$it{CosmoPower} ,$: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys. (arXiv:2106.03846v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Mancini_A/0/1/0/all/0/1">Alessio Spurio Mancini</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Piras_D/0/1/0/all/0/1">Davide Piras</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Alsing_J/0/1/0/all/0/1">Justin Alsing</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Joachimi_B/0/1/0/all/0/1">Benjamin Joachimi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hobson_M/0/1/0/all/0/1">Michael P. Hobson</a>
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 $5sigma$ region of
parameter space around the $it{Planck}$ best fit values with an error
$lesssim 20%$ 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
https://github.com/alessiospuriomancini/cosmopower .
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 $5sigma$ region of
parameter space around the $it{Planck}$ best fit values with an error
$lesssim 20%$ 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
https://github.com/alessiospuriomancini/cosmopower .
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