{"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/reduced-order-surrogate-models-for-scalar","title":"Reduced-order surrogate models for scalar-tensor gravity in the strong field and applications to binary pulsars and GW170817","arxiv_id":"1907.00780","date":"2019-07-01","proceeding":null,"authors":["Junjie Zhao","Lijing Shao","Zhoujian Cao","Bo-Qiang Ma"],"abstract":"We investigate the scalar-tensor gravity of Damour and Esposito-Far\\`ese (DEF), which predicts non-trivial phenomena in the nonperturbative strong-field regime for neutron stars (NSs). Instead of solving the modified Tolman-Oppenheimer-Volkoff equations, we construct reduced-order surrogate models, coded in the pySTGROM package, to predict the relations of a NS radius, mass, and effective scalar coupling to its central density. Our models are accurate at $\\sim1\\%$ level and speed up large-scale calculations by two orders of magnitude. As an application, we use pySTGROM and Markov-chain Monte Carlo techniques to constrain parameters in the DEF theory, with five well-timed binary pulsars, the binary NS (BNS) inspiral GW170817, and a hypothetical BNS inspiral in the Advanced LIGO and next-generation GW detectors. In the future, as more binary pulsars and BNS mergers are detected, our surrogate models will be helpful in constraining strong-field gravity with essential speed and accuracy.","url_abs":"https://arxiv.org/abs/1907.00780v1","url_pdf":"https://arxiv.org/pdf/1907.00780v1.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":"reduced-order-surrogate-models-for-scalar","repo_url":"https://github.com/BenjaminDbb/pySTGROM","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}