Copertina

Salvato in:
Dettagli Bibliografici
Autori principali: Jeffrey, N., Whiteway, L., Gatti, M., Williamson, J., Alsing, J., Porredon, A., Prat, J., Doux, C., Jain, B., Chang, C., Cheng, T. -Y., Kacprzak, T., Lemos, P., Alarcon, A., Amon, A., Bechtol, K., Becker, M. R., Bernstein, G. M., Campos, A., Rosell, A. Carnero, Chen, R., Choi, A., DeRose, J., Drlica-Wagner, A., Eckert, K., Everett, S., Ferté, A., Gruen, D., Gruendl, R. A., Herner, K., Jarvis, M., McCullough, J., Myles, J., Navarro-Alsina, A., Pandey, S., Raveri, M., Rollins, R. P., Rykoff, E. S., Sánchez, C., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M. A., Tutusaus, I., Varga, T. N., Yanny, B., Yin, B., Zuntz, J., Aguena, M., Allam, S. S., Alves, O., Bacon, D., Bocquet, S., Brooks, D., da Costa, L. N., Davis, T. M., De Vicente, J., Desai, S., Diehl, H. T., Ferrero, I., Frieman, J., García-Bellido, J., Gaztanaga, E., Giannini, G., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., Huterer, D., James, D. J., Lahav, O., Lee, S., Marshall, J. L., Mena-Fernández, J., Miquel, R., Pieres, A., Malagón, A. A. Plazas, Roodman, A., Sako, M., Sanchez, E., Cid, D. Sanchez, Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Tucker, D. L., Weaverdyck, N., Weller, J., Wiseman, P., Yamamoto, M.
Natura: Preprint
Pubblicazione: 2024
Soggetti:
Cosmology and Nongalactic Astrophysics
Accesso online:https://arxiv.org/abs/2403.02314
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Sommario:
  • We present simulation-based cosmological $w$CDM inference using Dark Energy Survey Year 3 weak-lensing maps, via neural data compression of weak-lensing map summary statistics: power spectra, peak counts, and direct map-level compression/inference with convolutional neural networks (CNN). Using simulation-based inference, also known as likelihood-free or implicit inference, we use forward-modelled mock data to estimate posterior probability distributions of unknown parameters. This approach allows all statistical assumptions and uncertainties to be propagated through the forward-modelled mock data; these include sky masks, non-Gaussian shape noise, shape measurement bias, source galaxy clustering, photometric redshift uncertainty, intrinsic galaxy alignments, non-Gaussian density fields, neutrinos, and non-linear summary statistics. We include a series of tests to validate our inference results. This paper also describes the Gower Street simulation suite: 791 full-sky PKDGRAV dark matter simulations, with cosmological model parameters sampled with a mixed active-learning strategy, from which we construct over 3000 mock DES lensing data sets. For $w$CDM inference, for which we allow $-1<w<-\frac{1}{3}$, our most constraining result uses power spectra combined with map-level (CNN) inference. Using gravitational lensing data only, this map-level combination gives $Ω_{\rm m} = 0.283^{+0.020}_{-0.027}$, ${S_8 = 0.804^{+0.025}_{-0.017}}$, and $w < -0.80$ (with a 68 per cent credible interval); compared to the power spectrum inference, this is more than a factor of two improvement in dark energy parameter ($Ω_{\rm DE}, w$) precision.

Documenti analoghi