<scp>CosmoPower</scp>: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
Alessio Spurio Mancini, Davide Piras, Justin Alsing, Benjamin Joachimi, M. P. Hobson
Abstract
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 $0.4{{\ \rm per\ cent}}$ in the wavenumber range $k \in [10^{-5}, 10] \, \mathrm{Mpc}^{-1}$ for redshift z ∈ [0, 5]. CosmoPower emulates CMB temperature, polarization, and lensing potential power spectra in the 5-σ region of parameter space around the Planck best-fitting values with an error ${\lesssim}10{{\ \rm per\ cent}}$ 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 polarization 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(104) 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.