{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/it-cosmopower-emulating-cosmological-power","title":"COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys","arxiv_id":"2106.03846","date":"2021-06-07","proceeding":null,"authors":["A. Spurio Mancini","D. Piras","J. Alsing","B. Joachimi","M. P. Hobson"],"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 $\\lesssim 10\\%$ 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 .","url_abs":"https://arxiv.org/abs/2106.03846v2","url_pdf":"https://arxiv.org/pdf/2106.03846v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"it-cosmopower-emulating-cosmological-power","repo_url":"https://github.com/alessiospuriomancini/cosmopower","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"it-cosmopower-emulating-cosmological-power","repo_url":"https://github.com/alexreevesy/planck_compressed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"it-cosmopower-emulating-cosmological-power","repo_url":"https://github.com/karimpsi22/ds-emulators","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"it-cosmopower-emulating-cosmological-power","repo_url":"https://github.com/kids-wl/cosmopowercosmosis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}