{"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/accuracy-requirements-for-empirically","title":"Accuracy Requirements for Empirically-Measured Selection Functions","arxiv_id":"1904.10879","date":"2019-04-24","proceeding":null,"authors":["Will M. Farr"],"abstract":"I give formulas for the accuracy to which a selection function must be measured via Monte-Carlo injections in order to have un-biased population inference. The number of found injections scales linearly with the number of objects in the population; the coefficient in front of the linear term depends on both the distribution of injections and the inferred population distribution.","url_abs":"http://arxiv.org/abs/1904.10879v1","url_pdf":"http://arxiv.org/pdf/1904.10879v1.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":"accuracy-requirements-for-empirically","repo_url":"https://github.com/farr/SelectionAccuracy","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}