COSMOPOWER: emulating cosmological power spectra for accelerated
  Bayesian inference from next-generation surveys
  We present $\it{CosmoPower}$, 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 $z \in [0, 5]$. $\it{CosmoPower}$ emulates CMB temperature,
polarisation and lensing potential power spectra in the $5\sigma$ region of
parameter space around the $\it{Planck}$ best fit values with an error
$\lesssim 10\%$ of the expected shot noise for the forthcoming Simons
Observatory. $\it{CosmoPower}$ is showcased on a joint cosmic shear and galaxy
clustering analysis from the Kilo-Degree Survey, as well as on a Stage IV
$\it{Euclid}$-like simulated cosmic shear analysis. For the CMB case,
$\it{CosmoPower}$ is tested on a $\it{Planck}$ 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 $\it{Planck}$ likelihood case.
$\it{CosmoPower}$ is written entirely in Python, can be interfaced with all
commonly used cosmological samplers and is publicly available at
https://github.com/alessiospuriomancini/cosmopower .
