes, J.; Riccio, G.; Romelli, E.; Roncarelli, M.; Rosset, C.; Saglia, R.; Sakr, Z.; Sanchez, A. G.; Sapone, D.; Sartoris, B.; Schneider, P.; Schrabback, T.; Secroun, A.; Sefusatti, E.; Seidel, G.; Seiffert, M.; Serrano, S.; Simon, P.; Sirignano, C.; Sirri, G.; Mancini, A. Spurio; Stanco, L.; Steinwagner, J.; Tallada-Crespi, P.; Tavagnacco, D.; Taylor, A. N.; Tereno, I.; Toft, S.; Toledo-Moreo, R.; Torradeflot, F.; Tutusaus, I.; Valenziano, L.; Valiviita, J.; Vassallo, T.; Kleijn, G. Verdoes; Veropalumbo, A.; Wang, Y.; Weller, J.; Zamorani, G.; Zerbi, F. M.; Zucca, E.; Allevato, V.; Ballardini, M.; Bolzonella, M.; Bozzo, E.; Burigana, C.; Cabanac, R.; Calabrese, M.; Cappi, A.; Di Ferdinando, D.; Vigo, J. A. Escartin; Gabarra, L.; Martin-Fleitas, J.; Matthew, S.; Mauri, N.; Metcalf, R. B.; Pezzotta, A.; Pontinen, M.; Risso, I.; Scottez, V.; Sereno, M.; Tenti, M.; Viel, M.; Wiesmann, M.; Akrami, Y.; Andika, I. T.; Anselmi, S.; Archidiacono, M.; Atrio-Barandela, F.; Balaguera-Antolinez, A.; Bertacca, D.; Bethermin, M.; Blanchard, A.; Blot, L.; Borgani, S.; Brown, M. L.; Bruton, S.; Calabro, A.; Quevedo, B. Camacho; Caro, F.; Carvalho, C. S.; Castro, T.; Cogato, F.; Conseil, S.; Cooray, A. R.; Davini, S.; Desprez, G.; Diaz-Sanchez, A.; Diaz, J. J.; Di Domizio, S.; Diego, J. M.; Dimauro, P.; Enia, A.; Fang, Y.; Ferrari, A. G.; Ferreira, P. G.; Finoguenov, A.; Fontana, A.; Franco, A.; Ganga, K.; Garcia-Bellido, J.; Gasparetto, T.; Gautard, V.; Gaztanaga, E.; Giacomini, F.; Gianotti, F.; Gozaliasl, G.; Guidi, M.; Gutierrez, C. M.; Hall, A.; Hernandez-Monteagudo, C.; Hildebrandt, H.; Hjorth, J.; Joudaki, S.; Kajava, J. J. E.; Kang, Y.; Kansal, V.; Karagiannis, D.; Kiiveri, K.; Kirkpatrick, C. C.; Kruk, S.; Lattanzi, M.; Legrand, L.; Lembo, M.; Lepori, F.; Leroy, G.; Lesci, G. F.; Lesgourgues, J.; Leuzzi, L.; Liaudat, T. I.; Macias-Perez, J.; Maggio, G.; Magliocchetti, M.; Mannucci, F.; Maoli, R.; Martins, C. J. A. P.; Maurin, L.; Miluzio, M.; Monaco, P.; Moretti, C.; Morgante, G.; Nadathur, S.; Navarro-Alsina, A.; Pagano, L.; Passalacqua, F.; Paterson, K.; Patrizii, L.; Pisani, A.; Potter, D.; Quai, S.; Radovich, M.; Rocci, P. -F.; Sacquegna, S.; Sahlen, M.; Sanders, D. B.; Sarpa, E.; Schneider, A.; Sciotti, D.; Sellentin, E.; Smith, L. C.; Tanidis, K.; Testera, G.; Teyssier, R.; Tosi, S.; Troja, A.; Tucci, M.; Valieri, C.; Venhola, A.; Vergani, D.; Verza, G.; Vielzeuf, P.; Walton, N. A.; Euclid Collaboration
Publisher: EDP Sciences
Publication year: 2026
Journal: Astronomy and Astrophysics
Article number: A167
Volume: 708
ISSN: 0004-6361
eISSN: 1432-0746
DOI: https://doi.org/10.1051/0004-6361/202555893
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://doi.org/10.1051/0004-6361/202555893
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/523280716
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
We develop techniques for generating accurate and precise internal covariances for measurements of clustering and weak-lensing angular power spectra. These methods have been designed to produce non-singular and unbiased covariances for Euclid's large anticipated data vector and will be critical for validation against observational systematic effects. We constructed jackknife segments that are equal in area to a high precision by adapting the binary space partition algorithm to work on arbitrarily shaped regions on the unit sphere. Jackknife estimates of the covariances are internally derived and require no assumptions about cosmology or galaxy population and bias. Our covariance estimation, called DICES (Debiased Internal Covariance Estimation with Shrinkage), first estimated a noisy covariance through conventional delete-1 jackknife resampling. This was followed by linear shrinkage of the empirical correlation matrix towards the Gaussian prediction, rather than linear shrinkage of the covariance matrix. Shrinkage ensures the covariance is non-singular and therefore invertible, which is critical for the estimation of likelihoods and validation. We then applied a delete-2 jackknife bias correction to the diagonal components of the jackknife covariance that removed the general tendency for jackknife error estimates to be biased high. We validated internally derived covariances, which used the jackknife resampling technique, on synthetic Euclid-like lognormal catalogues. We demonstrate that DICES produces accurate, non-singular covariance estimates, with the relative error improving by 33% for the covariance and 48% for the correlation structure in comparison to jackknife estimates. These estimates can be used for highly accurate regression and inference.
Downloadable publication This is an electronic reprint of the original article. |  |
Funding information in the publication:
KN, NT, JRZ, and BJ acknowledge support by the UK Space Agency through grants ST/W002574/1 and ST/X00208X/1. AL acknowledges support by the Swedish National Space Agency (Rymdstyrelsen) through the Career Grant Project Dnr. 2024-00171. 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 Agenzia Spaziale Italiana, the Austrian Forschungsforderungsgesellschaft funded through BMK, the Belgian Science Policy, the Canadian Euclid Consortium, the Deutsches Zentrum fur Luft-und Raumfahrt, the DTU Space and the Niels Bohr Institute in Denmark, the French Centre National d'Etudes Spatiales, the Fundacao para a Ciencia e a Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia, Innovacion y Universidades, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Research Council of Finland, the Romanian Space Agency, the State Secretariat for Education, Research, and Innovation (SERI) at the Swiss Space O ffice (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (www.euclid-ec.org
