COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from nextgeneration surveys
Abstract
We present COSMOPOWER, a suite of neural cosmological power spectrum emulators providing ordersofmagnitude acceleration for parameter estimation from twopoint statistics analyses of LargeScale 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 retrained 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 5σ region of parameter space around the Planck bestfitting 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 KiloDegree Survey, as well as on a Stage IV Euclidlike 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 speedup 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.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 April 2022
 DOI:
 10.1093/mnras/stac064
 arXiv:
 arXiv:2106.03846
 Bibcode:
 2022MNRAS.511.1771S
 Keywords:

 methods: data analysis;
 methods: statistical;
 cosmic background radiation;
 largescale structure of Universe;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 13+6 pages, 6+3 figures. Matches MNRAS published version. COSMOPOWER available at https://github.com/alessiospuriomancini/cosmopower