{"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/machine-learning-constraints-on-deviations","title":"Machine learning constraints on deviations from general relativity from the large scale structure of the Universe","arxiv_id":"2209.12799","date":"2022-09-26","proceeding":null,"authors":["George Alestas","Lavrentios Kazantzidis","Savvas Nesseris"],"abstract":"We use a particular machine learning approach, called the genetic algorithms (GA), in order to place constraints on deviations from general relativity (GR) via a possible evolution of Newton's constant $\\mu\\equiv G_\\mathrm{eff}/G_\\mathrm{N}$ and of the dark energy anisotropic stress $\\eta$, both defined to be equal to one in GR. Specifically, we use a plethora of background and linear-order perturbations data, such as type Ia supernovae, baryon acoustic oscillations, cosmic chronometers, redshift space distortions and $E_g$ data. We find that although the GA is affected by the lower quality of the currently available data, especially from the $E_g$ data, the reconstruction of Newton's constant is consistent with a constant value within the errors. On the other hand, the anisotropic stress deviates strongly from unity due to the sparsity and the systematics of the $E_g$ data. Finally, we also create synthetic data based on a next-generation survey and forecast the limits of any possible detection of deviations from GR. In particular, we use two fiducial models: one based on the cosmological constant $\\Lambda$CDM model and another on a model with an evolving Newton's constant, dubbed $\\mu$CDM. We find that the GA reconstructions of $\\mu(z)$ and $\\eta(z)$ can be constrained to within a few percent of the fiducial models and in the case of the $\\mu$CDM mocks, they can also provide a strong detection of several $\\sigma$s, thus demonstrating the utility of the GA reconstruction approach.","url_abs":"https://arxiv.org/abs/2209.12799v3","url_pdf":"https://arxiv.org/pdf/2209.12799v3.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":"machine-learning-constraints-on-deviations","repo_url":"https://github.com/snesseris/ga-geff-gr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}