发表机构
Heidelberg University; The Center for Astrophysics and Space Science (CASS), NYUAD Research Institute, New York University Abu Dhabi; Universidade Estadual de Londrina; Centro Brasileiro de Pesquisas Físicas; Universidade Federal do Rio de Janeiro; Universidade Federal do Espírito Santo（海德堡大学; 纽约大学阿布扎比分校研究学院天体物理与空间科学中心; 朗德里纳州立大学; 巴西物理研究中心; 里约热内卢联邦大学; 圣埃斯皮里图联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍 neckar，一个基于 FFTLog 的快速精确宇宙学圈图计算代码，用于 EFTofLSS 功率谱和相关函数，通过多项数值与解析改进，显著加速高维贝叶斯参数推断。
AI 中文摘要
我们介绍 neckar，一个公开代码，用于计算欧拉有效场论（EFTofLSS）中的单圈物质功率谱、星系功率谱以及相关函数。它基于 FFTLog 展开，旨在从大尺度结构数据中进行快速而精确的参数推断，使其成为当前和未来星系巡天高维贝叶斯分析的强大工具。neckar 结合了多项数值和解析改进，包括可预计算部分与宇宙学依赖部分的分离、对功率谱多极的 Alcock-Paczynski 效应的半解析处理，以及基于对称性的 bootstrap 核的实现，从而能够直接应用于更广泛的宇宙学模型类别。该代码还引入了一种改进的红外重求和方案，修正了先前实现中的不精确性，该不精确性在尺度 $k \gtrsim 0.1 h \textrm{Mpc}^{-1}$ 上导致四极误差约为 1%，十六极误差约为 10%；此外，还引入了一种渐近处理，将精确预测的范围扩展到弱非线性尺度 $k \sim 0.4 h \textrm{Mpc}^{-1}$。这导致相关函数在 BAO 峰附近产生相关修正。综合来看，这些改进使 neckar 比现有类似代码快达两个数量级，且无需依赖模拟器，同时提高了数值精度。该代码用 C 语言编写，并附带 Python 封装以便使用。
英文摘要
We introduce neckar, a public code for the one-loop matter and galaxy power spectrum, as well as correlation function, in the Eulerian Effective Field Theory of Large-Scale Structure (EFTofLSS). It is based on the FFTLog expansion, and is designed for fast and accurate parameter inference from large-scale structure data, making it a powerful tool for high-dimensional Bayesian analyses of current and upcoming galaxy surveys. neckar combines several numerical and analytical improvements, including the separation between pre-computable and cosmology-dependent contributions, a semi-analytic treatment of the Alcock-Paczynski effect for power spectrum multipoles, and an implementation of the symmetry-based bootstrap kernels, allowing straightforward applications to a broader class of cosmological models. The code also incorporates an improved infrared resummation scheme that corrects previous implementation inaccuracies, and which led to errors of the order of 1% for the quadrupole, and of 10% for the hexadecapole at scales $k \gtrsim 0.1 h \textrm{Mpc}^{-1}$, as well as an asymptotic treatment that extends the range of accurate predictions into mildly non-linear scales $k \sim 0.4 h \textrm{Mpc}^{-1}$. This leads to relevant corrections in the correlation function around the BAO peak. Taken altogether, these improvements render neckar up to two orders of magnitude faster than similar existing codes without relying on emulators, and at the same time improving numerical accuracy. The code is written in C and is distributed with a Python wrapper for convenience.
Comments25 pages, 6 figures, 4 appendices
