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The cosmological analysis of DES 3×2pt data from the Effective Field Theory of Large-Scale Structure

Title: The cosmological analysis of DES 3×2pt data from the Effective Field Theory of Large-Scale Structure
Authors: D'Amico, Guido; Refregier, Alexandre; Senatore, Leonardo; Zhang, Pierre
Source: Journal of Cosmology and Astroparticle Physics ; volume 2026, issue 03, page 062 ; ISSN 1475-7516
Publisher Information: IOP Publishing
Publication Year: 2026
Description: We analyze the Dark Energy Survey (DES) Year 3 data using predictions from the Effective Field Theory of Large-Scale Structure (EFTofLSS). Specifically, we fit three two-point observables (3×2pt), galaxy clustering, galaxy-galaxy lensing, and cosmic shear, using the one-loop expressions for the projected angular correlation functions. We validate our pipeline against numerical simulations and we check for several internal consistencies before applying it to the observational data. Fixing the spectral tilt and the baryons abundance, we measure S 8 = 0.833 ± 0.032, Ω m = 0.272 ± 0.022, and h = 0.773 ± 0.049, to about 3.8%, 8.1%, and 6.3%, at 68%CL, respectively. Our results are consistent at the ∼ 1.5–2 σ level with those from Planck and the BOSS full-shape analyses, as well as with those from DES collaboration 3×2pt analysis combined with a Big-Bang Nucleosynthesis prior and a Planck prior on n s . The difference in the posteriors compared to the DES collaboration results, obtained from the same dataset combinations, highlights the impact of modeling, scale cuts, and choice of prior. The theory code and likelihood used for our analyses, PyFowl , is made publicly available.
Document Type: article in journal/newspaper
Language: unknown
DOI: 10.1088/1475-7516/2026/03/062
DOI: 10.1088/1475-7516/2026/03/062/pdf
Availability: https://doi.org/10.1088/1475-7516/2026/03/062; https://iopscience.iop.org/article/10.1088/1475-7516/2026/03/062; https://iopscience.iop.org/article/10.1088/1475-7516/2026/03/062/pdf
Rights: http://creativecommons.org/licenses/by/4.0/ ; https://iopscience.iop.org/info/page/text-and-data-mining
Accession Number: edsbas.2E511DE2
Database: BASE