通过模拟Boltzmann解算器和拉格朗日摄动理论加速大尺度结构数据分析
Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory
摘要：
线性物质功率谱是解释大尺度结构观测量的所有理论模型的基本组成部分。虽然玻尔兹曼代码（如CLASS或CAMB）在计算线性谱方面非常有效，但数据分析通常需要$10^4$ - $10^6$评估，这意味着该任务可能是数据分析中计算成本最高的方面。在这里，我们解决这个问题，通过建立一个神经网络仿真器，提供线性理论（总和冷）的物质功率谱在约1毫秒的0.2%（0.5%）的精度超过红移$z \le 3$（$z \le 9$），和规模$10^{-4} \le k \, [h {\rm Mpc^{-1}}] < 50$。我们训练这个模拟器超过20万次测量，跨越了广泛的宇宙学参数空间，包括大质量中微子和动态暗能量。我们表明，我们的模拟器的参数范围和精度是足够的，以获得无偏的宇宙学约束在分析的欧几里德弱透镜调查。补充这个仿真器，我们训练15个其他仿真器的交叉谱的各种线性领域在欧拉空间，预测的二阶拉格朗日摄动理论，它可以用来加速扰动偏差描述的星系团。我们的仿真器专门设计用于与非线性物质功率谱和重子效应的仿真器结合使用，所有这些都可以在http://www.dipc.org/bacco上公开获得。
英文摘要：
The linear matter power spectrum is an essential ingredient in all theoretical models for interpreting large-scale-structure observables. Although Boltzmann codes such as CLASS or CAMB are very efficient at computing the linear spectrum, the analysis of data usually requires $10^4$-$10^6$ evaluations, which means this task can be the most computationally expensive aspect of data analysis. Here, we address this problem by building a neural network emulator that provides the linear theory (total and cold) matter power spectrum in about one millisecond with 0.2% (0.5%) accuracy over redshifts $z \le 3$ ($z \le 9$), and scales $10^{-4} \le k \, [h {\rm Mpc^{-1}}] < 50$. We train this emulator with more than 200,000 measurements, spanning a broad cosmological parameter space that includes massive neutrinos and dynamical dark energy. We show that the parameter range and accuracy of our emulator is enough to get unbiased cosmological constraints in the analysis of a Euclid-like weak lensing survey. Complementing this emulator, we train 15 other emulators for the cross-spectra of various linear fields in Eulerian space, as predicted by 2nd-order Lagrangian Perturbation theory, which can be used to accelerate perturbative bias descriptions of galaxy clustering. Our emulators are specially designed to be used in combination with emulators for the nonlinear matter power spectrum and for baryonic effects, all of which are publicly available at http://www.dipc.org/bacco.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
