Matryoshka: halo model emulator for the galaxy power spectrum

Abstract

ABSTRACT 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 non-linear 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 non-linearities to be included in predictions from a non-linear boost component emulator. The linear component emulators include an emulator for the matter transfer function that produces predictions in ∼0.0004 s, with an error of ${\lt} 0.08{{\ \rm per\ cent}}$ (at 1σ level) on scales 10−4 < k < 101 h Mpc−1. In this paper, we demonstrate matryoshka by training the non-linear boost component emulator with analytic training data calculated with Halofit, which 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 ${\lt} 0.75{{\ \rm per\ cent}}$ (at 1σ level) when predicting the real space non-linear galaxy power spectrum on scales 0.0025 < k < 1 h Mpc−1. We use matryoshka to investigate the impact of the analysis set-up 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 0.25 to 0.85 h Mpc−1, highlighting the potential gains when using clustering emulators such as matryoshka in cosmological analyses.