标题： 用DESI DR2探测广义涌现暗能量 显示英文标题

标题： Probing Generalized Emergent Dark Energy with DESI DR2

摘要： 作为对DESI初始发现的更新，新结果提供了宇宙学常数（$w=-1$）可能存在偏离的第一个线索，如果以显著性$>5\sigma$得到确认，将推翻$\Lambda$CDM 模型。 我们探讨了一个涌现暗能量（GEDE）模型的广义表述，并使用多个观测数据集测试其可行性，包括 DESI DR2 发布的重子声学振荡（BAO）测量值、Ia 型超新星（SNe Ia）数据集以及宇宙微波背景（CMB）距离先验。 通过这项分析，我们对暗能量状态方程$w$和红移相关的能量密度函数$f_{DE}(z)$进行了联合约束，其行为与第五元素一致。 具体来说，$w(0) = -0.820$与 DESI DR2 + CMB，$-0.845$与 DESI DR2 + CMB + PP$^+$，$-0.828$与 DESI DR2 + CMB + Union3，以及$-0.804$与 DESI DR2 + CMB + DESY5。在多个数据集组合中，我们观察到 GEDE 参数$\Delta$倾向于负值的重复趋势。 负值的$\Delta$有利于在较早的红移下注入暗能量，且$z_t$接近 0.5。 我们使用不同的 BAO 尺度对 GEDE 和$\Lambda$CDM 模型进行了比较分析。 在当前的观测不确定性范围内，我们的结果将 GEDE 框架定位为一种可行且有前景的替代方案，值得进一步研究。 该模型的效果通过贝叶斯因子进行了定量评估。 显示英文摘要

摘要： As an update on the initial findings of DESI, the new results provide the first hint of potential deviations from a cosmological constant ($w=-1$), which, if confirmed with significance $>5\sigma$, will falsify the $\Lambda$CDM model. We explore a generalized formulation of an emergent dark energy (GEDE) model and test its viability using multiple observational datasets, including Baryon Acoustic Oscillation (BAO) measurements from the DESI DR2 release, the Type Ia Supernovae (SNe Ia) compilation, and Cosmic Microwave Background (CMB) distance priors. Through this analysis, we place joint constraints on the dark energy equation of state $w$ and the redshift-dependent energy density function $f_{DE}(z)$, which exhibits behavior consistent with a quintessence. Specifically, $w(0) = -0.820$ with DESI DR2 + CMB, $-0.845$ with DESI DR2 + CMB + PP$^+$, $-0.828$ with DESI DR2 + CMB + Union3, and $-0.804$ with DESI DR2 + CMB + DESY5. Across multiple combinations of datasets, we observe a recurring trend toward negative values of the GEDE parameter $\Delta$. The negative value of $\Delta$ favors the injection of dark energy at earlier redshift with $z_t$ close to 0.5. We conduct a comparative analysis between the GEDE and $\Lambda$CDM models using different BAO scale. Within current observational uncertainties, our results position the GEDE framework as a viable and promising alternative, meriting further investigation. The effectiveness of the model is quantitatively assessed using the Bayes factor.