AI 中文总结
针对DES第6年相关宇宙学探针分析，提出基于高斯混合近似的快速PPD一致性检验方法，引入标量度量Δ_PPD，能高效识别探针间张力，适用于未来大型巡天项目。
AI 中文摘要
我们提出了一个快速且数值稳定的框架，用于对相关宇宙学探针的内部一致性进行后验预测分布（PPD）检验。我们的方法是为暗能量巡天（DES）第6年弱透镜与星系聚集联合分析开发的验证检查，采用高斯混合模型高效近似高维PPD，并引入标量一致性度量$\u0394_{\ m PPD}$，定义为PPD中概率密度低于观测数据的比例。该方法避免了影响DES第3年所用PPD方法的校准和数值稳定性问题。在通过玩具模型展示性能后，我们确认当应用于DES第3年测量时，$\u0394_{\ m PPD}$识别出与已发表校准PPD结果相同的探针间张力，且每次测试仅需数分钟。我们进一步在模拟DES Y6分析的多个噪声实现上验证了该方法，表明$\u0394_{\ m PPD}$对注入的不一致性有连贯响应，同时强调了两个可能使日益复杂模型的PPD检验解释复杂化的效应。首先，可被吸收为扰动参数偏移的内部张力变得更难检测。其次，参数空间中约束较差的维度可能对不相交的观测量集合在PPD中产生投影效应，即使数据一致也会产生张力报告。我们得出结论，$\u0394_{\ m PPD}$为多探针解盲提供了一种实用的基于PPD的诊断方法，需与其他验证检查一起谨慎解读。该框架已在DES分析中发挥重要作用，非常适合应用于即将到来的第四阶段巡天项目，如LSST、Euclid和Roman，其中快速、稳健的内部一致性检验将是必不可少的。
英文摘要
We present a fast and numerically stable framework for conducting posterior predictive distribution (PPD) tests of internal consistency in correlated cosmological probes. Our method, developed as a validation check for the Dark Energy Survey (DES) Year 6 combined analysis of weak lensing and galaxy clustering, employs a Gaussian-mixture model to efficiently approximate the high-dimensional PPD and introduces a scalar consistency metric, $Δ_{\rm PPD}$, defined as the fraction of the PPD at lower probability density than the observed data. This method avoids challenges related to calibration and numerical stability that impacted PPD methods previously used in DES Year 3. After demonstrating performance with a toy model, we confirm that when applied to DES Year 3 measurements, $Δ_{\rm PPD}$ identifies the same inter-probe tensions as published, calibrated PPD results while requiring only minutes per test. We further validate the method on several noise realizations of simulated DES Y6 analyses, demonstrating that $Δ_{\rm PPD}$ responds coherently to injected inconsistency, while highlighting two effects which can complicate the interpretation of PPD tests for increasingly complex models. First, internal tensions that can be absorbed as shifts in nuisance parameters become more difficult to detect. Second, poorly constrained directions in parameter space can induce projection effects in PPDs for disjoint sets of observables, producing reports of tension even for consistent data. We conclude that $Δ_{\rm PPD}$ provides a practical PPD-based diagnostic for multiprobe unblinding, to be interpreted with care alongside other validation checks. This framework has already played an important role in DES analyses and is well suited for application to forthcoming Stage-IV surveys such as LSST, Euclid, and Roman, where fast, robust internal consistency tests will be essential.
Comments19 pages, 6 figures, 2 tables. Submitted to the Open Journal of Astrophysics. Code publicly available at https://github.com/des-science/y6-ppd-public/
