{"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/data-driven-extraction-of-the-substructure-of-1","title":"Data-driven extraction of the substructure of quark and gluon jets in proton-proton and heavy-ion collisions","arxiv_id":"2211.15785","date":"2022-11-28","proceeding":null,"authors":["Yueyang Ying"],"abstract":"The modification of quark- and gluon-initiated jets in the quark-gluon plasma produced in heavy-ion collisions is a long-standing question that has not yet received a definitive answer from experiments. In particular, the size of the modifications in the quark-gluon plasma differs between theoretical models. Therefore a fully data-driven technique is crucial for an unbiased extraction of the quark and gluon jet spectra and substructure. Corroborating past results, I demonstrate the capability of a fully data-driven technique called topic modeling in separating quark and gluon contributions to jet observables. The data-driven topic separation results can further be used to extract jet substructures, such as jet shapes and jet fragmentation function, and their respective QGP modifications. In addition, I propose the use of machine learning constructed observables and demonstrate the potential to increase separability for the input observable. This proof-of-concept study is based on proton-proton and heavy-ion collision events from the PYQUEN generator with statistics accessible in Run 4 of the Large Hadron Collider. These results suggest the potential for an experimental determination of quark- and gluon-jet spectra, their substructures, and their modification in the QGP.","url_abs":"https://arxiv.org/abs/2211.15785v1","url_pdf":"https://arxiv.org/pdf/2211.15785v1.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":"data-driven-extraction-of-the-substructure-of-1","repo_url":"https://github.com/kying18/jet-topics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}