论文标题
美元
$\texttt{matryoshka}$ II: Accelerating Effective Field Theory Analyses of the Galaxy Power Spectrum
论文作者
论文摘要
在本文中，我们介绍了基于神经网络的仿真器的$ \ texttt {matryoshka} $套件的扩展。已开发新版本是为了加速红移空间中星系功率谱系的EFTOFLSS分析。它们共同称为$ \ texttt {eftemu} $。我们在功率谱级别测试$ \ texttt {eftemu} $，并获得比1 \％的预测准确性，其尺度为$ 0.001 \ h \ h \ h \ mathrm {mpc}^{-1}^{ - 1} \ leq k \ leq 0.19 \ h \ h \ \ h \ \ h \ \ h \ \ h \ \ mathrm {mpc}^{ - 1} $。我们还运行了一系列模拟完整形状分析，以测试执行参数推理时$ \ texttt {eftemu} $的性能。通过这些模拟分析，我们验证了$ \ texttt {eftemu} $在几个红移（$ z = [0.38,0.51,0.61 $）的$1σ$内恢复了真正的宇宙学，并且具有多种噪音水平（其中最严重的是与高斯的共同率相关的$ 5000^3 3 3 \ \ \ \ \ \ \ \ \ \ \ \ m。 h^{ - 3} $）。我们将$ \ texttt {eftemu} $的模拟推理结果与使用完全分析的EFTOFLSS模型获得的模拟推理结果进行了比较，并且再次发现没有明显的偏见，同时通过三个数量级加速了推理。 $ \ texttt {eftemu} $作为$ \ texttt {matryoshka} $ $ $ \ texttt {python} $ package公开可用。
In this paper we present an extension to the $\texttt{matryoshka}$ suite of neural-network-based emulators. The new editions have been developed to accelerate EFTofLSS analyses of galaxy power spectrum multipoles in redshift space. They are collectively referred to as the $\texttt{EFTEMU}$. We test the $\texttt{EFTEMU}$ at the power spectrum level and achieve a prediction accuracy of better than 1\% with BOSS-like bias parameters and counterterms on scales $0.001\ h\ \mathrm{Mpc}^{-1} \leq k \leq 0.19\ h\ \mathrm{Mpc}^{-1}$. We also run a series of mock full shape analyses to test the performance of the $\texttt{EFTEMU}$ when carrying out parameter inference. Through these mock analyses we verify that the $\texttt{EFTEMU}$ recovers the true cosmology within $1σ$ at several redshifts ($z=[0.38,0.51,0.61]$), and with several noise levels (the most stringent of which is Gaussian covariance associated with a volume of $5000^3 \ \mathrm{Mpc}^3 \ h^{-3}$). We compare the mock inference results from the $\texttt{EFTEMU}$ to those obtained with a fully analytic EFTofLSS model and again find no significant bias, whilst speeding up the inference by three orders of magnitude. The $\texttt{EFTEMU}$ is publicly available as part of the $\texttt{matryoshka}$ $\texttt{Python}$ package.
