{"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/principal-components-for-model-agnostic","title":"Principal Components for Model-Agnostic Modified Gravity with 3x2pt","arxiv_id":"2503.20951","date":"2025-03-26","proceeding":null,"authors":["C. M. A. Zanoletti","C. D. Leonard"],"abstract":"To mitigate the severe information loss arising from widely adopted linear scale cuts in constraints on modified gravity parameterisations with Weak Lensing (WL) and Large-Scale Structure (LSS) data, we introduce a novel alternative method for data reduction. This Principal Component Analysis (PCA)-based framework extracts key features in the matter power spectrum arising from nonlinear effects in a set of representative gravity theories. By performing the analysis in the space of principal components, we can replace sweeping `linear-only' scale cuts with targeted cuts on the transformed data vector, ultimately reducing parameter bias and significantly tightening constraints. We forecast constraints on a minimal parameterised extension to $\\Lambda$CDM which includes modifications to the growth of structure and lensing of light ($\\Lambda$CDM$+\\mu_0+\\Sigma_0$) using mock Stage-IV data for two simulated cosmologies: the $\\Lambda$CDM model and Extended Shift Symmetric (ESS) gravity. Under the assumption of a Universe defined by $\\Lambda$CDM and General Relativity, our method offers constraints on $\\mu_0$ a factor of 1.65 tighter than traditional linear-only scale cuts. Crucially, our approach also provides the necessary constraining power to break key degeneracies in modified gravity without relying on $f\\sigma_8$ measurements, introducing a promising new tool for the analysis of present and future WL and LSS photometric surveys.","url_abs":"https://arxiv.org/abs/2503.20951v1","url_pdf":"https://arxiv.org/pdf/2503.20951v1.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":"principal-components-for-model-agnostic","repo_url":"https://github.com/CarolaZano/MG-PCA-DataReduction","is_official":1,"mentioned_in_paper":0,"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}