{"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/fast-and-accurate-sensitivity-estimation-for","title":"Fast and Accurate Sensitivity Estimation for Continuous-Gravitational-Wave Searches","arxiv_id":"1808.02459","date":"2018-08-07","proceeding":null,"authors":["Christoph Dreissigacker","Reinhard Prix","Karl Wette"],"abstract":"This paper presents an efficient numerical sensitivity-estimation method and implementation for continuous-gravitational-wave searches, extending and generalizing an earlier analytic approach by Wette [1]. This estimation framework applies to a broad class of F-statistic-based search meth- ods, namely (i) semi-coherent StackSlide F-statistic (single-stage and hierarchical multi-stage), (ii) Hough number count on F-statistics, as well as (iii) Bayesian upper limits on (coherent or semi-coherent) F-statistic search results. We test this estimate against results from Monte-Carlo simulations assuming Gaussian noise. We find the agreement to be within a few % at high (i.e. low false-alarm) detection thresholds, with increasing deviations at decreasing (i.e. higher false- alarm) detection thresholds, which can be understood in terms of the approximations used in the estimate. We also provide an extensive summary of sensitivity depths achieved in past continuous- gravitational-wave searches (derived from the published upper limits). For the F-statistic-based searches where our sensitivity estimate is applicable, we find an average relative deviation to the published upper limits of less than 10%, which in most cases includes systematic uncertainty about the noise-floor estimate used in the published upper limits.","url_abs":"https://arxiv.org/abs/1808.02459v1","url_pdf":"https://arxiv.org/pdf/1808.02459v1.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":"fast-and-accurate-sensitivity-estimation-for","repo_url":"https://github.com/rodrigo-tenorio/continuous-wave-search-sensitivity-simulator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}