The power spectrum is the most commonly applied summary statistics to extract cosmological information from the observed three-dimensional distribution of galaxies in spectroscopic surveys. We present CLASS-OneLoop, a new numerical tool, fully integrated into the Boltzmann code CLASS, enabling the calculation of the one-loop power spectrum of biased tracers in spectroscopic surveys. Built upon the Eulerian moment expansion framework for redshift-space distortions, the implemented model incorporates a complete set of nonlinear biases, counterterms, and stochastic contributions, and includes the infrared resummation and the Alcock-Paczynski effect. The code features an evaluation of the loops by either direct numerical integration or Fast Fourier Transform, and employs a fast-slow parameter decomposition, which is essential for accelerating MCMC runs. After presenting performance and validation tests, as an illustration of the capabilities of the code, we apply it to fit the measured redshift-space halo power spectrum wedges on a ΛCDM subset of the AbacusSummit simulation suite and considering scales up to k_max = 0.3 h/Mpc. We find that the one-loop model adeptly recovers the fiducial cosmology of the simulation, while a simplified model commonly used in the literature for sensitivity forecasts yields significantly biased results. Furthermore, we conduct Monte Carlo Markov Chain (MCMC) forecasts for a DESI-like survey, considering a model with a dynamical dark energy component. Our results demonstrate the ability to independently constrain cosmological and nuisance parameters, even in the presence of a large parameter space with twenty-nine variables.

中文翻译：

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CLASS-OneLoop：来自光谱星系调查的准确且公正的推断


功率谱是最常用的汇总统计数据，用于从光谱巡天中观测到的星系三维分布中提取宇宙学信息。我们提出CLASS-OneLoop ，一种新的数值工具，完全集成到玻尔兹曼代码中班级，能够计算光谱测量中偏置示踪剂的单环功率谱。所实现的模型基于红移空间畸变的欧拉矩展开框架，包含一整套非线性偏差、反项和随机贡献，并包括红外恢复和 Alcock-Paczynski 效应。该代码的特点是通过直接数值积分或快速傅立叶变换对循环进行评估，并采用快慢参数分解，这对于加速 MCMC 运行至关重要。在提供性能和验证测试之后，作为代码功能的说明，我们将其应用于在 ΛCDM 子集上拟合测量的红移空间光晕功率谱楔形。算盘峰会模拟套件并考虑规模达到 k _max = 0.3 H /Mpc。我们发现单环模型巧妙地恢复了模拟的基准宇宙学，而文献中常用的灵敏度预测简化模型会产生明显偏差的结果。此外，我们考虑具有动态暗能量成分的模型，对类似 DESI 的调查进行蒙特卡洛马尔可夫链 (MCMC) 预测。我们的结果证明了独立约束宇宙学和干扰参数的能力，即使存在具有二十九个变量的大参数空间。