COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys

Abstract

We present $CosmoPower$, a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics analyses of Large-Scale Structure (LSS) and Cosmic Microwave Background (CMB) surveys. The emulators replace the computation of matter and CMB power spectra from Boltzmann codes; thus, they do not need to be re-trained for different choices of astrophysical nuisance parameters or redshift distributions. The matter power spectrum emulation error is less than $0.4\%$ in the wavenumber range $k\in [10^{-5},10]{\mathrm{Mpc}}^{-1}$, for redshift $z\in [0,5]$. $CosmoPower$ emulates CMB temperature, polarisation and lensing potential power spectra in the $5\sigma$ region of parameter space around the $Planck$ best fit values with an error $\lesssim 10\%$ of the expected shot noise for the forthcoming Simons Observatory. $CosmoPower$ is showcased on a joint cosmic shear and galaxy clustering analysis from the Kilo-Degree Survey, as well as on a Stage IV $Euclid$-like simulated cosmic shear analysis. For the CMB case, $CosmoPower$ is tested on a $Planck$ 2018 CMB temperature and polarisation analysis. The emulators always recover the fiducial cosmological constraints with differences in the posteriors smaller than sampling noise, while providing a speed-up factor up to $O(10^4)$ to the complete inference pipeline. This acceleration allows posterior distributions to be recovered in just a few seconds, as we demonstrate in the $Planck$ likelihood case. $CosmoPower$ is written entirely in Python, can be interfaced with all commonly used cosmological samplers and is publicly available at https://github.com/alessiospuriomancini/cosmopower .

13+6 pages, 6+3 figures. Matches MNRAS published version. COSMOPOWER available at https://github.com/alessiospuriomancini/cosmopower

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