{"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/sunbird-a-simulation-based-model-for-full","title":"SUNBIRD: A simulation-based model for full-shape density-split clustering","arxiv_id":"2309.16539","date":"2023-09-28","proceeding":null,"authors":["Carolina Cuesta-Lazaro","Enrique Paillas","Sihan Yuan","Yan-Chuan Cai","Seshadri Nadathur","Will J. Percival","Florian Beutler","Arnaud de Mattia","Daniel Eisenstein","Daniel Forero-Sanchez","Nelson Padilla","Mathilde Pinon","Vanina Ruhlmann-Kleider","Ariel G. Sánchez","Georgios Valogiannis","Pauline Zarrouk"],"abstract":"Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-$\\Lambda$CDM framework, incorporating the effects of redshift-space, Alcock-Paczynski distortions and models of the halo-galaxy connection. Our models reach sub-percent level accuracy down to $1\\,h^{-1}{\\rm Mpc}$ and are robust against different choices of galaxy-halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on $\\omega_{\\rm cdm}$, $\\sigma_8$, and $n_s$ by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters. Our code is made publicly available on Github.","url_abs":"https://arxiv.org/abs/2309.16539v2","url_pdf":"https://arxiv.org/pdf/2309.16539v2.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":"sunbird-a-simulation-based-model-for-full","repo_url":"https://github.com/florpi/sunbird","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","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}