Results 21 to 30 of about 171,556 (244)

Cosmological parameter estimation: methods [PDF]

Comptes Rendus. Physique, 2003
CMB anisotropy data could put powerful constraints on theories of the evolution of our Universe. Using the observations of the large number of CMB experiments, many studies have put constraints on cosmological parameters assuming different frameworks. Assuming for example inflationary paradigm, one can compute the confidence intervals on the different ...
openaire   +3 more sources

CosmoSIS: Modular cosmological parameter estimation [PDF]

Astronomy and Computing, 2015
Finally got around to updating to refereed version.
J. Zuntz, M. Paterno, E. Jennings, D. Rudd, A. Manzotti, S. Dodelson, S. Bridle, S. Sehrish, J. Kowalkowski   +8 more
openaire   +3 more sources

Investigating Cosmological Models and the Hubble Tension Using Localized Fast Radio Bursts

The Astrophysical Journal, 2023
We use the dispersion measure (DM) and redshift measurements of 24 localized fast radio bursts (FRBs) to compare cosmological models and investigate the Hubble tension.
Jun-Jie Wei, Fulvio Melia
doaj   +1 more source

Planck 2018 results [PDF]

Astronomy & Astrophysics, 2020
We present cosmological parameter results from the final full-mission Planck measurements of the cosmic microwave background (CMB) anisotropies, combining information from the temperature and polarization maps and the lensing reconstruction. Compared to the 2015 results, improved measurements
Collaboration, Planck, Aghanim, N., Akrami, Y., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., Banday, A. J., Barreiro, R. B., Bartolo, N., Basak, S., Battye, R., Benabed, K., Bernard, J.-P., Bersanelli, M., Bielewicz, P., Bock, J. J., Bond, J. R., Borrill, J., Bouchet, F. R., Boulanger, F., Bucher, M., Burigana, C., Butler, R. C., Calabrese, E., Cardoso, J.-F., Carron, J., Challinor, A., Chiang, H. C., Chluba, J., Colombo, L. P. L., Combet, C., Contreras, D., Crill, B. P., Cuttaia, F., de Bernardis, P., de Zotti, G., Delabrouille, J., Delouis, J.-M., Di Valentino, E., Diego, J. M., Doré, O., Douspis, M., Ducout, A., Dupac, X., Dusini, S., Efstathiou, G., Elsner, F., Enßlin, T. A., Eriksen, H. K., Fantaye, Y., Farhang, M., Fergusson, J., Fernandez-Cobos, R., Finelli, F., Forastieri, F., Frailis, M., Fraisse, A. A., Franceschi, E., Frolov, A., Galeotta, S., Galli, S., Ganga, K., Génova-Santos, R. T., Gerbino, M., Ghosh, T., González-Nuevo, J., Górski, K. M., Gratton, S., Gruppuso, A., Gudmundsson, J. E., Hamann, J., Handley, W., Hansen, F. K., Herranz, D., Hildebrandt, S. R., Hivon, E., Huang, Z., Jaffe, A. H., Jones, W. C., Karakci, A., Keihänen, E., Keskitalo, R., Kiiveri, K., Kim, J., Kisner, T. S., Knox, L., Krachmalnicoff, N., Kunz, M., Kurki-Suonio, H., Lagache, G., Lamarre, J.-M., Lasenby, A., Lattanzi, M., Lawrence, C. R., Le Jeune, M., Lemos, P., Lesgourgues, J., Levrier, F., Lewis, A., Liguori, M., Lilje, P. B., Lilley, M., Lindholm, V., López-Caniego, M., Lubin, P. M., Ma, Y.-Z., Macías-Pérez, J. F., Maggio, G., Maino, D., Mandolesi, N., Mangilli, A., Marcos-Caballero, A., Maris, M., Martin, P. G., Martinelli, M., Martínez-González, E., Matarrese, S., Mauri, N., McEwen, J. D., Meinhold, P. R., Melchiorri, A., Mennella, A., Migliaccio, M., Millea, M., Mitra, S., Miville-Deschênes, M.-A., Molinari, D., Montier, L., Morgante, G., Moss, A., Natoli, P., Nørgaard-Nielsen, H. U., Pagano, L., Paoletti, D., Partridge, B., Patanchon, G., Peiris, H. V., Perrotta, F., Pettorino, V., Piacentini, F., Polastri, L., Polenta, G., Puget, J.-L., Rachen, J. P., Reinecke, M., Remazeilles, M., Renzi, A., Rocha, G., Rosset, C., Roudier, G., Rubiño-Martín, J. A., Ruiz-Granados, B., Salvati, L., Sandri, M., Savelainen, M., Scott, D., Shellard, E. P. S., Sirignano, C., Sirri, G., Spencer, L. D., Sunyaev, R., Suur-Uski, A.-S., Tauber, J. A., Tavagnacco, D., Tenti, M., Toffolatti, L., Tomasi, M., Trombetti, T., Valenziano, L., Valiviita, J., Van Tent, B., Vibert, L., Vielva, P., Villa, F., Vittorio, N., Wandelt, B. D., Wehus, I. K., White, M., White, S. D. M., Zacchei, A., Zonca, A.   +181 more
openaire   +16 more sources

Cosmological Parameters from Velocities, CMB and Supernovae [PDF]

, 2000
We compare and combine likelihood functions of the cosmological parameters Omega_m, h and sigma_8, from peculiar velocities, CMB and type Ia supernovae. These three data sets directly probe the mass in the Universe, without the need to relate the galaxy ...
A. Dekel, A. N. Lasenby, Bahcall, Dodelson, Gawiser, I. Zehavi, M. P. Hobson, O. Lahav, S. L. Bridle   +8 more
core   +2 more sources

Cosmological Constraints from Galaxy Clustering and the Mass-to-Number Ratio of Galaxy Clusters: Marginalizing over the Physics of Galaxy Formation

, 2013
Many approaches to obtaining cosmological constraints rely on the connection between galaxies and dark matter. However, the distribution of galaxies is dependent on their formation and evolution as well as the cosmological model, and galaxy formation is ...
Lu, Yu, Reddick, Rachel, Tinker, Jeremy, Wechsler, Risa   +3 more
core   +2 more sources

Cosmological parameter estimation and the inflationary cosmology [PDF]

Physical Review D, 2002
15 pages RevTeX file with figures incorporated. Slow-roll inflation module for use with the CAMB program can be found at http://astronomy.cpes.susx.ac.uk/~sleach/inflation/ This version corrects a typo in the definition of z_S (after Eq.1) and supersedes the journal ...
Leach, SM, Liddle, AR, Martin, J, Schwarz, Dominik   +3 more
openaire   +2 more sources

Exploring hints for dark energy density evolution in light of recent data

Physics Letters B, 2015
Considering a quadratic parameterization of the dark energy density, we explore signatures of evolution using data from gas mass fraction in clusters, type Ia supernova, BAO and CMB.
Víctor H. Cárdenas
doaj   +1 more source

Overcoming the Circular Problem for \gamma-ray Bursts in Cosmological Global Fitting Analysis [PDF]

, 2008
Due to the lack of low redshift long Gamma-Ray Bursts (GRBs), the circular problem has been a severe obstacle for using GRBs as cosmological candles.
Amati L., Firmani C., Gong‐Bo Zhao, Hong Li, Jie Liu, Jun‐Qing Xia, Masetti N., Xinmin Zhang, Zu‐Hui Fan   +8 more
core   +1 more source

Observational Cosmology with Artificial Neural Networks

Universe, 2022
In cosmology, the analysis of observational evidence is very important when testing theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and have recently been considered in the ...
Juan de Dios Rojas Olvera, Isidro Gómez-Vargas, Jose Alberto Vázquez   +2 more
doaj   +1 more source