We present matryoshka, a suite of neural network based emulators and accompanying Python package that have been developed with the goal of producing fast and accurate predictions of the nonlinear galaxy power spectrum. The suite of emulators consists of four linear component emulators, from which fast linear predictions of the power spectrum can be made, allowing all nonlinearities to be included in predictions from a nonlinear boost component emulator. The linear component emulators includes an emulator for the matter transfer function that produces predictions in ∼0.0004 s, with an error of <0.08% (at 1σ level) on scales 10−4 h Mpc−1<k<101 h Mpc−1. In this paper we demonstrate matryoshka by training the nonlinear boost component emulator with analytic training data calculated with HALOFIT, that has been designed to replicate training data that would be generated using numerical simulations. Combining all the component emulator predictions we achieve an accuracy of <0.75% (at 1σ level) when predicting the real space nonlinear galaxy power spectrum on scales 0.0025 h Mpc−1<k<1 h Mpc−1. We use matryoshka to investigate the impact of the analysis setup on cosmological constraints by conducting several full shape analyses of the real space galaxy power spectrum. Specifically we investigate the impact of the minimum scale (or kmax), finding an improvement of ∼1.8× in the constraint on σ8 by pushing kmax from kmax=0.25 h Mpc−1 to kmax=0.85 h Mpc−1, highlighting the potential gains when using clustering emulators such as matryoshka in cosmological analyses.
- large-scale structure of the Universe
- methods: data analysis
- cosmology: cosmological parameters