{"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/jet-constituents-for-deep-neural-network","title":"Jet Constituents for Deep Neural Network Based Top Quark Tagging","arxiv_id":"1704.02124","date":"2017-04-07","proceeding":null,"authors":["Jannicke Pearkes","Wojciech Fedorko","Alison Lister","Colin Gay"],"abstract":"Recent literature on deep neural networks for tagging of highly energetic\njets resulting from top quark decays has focused on image based techniques or\nmultivariate approaches using high-level jet substructure variables. Here, a\nsequential approach to this task is taken by using an ordered sequence of jet\nconstituents as training inputs. Unlike the majority of previous approaches,\nthis strategy does not result in a loss of information during pixelisation or\nthe calculation of high level features. The jet classification method achieves\na background rejection of 45 at a 50% efficiency operating point for\nreconstruction level jets with transverse momentum range of 600 to 2500 GeV and\nis insensitive to multiple proton-proton interactions at the levels expected\nthroughout Run 2 of the LHC.","url_abs":"http://arxiv.org/abs/1704.02124v2","url_pdf":"http://arxiv.org/pdf/1704.02124v2.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":"jet-constituents-for-deep-neural-network","repo_url":"https://github.com/jpearkes/topo_dnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.02124","atlas_url":"https://app.syntology.ai/?focus=1704.02124","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}