BibTeX

@article{2606.11309v1,
Author        = {J. Williamson and T. L. Makinen and N. Porqueres and N. Jeffrey and A. Heavens and M. Gatti and B. D. Wandelt and L. Whiteway and J. Prat 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 C. Doux and A. Drlica-Wagner and K. Eckert and S. Everett and A. Ferté and Z. Gong and D. Gruen and R. A. Gruendl and K. Herner and M. Jarvis and T. Kacprzak and O. Lahav 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 A. Thomsen and M. A. Troxel and I. Tutusaus and T. N. Varga and B. Yanny and B. Yin and J. Zuntz and T. M. C. Abbott and M. Aguena and F. Andrade-Oliveira and D. Brooks and R. Camilleri and J. Carretero and R. Cawthon and M. Crocce and L. N. da Costa and T. M. Davis and J. De Vicente and S. Desai and H. T. Diehl and B. Flaugher and J. Frieman and J. García-Bellido and G. Gutierrez and S. R. Hinton and D. L. Hollowood and K. Kuehn and J. L. Marshall and J. Mena-Fernández and R. Miquel and J. J. Mohr and J. Muir and A. A. Plazas Malagón and A. Porredon and A. Roodman and E. Sanchez and D. Sanchez Cid and E. Suchyta and M. E. C. Swanson and C. To and D. L. Tucker and V. Vikram and A. R. Walker and N. Weaverdyck and J. Weller},
Title         = {Dark Energy Survey Year 3 results: optimized $w$CDM simulation-based inference with weak lensing map-level hybrid statistics},
Eprint        = {2606.11309v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present cosmological constraints from the Dark Energy Survey Year 3 (DES Y3) weak lensing data using hierarchical hybrid statistics within a Bayesian simulation-based inference framework that is based on the Gower Street simulations. To maximize the precision of the inference, we have developed a new, information-theory based, data compression of the weak lensing maps to just seven highly informative summary statistics. The hybrid scheme exploits the high information content of the power spectrum, compressing both the power spectrum and neural-based summaries that are designed to extract further information. Our simulation-based approach enables principled forward modelling of all major sources of systematic uncertainty and survey properties into realistic mock observations, including the survey mask, photometric redshift uncertainties, intrinsic galaxy alignments, multiplicative shear calibration bias, source galaxy clustering, non-Gaussian shape noise, and non-linear structure formation. The summary statistics are then used in a Bayesian simulation-based inference pipeline. The inference is validated through coverage tests and checks for robustness against baryonic feedback. Assuming a $w$CDM cosmology, our analysis yields $S_8 = 0.808 \pm 0.017$, $Ω_{\rm m} = 0.325 \pm 0.024$, and $w < -0.766$ (marginalized posterior 68 per cent credible intervals). This rigorous combination of information theory, physics- and neural network-based extreme data compression, and principled Bayesian analysis improves the figure of merit for $(Ω_{\rm m}, S_8, w)$ by 60 per cent over the previous state-of-the-art, and by almost a factor of 3 over two-point analyses of the same data. They are the most precise joint constraints on $(Ω_{\rm m}, S_8, w)$ from weak gravitational lensing data alone of any survey to date. We intend to apply this analysis to the more recent DES Y6 data.},
Year          = {2026},
Month         = {Jun},
Url           = {http://arxiv.org/abs/2606.11309v1},
File          = {2606.11309v1.pdf}
}