发表机构
School of Engineering, Xi’an International University（西安国际大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究 DESI DR2 BAO 与其他数据结合对暗能量的偏好，采用分层高斯过程重建晚期膨胀历史，联合多种数据通过蒙特卡罗有效似然法分析，得出特定于压缩 CMB 分析的暗能量偏好结果，未涉及 DESI 完整普朗克似然结果。
AI 中文摘要
DESI DR2 BAO 与 CMB 和 Ia 型超新星在 Chevallier--Polarski--Linder（CPL）参数化中，倾向于演化暗能量而非 ΛCDM，显著性约为 2.8 - 4.2σ。我们用分层高斯过程重建晚期膨胀历史，联合 BAO、压缩普朗克距离先验和 Pantheon+，通过蒙特卡罗有效似然法，以 37 个宇宙计时仪 H(z)点为条件。基线后验给出 w(z≃0)= -0.80±0.26（68%置信水平），与 w = -1 相差约 0.8σ，l = 3.79±0.81。CPL 拟合对嵌套 ΛCDM 的改进仅约 Δχ² ∼ 1（约 1σ）。固定(σ_f,l)等消融操作使 w(0)中位数在距 -1 的约 1σ 范围内。此偏好特定于压缩 CMB 分析，未涉及 DESI 的完整普朗克似然结果。
英文摘要
We reconstruct the late-time expansion history with a hierarchical Gaussian process (GP) that co-samples the kernel hyperparameters $(σ_f,l)$ together with $(\Hzero,\Om,\Ok,ω_b h^2)$. The data are 37 cosmic-chronometer $H(z)$ points, DESI DR2 BAO, Planck 2018 compressed distance priors, and Pantheon+ supernovae, coupled by a Monte-Carlo effective likelihood. DESI's headline Chevallier--Polarski--Linder (CPL) preference at $2.8$--$4.2σ$ uses the full Planck likelihood; we do not reproduce or refute that result. On this compressed-CMB pipeline the baseline posterior is $w(z\simeq 0)=-0.80^{+0.26}_{-0.23}$ (68\%~C.L.), less than $1σ$ from $w=-1$, with $l=3.79^{+0.81}_{-1.12}$. A CPL fit on the same data improves nested $\lcdm$ by only $Δχ^2\sim 1$; Akaike's criterion prefers nested $\lcdm$ ($Δ\mathrm{AIC}\simeq 3$), while BIC is quoted only as an indicative check for correlated supernovae. The reconstructed $f_{\rm DE}=1$ lies inside the 68\% band at every grid point with $z\le 1.6$. Ablations that fix $(σ_f,l)$, drop SN or LRG1/2 BAO, replace the radial-basis kernel by a Matérn family, swap the SN catalogue (Union3, DES-Y5, DES-Dovekie), or nest a residual GP around $\lcdm$ leave the median $w(0)$ within $\lesssim 1σ$ of $-1$.
Comments17 pages, 3 figures
