发表机构
University of Toronto; Independent University, Bangladesh（多伦多大学; 孟加拉独立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过样本外预测的ELPD评分发现，$w_0w_a$暗能量模型的偏好源于单个BAO观测点，而非SNIa，解决了相关模型比较的张力问题。
AI 中文摘要
近期对DESI DR2 BAO、Planck CMB及各类超新星数据集的分析，在频率学派模型比较检验下，均显示出对演化暗能量的不同程度显著性偏好。然而，采用纯贝叶斯模型比较检验的相同分析，由于$w_0w_a$先验的影响，可能得出相反结论。与这些样本内模型比较路径不同，我们沿第三维度——哪个模型能更好地预测未见过的数据——来研究该问题。我们使用留一红移块交叉验证估计量，以期望对数预测密度（ELPD）度量对$\boldsymbol{\rm \u0394}$ELPD是样本外度量，与$\boldsymbol{\rm \u0394}\boldsymbol{\rm \u03c7}^2_{\rm MAP}$和贝叶斯因子不同，且与贝叶斯因子不同，它对先验宽度不敏感。汇总分析发现，对$w_0w_a$CDM存在适度偏好，该偏好主要由BAO驱动。按红移分解$\boldsymbol{\rm \u0394}$ELPD评分后发现，单个BAO块LRG2（$z=0.706$）提供了全部BAO偏好。值得注意的是，最大的异常点LRG1对模型预测评分贡献极小，因为$\boldsymbol{\rm \u039b}$CDM和$w_0w_a$CDM均以同等程度无法预测该观测。另一方面，所有SNIa数据集的$\boldsymbol{\rm \u0394}$ELPD评分均与0一致，表明在分布外预测层面，$w_0w_a$张力完全由一个BAO点驱动，而非SNIa。
英文摘要
The recent analyses of DESI DR2 BAO, Planck CMB and various Supernovae datasets have shown preferences for evolving dark energy at various levels of significance, under frequentist model comparison tests. However, the same analysis done with a pure Bayesian model comparison test can lead to the opposite conclusion due to the impact of $w_0w_a$ priors. In contrast to these in-sample model comparison route, we approach the problem along a third axis -- which model better predicts unseen data. We use the Expected Log Predictive Density (ELPD) metric to score the predictiveness of $Λ$CDM and $w_0w_a$CDM models, using the leave-one-redshift-block-out cross-validation estimator. The comparison metric $Δ$ELPD is out-of-sample, unlike $Δχ^2_{\rm MAP}$ and Bayes Factor, and insensitive to prior width, unlike the Bayes factor. Aggregated, we find modest preferences for $w_0 w_a$CDM, primarily driven by the BAO. Decomposing the $Δ$ELPD score by redshift we find that a single BAO block, LRG2 ($z = 0.706$), supplies the entire BAO preference. Interestingly, the biggest outlier point, LRG1, contributes little to the model predictive scoring because both $Λ$CDM and $w_0w_a$CDM fail to predict the observation by equal amount. On the other hand, all SNIa datasets' $Δ$ELPD scores are consistent with $0$, indicating that at an out-of-distribution predictive level, the $w_0w_a$CDM tension is entirely driven by one BAO point, not SNIa.
Comments9 pages, 3 figures, 2 tables, Submitted to ApJL
