发表机构
Waterloo Centre for Astrophysics, University of Waterloo; Department of Physics and Astronomy, University of Waterloo; Perimeter Institute for Theoretical Physics; Dipartimento di Fisica, Università degli Studi di Torino; INFN – Istituto Nazionale di Fisica Nucleare, Sezione di Torino; INAF – Istituto Nazionale di Astrofisica, Osservatorio Astrofisico di Torino; Department of Physics & Astronomy, University of the Western Cape; Université Paris-Saclay; CIEMAT; California Institute of Technology（滑铁卢大学天体物理中心; 滑铁卢大学物理与天文系; 理论物理前沿研究所; 都灵大学物理系; 意大利国家核物理研究所都灵分部; 意大利国家天体物理研究所都灵天文台; 西开普大学物理与天文系; 巴黎萨克雷大学; 西班牙能源、环境和工业技术研究中央实验室; 加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究扩展了数值工具包Blast.jl，开发出纳入多种物理效应的可微非Limber功率谱算法，经验证可用于大尺度结构巡天的联合分析，能从模拟数据中恢复无偏宇宙学参数约束。
AI 中文摘要
即将开展的大尺度结构巡天需要对最大角尺度上的角功率谱进行精确理论预测，在该尺度上常用的Limber近似失效，且多种相对论效应和观测效应变得相关。与此同时，现代宇宙学分析越来越依赖基于梯度的推断技术，这推动了快速、完全可微算法的开发。本研究中，我们对Blast.jl（用于高效计算非Limber角功率谱的数值工具包）进行了重大扩展。在原始框架的切比雪夫多项式分解基础上，更新后的算法纳入了红移空间畸变、放大偏倚、原初非高斯性、内禀alignments、CMB透镜和积分萨克斯-沃尔夫效应，同时通过自定义自动微分规则保持完全可微性。尽管物理复杂度增加，该算法通过预计算所有与宇宙学无关的量仍保持良好的缩放性。我们通过暴力积分和成熟的宇宙学代码验证了扩展后的框架，并对算法超参数相关的计算速度与精度之间的权衡进行了详细分析。我们通过类似LSST Y10模拟数据的模拟似然分析，证明了Blast.jl适用于基于梯度的推断，使用基于梯度的采样器从35参数模型中恢复了无偏的宇宙学和扰动参数约束。
英文摘要
Upcoming large-scale structure surveys require accurate theoretical predictions for angular power spectra on the largest angular scales, where the commonly used Limber approximation breaks down and multiple relativistic and observational effects become relevant. At the same time, modern cosmological analyses increasingly rely on gradient-based inference techniques, motivating the development of fast, fully differentiable algorithms. In this work, we present a major extension of Blast.jl, a numerical toolkit for the efficient computation of non-Limber angular power spectra. Building on the Chebyshev-polynomial decomposition of the original framework, the updated algorithm incorporates redshift-space distortions, magnification bias, primordial non-Gaussianity, intrinsic alignments, CMB lensing, and the integrated Sachs-Wolfe effect, while remaining fully differentiable through custom automatic differentiation rules. Despite the increased physical complexity, the algorithm retains favorable scaling by precomputing all cosmology-independent quantities. We validate the expanded framework against brute-force integration and established cosmological codes, and provide a detailed analysis of the trade-off between computational speed and precision as a function of the algorithm hyperparameters. We demonstrate the readiness of Blast.jl for gradient-based inference through a simulated likelihood analysis with LSST Y10-like mock data, recovering unbiased cosmological and nuisance parameter constraints from a 35-parameter model using gradient-based samplers.
Comments18 pages, 11 figures. Comments welcome
