{"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/deepefficiency-optimal-efficiency-inversion","title":"DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHC","arxiv_id":"1809.06101","date":"2018-09-17","proceeding":null,"authors":["Mikael Mieskolainen"],"abstract":"We introduce a new high dimensional algorithm for efficiency corrected,\nmaximally Monte Carlo event generator independent fiducial measurements at the\nLHC and beyond. The approach is driven probabilistically using a Deep Neural\nNetwork on an event-by-event basis, trained using detector simulation and even\nonly pure phase space distributed events. This approach gives also a glimpse\ninto the future of high energy physics, where experiments publish new type of\nmeasurements in a radically multidimensional way.","url_abs":"http://arxiv.org/abs/1809.06101v1","url_pdf":"http://arxiv.org/pdf/1809.06101v1.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":"abstracts"},"code_links":[{"paper_slug":"deepefficiency-optimal-efficiency-inversion","repo_url":"https://github.com/mieskolainen/DeepEfficiency","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","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}