BibTeX

@article{2403.02314v1,
Author        = {N. Jeffrey and L. Whiteway and M. Gatti and J. Williamson and J. Alsing and A. Porredon and J. Prat and C. Doux and B. Jain and C. Chang and T. -Y. Cheng and T. Kacprzak and P. Lemos and A. Alarcon and A. Amon and K. Bechtol and M. R. Becker and G. M. Bernstein and A. Campos and A. Carnero Rosell and R. Chen and A. Choi and J. DeRose and A. Drlica-Wagner and K. Eckert and S. Everett and A. Ferté and D. Gruen and R. A. Gruendl and K. Herner and M. Jarvis and J. McCullough and J. Myles and A. Navarro-Alsina and S. Pandey and M. Raveri and R. P. Rollins and E. S. Rykoff and C. Sánchez and L. F. Secco and I. Sevilla-Noarbe and E. Sheldon and T. Shin and M. A. Troxel and I. Tutusaus and T. N. Varga and B. Yanny and B. Yin and J. Zuntz and M. Aguena and S. S. Allam and O. Alves and D. Bacon and S. Bocquet and D. Brooks and L. N. da Costa and T. M. Davis and J. De Vicente and S. Desai and H. T. Diehl and I. Ferrero and J. Frieman and J. García-Bellido and E. Gaztanaga and G. Giannini and G. Gutierrez and S. R. Hinton and D. L. Hollowood and K. Honscheid and D. Huterer and D. J. James and O. Lahav and S. Lee and J. L. Marshall and J. Mena-Fernández and R. Miquel and A. Pieres and A. A. Plazas Malagón and A. Roodman and M. Sako and E. Sanchez and D. Sanchez Cid and M. Smith and E. Suchyta and M. E. C. Swanson and G. Tarle and D. L. Tucker and N. Weaverdyck and J. Weller and P. Wiseman and M. Yamamoto},
Title         = {Dark Energy Survey Year 3 results: likelihood-free, simulation-based $w$CDM inference with neural compression of weak-lensing map statistics},
Eprint        = {2403.02314v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {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.},
Year          = {2024},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2403.02314v1},
File          = {2403.02314v1.pdf}
}