{"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/on-the-road-to-percent-accuracy-ii","title":"On the road to percent accuracy II: calibration of the non-linear matter power spectrum for arbitrary cosmologies","arxiv_id":"1906.02742","date":"2019-06-06","proceeding":null,"authors":["Benjamin Giblin","Matteo Cataneo","Ben Moews","Catherine Heymans"],"abstract":"We introduce an emulator approach to predict the non-linear matter power spectrum for broad classes of beyond-$\\Lambda$CDM cosmologies, using only a suite of $\\Lambda$CDM $N$-body simulations. By including a range of suitably modified initial conditions in the simulations, and rescaling the resulting emulator predictions with analytical `halo model reactions', accurate non-linear matter power spectra for general extensions to the standard $\\Lambda$CDM model can be calculated. We optimise the emulator design by substituting the simulation suite with non-linear predictions from the standard {\\sc halofit} tool. We review the performance of the emulator for artificially generated departures from the standard cosmology as well as for theoretically motivated models, such as $f (R)$ gravity and massive neutrinos. For the majority of cosmologies we have tested, the emulator can reproduce the matter power spectrum with errors $\\lesssim 1\\%$ deep into the highly non-linear regime. This work demonstrates that with a well-designed suite of $\\Lambda$CDM simulations, extensions to the standard cosmological model can be tested in the non-linear regime without any reliance on expensive beyond-$\\Lambda$CDM simulations.","url_abs":"http://arxiv.org/abs/1906.02742v2","url_pdf":"http://arxiv.org/pdf/1906.02742v2.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":"on-the-road-to-percent-accuracy-ii","repo_url":"https://github.com/moews/smurves","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}