[论文解读] Cosmological constraints from a joint DESI DR1 Full-Shape and DR2 BAO

D. Forero-Sánchez, H. Gil-Marín+62 more|arXiv (Cornell University)|Feb 21, 2026

Galaxies: Formation, Evolution, Phenomena被引用 0

一句话总结

本论文提出仅用 DESI 的 DESI-only 宇宙学分析，结合 DR1 全形态与 DR2 BAO 的 ShapeFit 压缩，得到对 ΛCDM 及超 ΛCDM 的稳健约束，且尽量减小先验体积效应。

ABSTRACT

We present a cosmological analysis combining full-shape (FS) clustering measurements from the Dark Energy Spectroscopic Instrument (DESI) DR1 with baryon acoustic oscillation (BAO) measurements from DESI DR2. To achieve a robust combination that accounts for the correlation between the two data releases, we employ the ShapeFit compression method and estimate the joint covariance using EZmocks. This compressed approach inherently mitigates the prior volume effects that have previously dominated Bayesian constraints from DESI data with minimal external priors. Consequently, we obtain--for the first time within a Bayesian framework--reliable DESI-only constraints on extensions to $Λ$CDM using only a Big Bang Nucleosynthesis prior on the baryon density and a wide prior on the spectral index. In flat $Λ$CDM, we find $Ω_m = 0.3035 \pm 0.0085$, $h = 0.6876 \pm 0.0059$, and $σ_8 = 0.822 \pm 0.034$. For the $w_0 w_a$CDM dynamical dark energy model, we measure $w_0 = 0.49 \pm 0.25$ and $w_a = -1.52 \pm 0.77$, improving constraints by $\sim 30\%$ relative to the analogous DR1 measurement and reducing the discrepancy with $Λ$CDM to $1.4σ$ when compared to BAO only analyses. We also report competitive limits on the sum of neutrino masses and spatial curvature. This work demonstrates that the ShapeFit compression provides a prior-robust and computationally efficient pathway to constrain beyond-$Λ$CDM physics with large-scale structure.

研究动机与目标

• Motivate DESI’s standalone constraints on ΛCDM and beyond using DR1 FS and DR2 BAO data.
• Develop a robust, prior-volume-robust combination method accounting for DR1/DR2 correlations via ShapeFit compression.
• Demonstrate that ShapeFit provides reliable constraints with minimal external priors.
• Assess cosmological extensions (w0waCDM, neutrino mass, curvature) and compare to BAO-only analyses.

提出的方法

• Use ShapeFit to compress DESI DR1 Full-Shape data into a small set of observables (qSF iso, qSF ap, dm, df).
• Use BAO analysis on DR2 data to obtain compressed parameters (qBAO iso, qBAO ap) via post-reconstruction 2-point function fits.
• Estimate joint DR1×DR2 covariance with EZmock catalogs and explore three covariance options (A, B, C) for cross-terms.
• Combine DESI DR1 FS and DR2 BAO (and Lyα FS where relevant) into a joint likelihood using the compressed parameter vectors.
• Adopt cosmological priors: Big Bang Nucleosynthesis (BBN) on ωb and a broad ns prior (ns10) to enable DESI-only constraints without CMB priors.
• Incorporate external datasets where appropriate (PantheonPlus SNe, Planck/CMB lensing) to contextualize results.

实验结果

研究问题

• RQ1 What are DESI DR1 Full-Shape and DR2 BAO constraints on standard ΛCDM parameters when analyzed jointly in a Bayesian framework using ShapeFit?
• RQ2 How does ShapeFit compression mitigate prior-volume effects relative to Full-Modeling analyses for DESI data?
• RQ3 What are the constraints on extensions to ΛCDM (w0 waCDM, neutrino mass sum, spatial curvature) from DESI DR1 FS + DR2 BAO data alone?
• RQ4 How do DR1×DR2 cross-correlations affect cosmological inferences under compressed vs full data analyses?

主要发现

• In flat ΛCDM, the joint analysis yields Ωm = 0.3035 ± 0.0085, h = 0.6876 ± 0.0059, and σ8 = 0.822 ± 0.034.
• For the w0waCDM model, constraints are w0 = −0.49 ± 0.25 and wa = −1.52 ± 0.77, improving DR1 constraints by ~30% and reducing BAO-only discrepancy to 1.4σ.
• The analysis reports competitive limits on the sum of neutrino masses and spatial curvature.
• ShapeFit compression provides prior-robust, computationally efficient, DESI-only constraints with minimal external priors (BBN on ωb and ns10).
• DR1×DR2 cross-covariance has limited impact on ΛCDM inferences when using compressed parameters, suggesting robustness of the approach.