{"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/constraining-effective-field-theories-with","title":"Constraining Effective Field Theories with Machine Learning","arxiv_id":"1805.00013","date":"2018-04-30","proceeding":null,"authors":["Johann Brehmer","Kyle Cranmer","Gilles Louppe","Juan Pavez"],"abstract":"We present powerful new analysis techniques to constrain effective field\ntheories at the LHC. By leveraging the structure of particle physics processes,\nwe extract extra information from Monte-Carlo simulations, which can be used to\ntrain neural network models that estimate the likelihood ratio. These methods\nscale well to processes with many observables and theory parameters, do not\nrequire any approximations of the parton shower or detector response, and can\nbe evaluated in microseconds. We show that they allow us to put significantly\nstronger bounds on dimension-six operators than existing methods, demonstrating\ntheir potential to improve the precision of the LHC legacy constraints.","url_abs":"http://arxiv.org/abs/1805.00013v4","url_pdf":"http://arxiv.org/pdf/1805.00013v4.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":"constraining-effective-field-theories-with","repo_url":"https://github.com/johannbrehmer/simulator-mining-example","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.00013","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}