AI 中文总结
研究针对宇宙偶极张力超预期挑战，提出基于模拟的推理架构，用神经比率估计器测量张力，经验证可准确恢复真实值，应用于多数据集测得了不同张力，能在新观测时代实现稳健张力量化。
AI 中文摘要
在宇宙学远距离源的调查中测得的宇宙偶极持续超过从宇宙微波背景得出的预期，这对标准的 ΛCDM 宇宙学构成重大挑战。在精确宇宙学时代，量化稳健的张力受我们对复杂非线性效应建模能力的限制，这些效应常导致难以处理的似然函数。本文提出一种灵活的基于模拟的推理（SBI）架构，使用神经比率估计器（NREs）来测量宇宙偶极张力。设计并训练一组 NREs 来评估对数贝叶斯证据比率并测量 Nσ 张力。通过与嵌套采样对比验证了该方法，证明其能准确恢复真实值。在偶极的运动学解释下，将该方法应用于普朗克、NRAO VLA 天空调查（NVSS）、快速 ASKAP 连续体调查（RACS）和广域红外巡天探测器目录（CatWISE）。利用 SBI 测量出 CatWISE 和普朗克之间约 5.7σ 的张力。将其应用于 CatWISE 埃丁顿偏差的正向建模模拟，揭示出约 6.7σ 的张力。随着进入 LSST、欧几里得和 SKA 时代，这里提出的方法能够实现稳健的张力量化。
英文摘要
The cosmic dipole measured in surveys of cosmologically distant sources consistently exceeds the expectation derived from the cosmic microwave background, posing a significant challenge to the standard $Λ$CDM cosmology. In the era of precision cosmology, quantifying robust tensions is constrained by our ability to model complex, non-linear effects that often result in intractable likelihood functions. In this paper, we present a flexible simulation-based inference (SBI) architecture for measuring the cosmic dipole tension using Neural Ratio Estimators (NREs). We design and train an ensemble of NREs to evaluate the log Bayesian evidence ratio and measure $Nσ$ tension. We validate our approach against nested sampling, demonstrating that it accurately recovers the ground-truth. Under the kinematic interpretation of the dipole, we apply our approach to Planck, the NRAO VLA Sky Survey (NVSS), the Rapid ASKAP Continuum Survey (RACS), and the Wide-field Infrared Survey Explorer catalogue (CatWISE). Here, leveraging SBI, we measure a $\approx5.7σ$ tension between CatWISE and Planck. We demonstrate the extensibility of our approach by applying it to forward-modelled simulations of the CatWISE Eddington bias, revealing a $\approx6.7σ$ tension. The methodology proposed here enables robust tension quantification as we enter the era of LSST, Euclid, and the SKA.
Comments14 pages, 9 figures, accepted for publication in MNRAS
Journal refMon Not R Astron Soc (2026)
