October
2026
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2026A&A...714A.440L
Authors
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Lindholm, V.
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Sihvola, E.
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Valiviita, J.
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Fumagalli, A.
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Altieri, B.
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Andreon, S.
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Auricchio, N.
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Baccigalupi, C.
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Baldi, M.
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Bardelli, S.
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Battaglia, P.
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Biviano, A.
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Branchini, E.
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Brescia, M.
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Camera, S.
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Capobianco, V.
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Carbone, C.
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Cardone, V. F.
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Carretero, J.
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Casas, S.
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Castellano, M.
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Castignani, G.
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Cavuoti, S.
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Chambers, K. C.
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Cimatti, A.
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Colodro-Conde, C.
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Congedo, G.
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Conversi, L.
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Copin, Y.
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Courbin, F.
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Courtois, H. M.
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Da Silva, A.
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Degaudenzi, H.
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De Lucia, G.
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Dole, H.
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Dubath, F.
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Dupac, X.
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Dusini, S.
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Escoffier, S.
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Farina, M.
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Farinelli, R.
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Ferriol, S.
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Finelli, F.
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Fosalba, P.
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Fotopoulou, S.
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Frailis, M.
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Franceschi, E.
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Fumana, M.
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Galeotta, S.
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George, K.
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Gillis, B.
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Giocoli, C.
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Gracia-Carpio, J.
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Grazian, A.
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Grupp, F.
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Haugan, S. V. H.
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Holmes, W.
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Hormuth, F.
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Hornstrup, A.
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Jahnke, K.
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Jhabvala, M.
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Kermiche, S.
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Kiessling, A.
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Kubik, B.
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Kunz, M.
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Kurki-Suonio, H.
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Le Brun, A. M. C.
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Ligori, S.
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Lilje, P. B.
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Lloro, I.
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Mainetti, G.
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Maiorano, E.
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Mansutti, O.
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Marcin, S.
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Marggraf, O.
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Martinelli, M.
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Martinet, N.
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Marulli, F.
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Massey, R. J.
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Medinaceli, E.
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Mei, S.
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Melchior, M.
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Meneghetti, M.
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Merlin, E.
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Meylan, G.
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Mora, A.
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Moresco, M.
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Moscardini, L.
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Nakajima, R.
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Neissner, C.
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Niemi, S.-M.
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Padilla, C.
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Paltani, S.
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Pasian, F.
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Pedersen, K.
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Pettorino, V.
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Pires, S.
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Polenta, G.
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Poncet, M.
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Popa, L. A.
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Raison, F.
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Renzi, A.
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Rhodes, J.
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Riccio, G.
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Romelli, E.
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Roncarelli, M.
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Rosset, C.
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Saglia, R.
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Sakr, Z.
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Sánchez, A. G.
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Sapone, D.
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Schneider, P.
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Schrabback, T.
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Secroun, A.
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Seidel, G.
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Simon, P.
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Sirignano, C.
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Sirri, G.
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Stanco, L.
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Tallada-Crespí, P.
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Taylor, A. N.
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Tereno, I.
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Toft, S.
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Toledo-Moreo, R.
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Torradeflot, F.
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Tutusaus, I.
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Vassallo, T.
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Verdoes Kleijn, G.
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Wang, Y.
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Weller, J.
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Zamorani, G.
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Zucca, E.
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Castro, T.
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Martín-Fleitas, J.
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Monaco, P.
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Pezzotta, A.
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Scottez, V.
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Sereno, M.
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Viel, M.
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Sciotti, D.
Abstract
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We study the properties of galaxy cluster two-point correlation function covariance matrices estimated using the linear-construction (LC) method, which is computationally up to 20 times faster than the standard sample-covariance method. Our goal is to assess how well the LC method performs in cosmological parameter estimation compared to the sample covariance. We used a set of 1000 mock dark matter halo catalogues to compute both the LC-covariance and the sample-covariance estimates in four redshift shells. These numerical matrices were used to fit a theoretical four-parameter model for the covariance. We then used the two fitted covariance models in a likelihood function to estimate two cosmological parameters -- the matter density parameter Ωm and the amplitude of the matter density fluctuations σ8 -- from the simulated mock catalogues. The purpose of this was to validate the LC-covariance-based model against the sample-covariance model. The catalogues were simulated assuming the spatially flat ΛCDM cosmology, with Ωm = 0.30711 and σ8 = 0.8288. We find that the parameter posteriors obtained using the sample- and LC-covariance models agree well with each other and with the simulation cosmology. The two pairs of marginalised constraints are Ωm = 0.307 ± 0.003 and σ8 = 0.826 ± 0.009 (sample covariance), and Ωm = 0.308 ± 0.003 and σ8 = 0.825 ± 0.009 (LC covariance). The posterior widths are the same, and the difference in the median values is less than 0.16σ for both parameters. ★ This paper is published on behalf of the Euclid Consortium.
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