CosmoPower : emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
| Autor: | Alessio Spurio Mancini, Davide Piras, Justin Alsing, Benjamin Joachimi, Michael P Hobson |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
Space and Planetary Science Astrophysics::Instrumentation and Methods for Astrophysics FOS: Physical sciences Astronomy and Astrophysics Astrophysics::Cosmology and Extragalactic Astrophysics Astrophysics - Instrumentation and Methods for Astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) Astrophysics - Cosmology and Nongalactic Astrophysics |
| Zdroj: | Monthly Notices of the Royal Astronomical Society. 511:1771-1788 |
| ISSN: | 1365-2966 0035-8711 |
| DOI: | 10.1093/mnras/stac064 |
| Popis: | 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 . Comment: 13+6 pages, 6+3 figures. Matches MNRAS published version. COSMOPOWER available at https://github.com/alessiospuriomancini/cosmopower |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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