%0 Generic %1 mancini2021itcosmopower %A Mancini, Alessio Spurio %A Piras, Davide %A Alsing, Justin %A Joachimi, Benjamin %A Hobson, Michael P. %D 2021 %K library %T $CosmoPower \,$: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys %U http://arxiv.org/abs/2106.03846 %X 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 $0.4\%$ in the wavenumber range $k 10^-5, 10 \, Mpc^-1$, for redshift $z 0, 5$. $CosmoPower$ emulates CMB temperature, polarisation and lensing potential power spectra in the $5\sigma$ region of parameter space around the $Planck$ best fit values with an error $20\%$ 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 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 $Planck$ likelihood case. $CosmoPower$ is written entirely in Python, can be interfaced with all commonly used cosmological samplers and is publicly available https://github.com/alessiospuriomancini/cosmopower .

@misc{mancini2021itcosmopower, abstract = {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 $\lesssim 20\%$ 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 .}, added-at = {2021-06-08T06:50:35.000+0200}, author = {Mancini, Alessio Spurio and Piras, Davide and Alsing, Justin and Joachimi, Benjamin and Hobson, Michael P.}, biburl = {https://www.bibsonomy.org/bibtex/25d7cff33d1d79ba6f3b70c1ac2769175/gpkulkarni}, description = {$\it{CosmoPower} \,$: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys}, interhash = {573436a002786946bb1a5bc38b565746}, intrahash = {5d7cff33d1d79ba6f3b70c1ac2769175}, keywords = {library}, note = {cite arxiv:2106.03846Comment: 13+6 pages, 6+3 figures}, timestamp = {2021-06-08T06:50:35.000+0200}, title = {$\it{CosmoPower} \,$: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys}, url = {http://arxiv.org/abs/2106.03846}, year = 2021 }