http://arxiv.org/abs/2202.07557
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 $\texttt{EFTEMU}$ at the inference level. Through these mock analyses we verify that the $\texttt{EFTEMU}$ recovers the true cosmology within $1\sigma$ at several redshifts ($z=[0.38,0.51,0.61]$), and with several noise levels (the most stringent of which being a Gaussian covariance associated with a volume of $5000^3 \ \mathrm{Mpc}^3 \ h^{-3}$). We compare 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 https://github.com/JDonaldM/Matryoshka.
J. Donald-McCann, K. Koyama and F. Beutler
Wed, 16 Feb 22
8/69
Comments: 8 pages, 6 figures, 2 tables. Code available at this https URL