CosmoPower: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys. Issue 2 (12th January 2022)

• Record Type: Journal Article
• Authors: Spurio Mancini, Alessio; Piras, Davide; Alsing, Justin; Joachimi, Benjamin; Hobson, Michael P
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 511:Issue 2(2022)
• Page Start: 1771
• Page End: 1788
• Publication Date: 2022-01-12
• Subjects: methods: data analysis -- methods: statistical -- cosmic background radiation -- large-scale structure of Universe; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stac064
• Languages: English
• ISSNs: 0035-8711

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 (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 .