{"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/machine-learning-methods-for-track","title":"Machine Learning Methods for Track Classification in the AT-TPC","arxiv_id":"1810.10350","date":"2018-10-21","proceeding":null,"authors":["Michelle P. Kuchera","Raghuram Ramanujan","Jack Z. Taylor","Ryan R. Strauss","Daniel Bazin","Joshua Bradt","Ruiming Chen"],"abstract":"We evaluate machine learning methods for event classification in the\nActive-Target Time Projection Chamber detector at the National Superconducting\nCyclotron Laboratory (NSCL) at Michigan State University. An automated method\nto single out the desired reaction product would result in more accurate\nphysics results as well as a faster analysis process. Binary and multi-class\nclassification methods were tested on data produced by the $^{46}$Ar(p,p)\nexperiment run at the NSCL in September 2015. We found a Convolutional Neural\nNetwork to be the most successful classifier of proton scattering events for\ntransfer learning. Results from this investigation and recommendations for\nevent classification in future experiments are presented.","url_abs":"http://arxiv.org/abs/1810.10350v3","url_pdf":"http://arxiv.org/pdf/1810.10350v3.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":"machine-learning-methods-for-track","repo_url":"https://github.com/ATTPC/event-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"machine-learning-methods-for-track","repo_url":"https://github.com/ATTPC/attpc-event-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1810.10350","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}