$\text{matryoshka}$ II: Accelerating Effective Field Theory Analyses of the Galaxy Power Spectrum

Abstract

In this paper we present an extension to the $\text{matryoshka}$ 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 $\text{EFTEMU}$. We test the $\text{EFTEMU}$ at the power spectrum level and achieve a prediction accuracy of better than 1\% with BOSS-like bias parameters and counterterms on scales $0.001h{\mathrm{Mpc}}^{-1}\le k\le 0.19h{\mathrm{Mpc}}^{-1}$. We also run a series of mock full shape analyses to test the performance of the $\text{EFTEMU}$ when carrying out parameter inference. Through these mock analyses we verify that the $\text{EFTEMU}$ 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 is Gaussian covariance associated with a volume of $5000^3{\mathrm{Mpc}}^3{h}^{-3}$). We compare the mock inference results from the $\text{EFTEMU}$ 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 $\text{EFTEMU}$ is publicly available as part of the $\text{matryoshka}$ $\text{Python}$ package.

arXiv commentMNRAS accepted version. 11 pages, 8 figures, 3 tables. Code available at https://github.com/JDonaldM/Matryoshka

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