{"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/automatic-trajectory-recognition-in-active","title":"Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clustering","arxiv_id":"1807.03513","date":"2018-07-10","proceeding":null,"authors":["Christoph Dalitz","Yassid Ayyad","Jens Wilberg","Lukas Aymans","Daniel Bazin","Wolfgang Mittig"],"abstract":"The automatic reconstruction of three-dimensional particle tracks from Active\nTarget Time Projection Chambers data can be a challenging task, especially in\nthe presence of noise. In this article, we propose a non-parametric algorithm\nthat is based on the idea of clustering point triplets instead of the original\npoints. We define an appropriate distance measure on point triplets and then\napply a single-link hierarchical clustering on the triplets. Compared to\nparametric approaches like RANSAC or the Hough transform, the new algorithm has\nthe advantage of potentially finding trajectories even of shapes that are not\nknown beforehand. This feature is particularly important in low-energy nuclear\nphysics experiments with Active Targets operating inside a magnetic field. The\nalgorithm has been validated using data from experiments performed with the\nActive Target Time Projection Chamber developed at the National Superconducting\nCyclotron Laboratory (NSCL).The results demonstrate the capability of the\nalgorithm to identify and isolate particle tracks that describe non-analytical\ntrajectories. For curved tracks, the vertex detection recall was 86\\% and the\nprecision 94\\%. For straight tracks, the vertex detection recall was 96\\% and\nthe precision 98\\%. In the case of a test set containing only straight linear\ntracks, the algorithm performed better than an iterative Hough transform.","url_abs":"http://arxiv.org/abs/1807.03513v3","url_pdf":"http://arxiv.org/pdf/1807.03513v3.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":"automatic-trajectory-recognition-in-active","repo_url":"https://github.com/Rujuta219/Ru219","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}