AI 中文总结
本研究针对全局21厘米宇宙学的波束不确定性问题，提出端到端贝叶斯框架，通过代理建模与分析边缘化减少模拟负担，实现稳健的不确定性量化。
AI 中文摘要
全局21厘米宇宙学中的稳健统计推断需要端到端的不确定性量化，以联合处理高度简并的宇宙学信号、前景发射以及仪器响应。尽管电磁模拟以参数化方式捕获天线的物理特性，但数小时的运行时间使其无法集成到基于似然的采样框架中。因此，大多数现有方法假设使用单一预计算波束，考虑到我们的演示显示现实的不匹配会严重偏差恢复的宇宙学和前景参数，这一假设十分脆弱。为解决该问题，我们提出一种加速且可微分的贝叶斯框架，其将色散波束不确定性的知情代理表示直接纳入正演建模管道。将天线的物理特性视为冗余量，我们直接对模拟的方向性图应用两阶段分解，在保留准确波束重建所需的角结构和光谱结构的同时，将仪器参数化减少两个数量级。利用所得代理的线性性，我们采用分析边缘化，使连续的仪器不确定性能够传播到最终后验和贝叶斯证据中，而无需直接对波束空间进行采样。针对一组未见的波束和宇宙学信号测试该框架，我们在约仪器噪声水平下恢复了真实输入。我们进一步表明，对于此处考虑的不确定性，仅需100次电磁模拟即可构建有效代理，大幅降低未来分析的模拟负担。该框架为全局21厘米宇宙学中的硬件加速不确定性量化提供了可扩展、统计严谨的途径。
英文摘要
Robust statistical inference in global 21-cm cosmology requires end-to-end uncertainty quantification that jointly handles the highly degenerate cosmological signal, foreground emission, and instrumental response. Although electromagnetic simulations capture physical antenna properties in a parametrised way, multi-hour runtimes make their integration within likelihood-based sampling frameworks infeasible. Most existing approaches therefore assume a single precomputed beam, a fragile assumption given our demonstration that realistic mismatches can severely bias the recovered cosmological and foreground parameters. To address this, we present an accelerated and differentiable Bayesian framework that incorporates an informed surrogate representation of chromatic beam uncertainty directly into a forward-modelling pipeline. Treating the physical antenna properties as nuisance quantities, we apply a two-stage decomposition directly to simulated directivity patterns, reducing the instrumental parameterisation by two orders of magnitude while retaining the angular and spectral structure required for accurate beam reconstruction. Exploiting the linearity of the resulting surrogate, we use analytical marginalisation to allow the continuous instrumental uncertainty to be propagated into the final posteriors and Bayesian evidence without directly sampling the beam space. Testing the framework against a suite of unseen beams and cosmological signals, we recover the true inputs at approximately the instrumental-noise level. We further show that, for the uncertainty considered here, as few as 100 electromagnetic simulations are sufficient to construct an effective surrogate, substantially reducing the simulation burden for future analyses. This framework provides a scalable, statistically rigorous route towards hardware-accelerated uncertainty quantification in global 21-cm cosmology.
Comments19 pages, 15 figures, 4 tables
