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Autori principali: Lindholm, V., Sihvola, E., Valiviita, J., Fumagalli, A., Altieri, B., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Battaglia, P., Biviano, A., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., 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., Dole, H., Dubath, F., Dupac, X., Dusini, S., Escoffier, S., Farina, M., Farinelli, R., Ferriol, S., Finelli, F., Fosalba, P., Fotopoulou, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., George, K., Gillis, B., Giocoli, C., Gracia-Carpio, J., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Jhabvala, M., Kermiche, S., Kiessling, A., Kubik, B., Kunz, M., Kurki-Suonio, H., Brun, A. M. C. Le, Ligori, S., Lilje, P. B., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Marcin, S., Marggraf, O., Martinelli, M., Martinet, N., Marulli, F., Massey, R. J., Medinaceli, E., Mei, S., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Mora, A., Moresco, M., Moscardini, L., Nakajima, R., Neissner, C., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rosset, C., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Simon, P., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toft, S., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Vassallo, T., Kleijn, G. Verdoes, Wang, Y., Weller, J., Zamorani, G., Zucca, E., Castro, T., Martín-Fleitas, J., Monaco, P., Pezzotta, A., Scottez, V., Sereno, M., Viel, M., Sciotti, D.
Natura: Preprint
Pubblicazione: 2026
Soggetti:
Accesso online:https://arxiv.org/abs/2603.10735
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Sommario:
  • We study the properties of galaxy cluster 2-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 use 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 are used to fit a theoretical four-parameter model for the covariance. We then use the two fitted covariance models in a likelihood function to estimate two cosmological parameters - the matter density parameter $Ω_{\rm m}$ and the amplitude of the matter density fluctuations $σ_8$ - from the simulated mock catalogues. The purpose of this is to validate the LC-covariance-based model against the sample-covariance model. The catalogues were simulated assuming the spatially flat $Λ$CDM cosmology, with $Ω_{\rm m} = 0.30711$ and $σ_8=0.8288$. 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 marginalized constraints are $Ω_{\rm m} = 0.307 \pm 0.003$ and $σ_8 = 0.826\pm 0.009$ (sample covariance), and $Ω_{\rm m} = 0.308 \pm 0.003$ and $σ_8 = 0.825 \pm 0.009$ (LC covariance). The posterior widths are the same, and the difference in the median values is less than $0.16\,σ$ for both parameters.