Results 41 to 50 of about 170,764 (173)
Cosmology through arc statistics I: sensitivity to $\Omega_m$ and
$\sigma_8$ [PDF]
, 2016 The next generation of large sky photometric surveys will finally be able to
use arc statistics as a cosmological probe. Here we present the first of a
series of papers on this topic.Biviano, Andrea, Boldrin, Michele, Giocoli, Carlo, Meneghetti, Massimo, Moscardini, Lauro, Tormen, Giuseppe +5 morecore +4 more sourcesA Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
The Astrophysical Journal, 2023 We discover analytic equations that can infer the value of Ω _m from the positions and velocity moduli of halo and galaxy catalogs. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression.Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars Hernquist, Tiago Castro, Mark Vogelsberger +16 moredoaj +1 more sourcePlanck2013 results. XVI. Cosmological parameters [PDF]
Astronomy & Astrophysics, 2014 We present the first results based on Planck measurements of the CMB temperature and lensing-potential power spectra. The Planck spectra at high multipoles are extremely well described by the standard spatially-flat six-parameter LCDM cosmology. In this model Planck data determine the cosmological parameters to high precision.Tauber, Jan, Ade, P. A. R., Aghanim, N., Armitage Caplan, C., Arnaud, M., Ashdown, M., Atrio Barandela, F., Aumont, J., Baccigalupi, C., Banday, A. J., Barreiro, R. B., Bartlett, J. G., Battaner, E., Benabed, K., Benoît, A., Benoit Lévy, A., Bernard, J. P., Bersanelli, M., Bielewicz, P., Bobin, J., Bock, J. J., Bonaldi, A., Bond, J. R., Borrill, J., Bouchet, F. R., Bridges, M., Bucher, M., Burigana, C., Butler, R. C., Calabrese, E., Cappellini, B., Cardoso, J. F., Catalano, A., Challinor, A., Chamballu, A., Chary, R. R., Chen, X., Chiang, H. C., Chiang, L. Y., Christensen, P. R., Church, S., Clements, D. L., Colombi, S., Colombo, L. P. L., Couchot, F., Coulais, A., Crill, B. P., Curto, A., Cuttaia, F., Danese, L., Davies, R. D., Davis, R. J., De Bernardis, P., De Rosa, A., De Zotti, G., Delabrouille, J., Delouis, J. M., Désert, F. X., Dickinson, C., Diego, J. M., Dolag, K., Dole, H., Donzelli, S., Doré, O., Douspis, M., Dunkley, J., Dupac, X., Efstathiou, G., Elsner, F., Enßlin, T. A., Eriksen, H. K., Finelli, F., Forni, O., Frailis, M., Fraisse, A. A., Franceschi, E., Gaier, T. C., Galeotta, S., Galli, S., Ganga, K., Giard, M., Giardino, G., Giraud Héraud, Y., Gjerløw, E., González Nuevo, J., Górski, K. M., Gratton, S., Gruppuso, A., Gudmundsson, J. E., Haissinski, J., Hamann, J., Hansen, F. K., Hanson, D., Harrison, D., Henrot Versillé, S., Hernández Monteagudo, C., Herranz, D., Hildebrandt, S. R., Hivon, E., Hobson, M., Holmes, W. A., Hornstrup, A., Hou, Z., Hovest, W., Huffenberger, K. M., Jaffe, A. H., Jaffe, T. R., Jewell, J., Jones, W. C., Juvela, M., Keihänen, E., Keskitalo, R., Kisner, T. S., Kneissl, R., Knoche, J., Knox, L., Kunz, M., Kurki Suonio, H., Lagache, G., Lähteenmäki, A., Lamarre, J. M., Lasenby, A., Lattanzi, M., Laureijs, R. J., Lawrence, C. R., Leach, S., Leahy, J. P., Leonardi, R., León Tavares, J., Lesgourgues, J., Lewis, A., Liguori, M., Lilje, P. B., Linden Vørnle, M., López Caniego, M., Lubin, P. M., Maciás Pérez, J. F., Maffei, B., Maino, D., Mandolesi, N., Maris, M., Marshall, D. J., Martin, P. G., Martínez González, E., Masi, S., Massardi, M., Matarrese, S., Matthai, F., Mazzotta, P., Meinhold, P. R., Melchiorri, A., Melin, J. B., Mendes, L., Menegoni, E., Mennella, A., Migliaccio, M., Millea, M., Mitra, S., Miville Deschênes, M. A., Moneti, A., Montier, L., Morgante, G., Mortlock, D., Moss, A., Munshi, D., Murphy, J. A., Naselsky, P., Nati, F., Natoli, P., Netterfield, C. B., Nørgaard Nielsen, H. U., Noviello, F., Novikov, D., Novikov, I., O'Dwyer, I. J., Osborne, S., Oxborrow, C. A., Paci, F., Pagano, L., Pajot, F., Paladini, R., Paoletti, D., Partridge, B., Pasian, F., Patanchon, G., Pearson, D., Pearson, T. J., Peiris, H. V., Perdereau, O., Perotto, L., Perrotta, F., Pettorino, V., Piacentini, F., Piat, M., Pierpaoli, E., Pietrobon, D., Plaszczynski, S., Platania, P., Pointecouteau, E., Polenta, G., Ponthieu, N., Prunet, S., Rachen, J. P., Reach, W. T., Rebolo, R., Remazeilles, M., Ricciardi, S., Rocha, G., Rubinõ Martín, J. A., Rusholme, B., Sandri, M., Savini, G., Scott, D., Starck, J. L., Stolyarov, V., Sureau, F., Suur Uski, A. S., Terenzi, L., Toffolatti, L., Tomasi, M., Tristram, M., Türler, M., Umana, G., Valenziano, L., Valiviita, J., Vielva, P., Villa, F., Vittorio, N., Wandelt, B. D., Wehus, I. K., White, M., Yvon, D., Zacchei, A., GREGORIO, ANNA, TAVAGNACCO, DANIELE +234 moreopenaire +21 more sourcesDynamics of a Generalized Cosmological Scalar-Tensor Theory [PDF]
, 2002 A generalized scalar-tensor theory is investigated whose cosmological term
depends on both a scalar field and its time derivative. A correspondence with
solutions of five-dimensional Space-Time-Matter theory is noted. Analytic
solutions are found for the Banerjee A., JAMES M. OVERDUIN, Linde A. D., Ratra B., Sahni V., Silveira V., TAKAO FUKUI, Wesson P. S. +7 morecore +3 more sourcesThe BINGO/ABDUS Project: Forecast for Cosmological Parameters from a Mock Fast Radio Burst Survey
The Astrophysical JournalThere are various surveys that will provide excellent data to search for and localize fast radio bursts (FRBs). The BINGO project will be one such survey, and this collaboration has already estimated an FRB detection rate that the project will yield.Xue Zhang, Yu Sang, Gabriel A. Hoerning, Filipe B. Abdalla, Elcio Abdalla, Amilcar Queiroz, André A. Costa, Ricardo G. Landim, Chang Feng, Bin Wang, Marcelo V. dos Santos, Thyrso Villela, Carlos A. Wuensche, Jiajun Zhang, Edmar C. Gurjão, Alessandro Marins, Alexandre J. R. Serres, Linfeng Xiao +17 moredoaj +1 more sourceCrossing Statistic: Bayesian interpretation, model selection and
resolving dark energy parametrization problem
, 2012 By introducing Crossing functions and hyper-parameters I show that the
Bayesian interpretation of the Crossing Statistics [1] can be used trivially
for the purpose of model selection among cosmological models.A. Shafieloo, Arman Shafieloo, I. Gott, J. Sollerman, M. Kilbinger ., R.A. Daly, R.G. Crittenden, R.J. Barlow, T.M. Davis ., Y. Wang +9 morecore +1 more source
