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 $k 10^-5, 10 \, Mpc^-1$, for
redshift $z 0, 5$. $CosmoPower$ 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
$20\%$ 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
https://github.com/alessiospuriomancini/cosmopower .
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