Euclid preparation LXXXVII. Non-Gaussianity of two-point statistics likelihood: Precise analysis of the matter power spectrum distribution - UTU Research Portal

Euclid preparation LXXXVII. Non-Gaussianity of two-point statistics likelihood: Precise analysis of the matter power spectrum distribution;

: Bel, J.; Gouyou Beauchamps, S.; Baratta, P.; Blot, L.; Carbone, C.; Corasaniti, P. -S.; Sefusatti, E.; Escoffier, S.; Gillard, W.; Amara, A.; Andreon, S.; Auricchio, N.; Baccigalupi, C.; Baldi, M.; Bardelli, S.; Battaglia, P.; Biviano, A.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Canas-Herrera, G.; Capobianco, V.; Cardone, V. F.; Carretero, J.; Casas, S.; Castellano, M.; Castignani, G.; Cavuoti, S.; Chambers, K. C.; Cimatti, A.; Colodro-Conde, C.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Costille, A.; Courbin, F.; Courtois, H. M.; Da Silva, A.; Degaudenzi, H.; De la Torre, S.; De Lucia, G.; Dubath, F.; Duncan, C. A. J.; Dupac, X.; Farina, M.; Farinelli, R.; Faustini, F.; Ferriol, S.; Finelli, F.; Fourmanoit, N.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; George, K.; Gillis, B.; Giocoli, C.; Gracia-Carpio, J.; Grazian, A.; Grupp, F.; Guzzo, L.; Haugan, S. V. H.; Holmes, W.; Hormuth, F.; Hornstrup, A.; Jahnke, K.; Jhabvala, M.; Joachimi, B.; Keihänen, E.; Kermiche, S.; Kubik, B.; 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.; Marggraf, O.; Markovic, K.; Martinelli, M.; Martinet, N.; Marulli, F.; Massey, R.; Medinaceli, E.; Mellier, Y.; Meneghetti, M.; Merlin, E.; Meylan, G.; Mora, A.; Moresco, M.; 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.; Renzi, A.; Rhodes, J.; Riccio, G.; Rizzo, F.; Romelli, E.; Roncarelli, M.; Saglia, R.; Sakr, Z.; Sanchez, A. G.; Sapone, D.; Sartoris, B.; Schneider, P.; Schrabback, T.; Scodeggio, M.; Secroun, A.; Seidel, G.; Seiffert, M.; Serrano, S.; Simon, P.; Sirignano, C.; Sirri, G.; Stanco, L.; Steinwagner, J.; Tallada-Crespi, P.; Taylor, A. N.; Tereno, I.; Tessore, N.; Toft, S.; Toledo-Moreo, R.; Torradeflot, F.; Tutusaus, I.; Valenziano, L.; Valiviita, J.; Vassallo, T.; Veropalumbo, A.; Wang, Y.; Weller, J.; Zamorani, G.; Zucca, E.; Ballardini, M.; Bozzo, E.; Burigana, C.; Cabanac, R.; Calabrese, M.; Di Ferdinando, D.; Vigo, J. A. Escartin; Gabarra, L.; Martin-Fleitas, J.; Matthew, S.; Mauri, N.; Metcalf, R. B.; Pezzotta, A.; Pöntinen, M.; Porciani, C.; Risso, I.; Scottez, V.; Sereno, M.; Tenti, M.; Viel, M.; Wiesmann, M.; Akrami, Y.; Alvi, S.; Andika, I. T.; Anselmi, S.; Archidiacono, M.; Atrio-Barandela, F.; Bertacca, D.; Bethermin, M.; Blanchard, A.; Borgani, S.; Brown, M. L.; Bruton, S.; Calabro, A.; Quevedo, B. Camacho; Caro, F.; Carvalho, C. S.; Castro, T.; Cogato, F.; Conseil, S.; Contarini, S.; Cooray, A. R.; Davini, S.; Desprez, G.; Diaz-Sanchez, A.; Diaz, J. J.; Di Domizio, S.; Diego, J. M.; Enia, A.; Fang, Y.; Ferrari, A. G.; Finoguenov, 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.; Kajava, J. J. E.; Kang, Y.; Kansal, V.; Karagiannis, D.; Kiiveri, K.; Kirkpatrick, C. C.; Kruk, S.; Lattanzi, M.; Le Graet, J.; 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.; Naidoo, K.; Navarro-Alsina, A.; Nesseris, S.; Pagano, L.; Passalacqua, F.; Paterson, K.; Patrizii, L.; Pisani, A.; Potter, D.; Quai, S.; Radovich, M.; Reimberg, P.; Rocci, P. -F.; Rodighiero, G.; Sacquegna, S.; Sahlen, M.; Sanders, D. B.; Sarpa, E.; Schneider, A.; Sciotti, D.; Sellentin, E.; Smith, L. C.; Sorce, J. G.; Tanidis, K.; Tao, C.; Testera, G.; Teyssier, R.; Tosi, S.; Troja, A.; Tucci, M.; Valieri, C.; Venhola, A.; Vergani, D.; Vernizzi, F.; Verza, G.; Vielzeuf, P.; Walton, N. A.

Publisher: EDP Sciences

: 2026

Astronomy and Astrophysics

: A105

: 708

: 0004-6361

: 1432-0746

DOI: https://doi.org/10.1051/0004-6361/202556642

: https://www.aanda.org/articles/aa/full_html/2026/04/aa56642-25/aa56642-25.html

: https://research.utu.fi/converis/portal/detail/Publication/526620001

We investigate the non-Gaussian features in the distribution of the matter power spectrum multipoles. Using the COVMOS method, we generated 100 000 mock realisations of dark matter density fields in both real and redshift space across multiple redshifts and cosmological models. We derived an analytical framework linking the non-Gaussianity of the power spectrum distribution to higher-order statistics of the density field, including the trispectrum and pentaspectrum. We explored the effect of redshift-space distortions, the geometry of the survey, the Fourier binning, the integral constraint, and the shot noise on the skewness of the distribution of the power spectrum measurements. Our results demonstrate that the likelihood of the estimated matter power spectrum significantly deviates from a Gaussian assumption on non-linear scales, particularly at low redshift. This departure is primarily driven by the pentaspectrum contribution, which dominates over the trispectrum at intermediate scales. We also examined the impact of the finiteness of the survey geometry in the context of the Euclid mission, and we find that both the shape of the survey and the integral constraint amplify the skewness.

cosmological parameters, cosmology: observations, cosmology: theory, large-scale structure of Universe

KajavaEtAl2026EuclidPreparation.pdf

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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 Forschungsförderungsgesellschaft funded through BMK, the Belgian Science Policy, the Canadian Euclid Consortium, the Deutsches Zentrum für Luft- und Raumfahrt, the DTU Space and the Niels Bohr Institute in Denmark, the French Centre National d’Etudes Spatiales, the Fundação para a Ciência e a Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia, Innovación 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 Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (www.euclid-ec.org). The project leading to this publication has received funding from Excellence Initiative of Aix-Marseille University -A*MIDEX, a French “Investissements d’Avenir” programme (AMX-19-IET-008 -IPhU). The DEMNUni-cov simulations were carried out in the framework of “The Dark Energy and Massive Neutrino Universe covariances” project, using the Tier-0 Intel OmniPath Cluster Marconi-A1 of the Centro Interuniversitario del Nord-Est per il Calcolo Elettronico (CINECA). We acknowledge a generous CPU and storage allocation by the Italian Super-Computing Resource Allocation (ISCRA) as well as from the coordination of the “Accordo Quadro MoU per lo svolgimento di attività congiunta di ricerca Nuove frontiere in Astrofisica: HPC e Data Exploration di nuova generazione”, together with storage from INFN-CNAF and INAF-IA2.