利用AIC与BIC选择方法结合DESI DR2检验线性与二次暗能量参数化方案
Examining the Linear and Quadratic Dark Energy Parameterizations with DESI DR2 via AIC and BIC Selection
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中文总结 AI 辅助
该研究结合DESI DR2等多组数据，利用AIC与BIC准则检验线性、二次暗能量参数化方案，发现两参数模型是检验动力学暗能量特征的最优选择。
中文摘要 AI 辅助
我们通过在暗能量状态方程（EoS）参数w(z)中考虑更高阶的二次扩展，研究DESI当前报道的对动力学暗能量（DE）的观测偏好是否是所选暗能量EoS函数形式的人为结果。我们探讨三类不同的暗能量模型参数化方案：含两个参数的线性Chevallier-Polarski-Linder（CPL）模型、Wang模型，以及含三个参数的抛物线型（二次）模型。利用Planck和ACT DR6的CMB测量数据、DESI DR2的重子声学振荡数据，以及Pantheon+和DES-SN5YR的Ia型超新星数据构成的联合数据集，我们对模型参数进行约束，发现这些动力学模型的最大频率论偏好达约4σ。具体而言，CMB+DESI+DES-SN5YR的联合拟合相比ΛCDM模型有显著改善，CPL、Wang和抛物线模型的最小卡方差值Δχ²_min分别为-20.5、-19.4和-21.7。此外，赤池信息准则（AIC）对ΛCDM模型仅提供微弱或无证据支持，始终偏向动力学暗能量模型；而贝叶斯信息准则（BIC）对所有三类模型施加了严重惩罚，反映出其对数据集规模和模型复杂度的高度敏感性。我们发现BIC对CPL和Wang模型至多有微弱支持，但强烈不支持抛物线模型（ΔBIC约为+6.4至+15.6）。此外，由于参数空间扩展，抛物线模型的优值（FoM）相比两参数模型显著降低，表明当前宇宙学数据集尚不支持引入更高阶的暗能量模型。因此，w(z)的两参数描述仍是检验动力学暗能量特征的最优选择。
英文摘要
We examine whether or not the current observational preference for a dynamical dark energy(DE), as reported by DESI, is an artifact of the chosen functional form of dark energy equation of state (EoS) parameter $w(z)$ by considering a higher-order quadratic extension in EoS. We explore three distinct parameterizations of the DE models: the two-parameter (linear) Chevallier-Polarski-Linder (CPL) and Wang models, alongside a three-parameter parabolic (quadratic) formulation. Using a joint dataset of CMB measurements from Planck and ACT DR6, baryon acoustic oscillations of DESI DR2, and Type Ia supernovae from Pantheon+ and DES-SN5YR, we constrain the model parameters and observe a maximum frequentist preference of up to $\sim 4σ$ for these dynamical models. Specifically, the joint CMB + DESI + DES-SN5YR combination yields a substantial improvement in fit over $Λ\text{CDM}$, with $Δχ^2_{\text{min}}$ values of $-20.5$, $-19.4$, and $-21.7$ for CPL, Wang, and Parabolic models, respectively. In addition, the Akaike Information Criterion (AIC) provides weak to no evidence for $Λ$CDM, consistently favoring the dynamical DE models instead. On the other hand, the Bayesian Information Criterion (BIC) imposes a heavy penalty on all the three models, reflecting its high sensitivity to dataset size and model complexity. We find at best a mild support for CPL and Wang models but the Parabolic model is strongly disfavored ($Δ\text{BIC} \sim +6.4$ to $+15.6$). Furthermore, due to its expanded parameter space, the Parabolic model exhibits a significant reduction in the Figure of Merit (FoM) compared to the two-parameter counterparts, demonstrating that the current cosmological datasets do not yet justify an introduction of the higher-order DE models. So, as a result, the two-parameter descriptions of $w(z)$ remain the optimal choice for testing the dynamical DE signatures.
