{"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/non-linear-power-spectrum-and-forecasts-for","title":"Non-linear power spectrum and forecasts for Generalized Cubic Covariant Galileon","arxiv_id":"2404.11471","date":"2024-04-17","proceeding":null,"authors":["Luís Atayde","Noemi Frusciante","Benjamin Bose","Santiago Casas","Baojiu Li"],"abstract":"To fully exploit the data from next generation surveys, we need an accurate modelling of the matter power spectrum up to non-linear scales. Therefore in this work we present the halo model reaction framework for the Generalized Cubic Covariant Galileon (GCCG) model, a modified gravity model within the Horndeski class of theories which extends the cubic covariant Galileon (G3) by including power laws of the derivatives of the scalar field in the K-essence and cubic terms. We modify the publicly available software ReACT for the GCCG in order to obtain an accurate prediction of the non-linear power spectrum. In the limit of the G3 model we compare the modified ReACT code to $N$-body simulations and we find agreement within 5\\% for a wide range of scales and redshifts. We then study the relevant effects of the modifications introduced by the GCCG on the non-linear matter power spectrum. Finally, we provide forecasts from spectroscopic and photometric primary probes by next generation surveys using a Fisher matrix method. We show that future data will be able to constrain at 1$\\sigma$ the two additional parameters of the model at the percent level and that considering non-linear corrections to the matter power spectrum beyond the linear regime is crucial to obtain this result.","url_abs":"https://arxiv.org/abs/2404.11471v1","url_pdf":"https://arxiv.org/pdf/2404.11471v1.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":"non-linear-power-spectrum-and-forecasts-for","repo_url":"https://github.com/nebblu/actio-reactio","is_official":1,"mentioned_in_paper":1,"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}