matryoshka II: accelerating effective field theory analyses of the galaxy power spectrum

In this paper, we present an extension to the matryoshka suite of neural-network-based emulators. The new editions have been developed to accelerate effective field theory of large-scale structure (EFTofLSS) analyses of galaxy power spectrum multipoles in redshift space. They are collectively referred to as the EFTEMU. We test the EFTEMU at the power spectrum level and achieve a prediction accuracy of better than 1 per cent with BOSS-like bias parameters and counterterms on scales 0.001 ≤ k ≤ 0.19 hMpc⁻¹. We also run a series of mock full-shape analyses to test the performance of the EFTEMU when carrying out parameter inference. Through these mock analyses, we verify that the 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 5000³ Mpc³ h⁻³). We compare the mock inference results from the 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 EFTEMU is publicly available as part of the matryoshka Python package.