{"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/efficient-antihydrogen-detection-in","title":"Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning","arxiv_id":"1706.01826","date":"2017-06-06","proceeding":null,"authors":["Peter Sadowski","Balint Radics","Ananya","Yasunori Yamazaki","Pierre Baldi"],"abstract":"Antihydrogen is at the forefront of antimatter research at the CERN\nAntiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry\nand antigravity effects require the efficient detection of antihydrogen\nannihilation events, which is performed using highly granular tracking\ndetectors installed around an antimatter trap. Improving the efficiency of the\nantihydrogen annihilation detection plays a central role in the final\nsensitivity of the experiments. We propose deep learning as a novel technique\nto analyze antihydrogen annihilation data, and compare its performance with a\ntraditional track and vertex reconstruction method. We report that the deep\nlearning approach yields significant improvement, tripling event coverage while\nsimultaneously improving performance by over 5% in terms of Area Under Curve\n(AUC).","url_abs":"http://arxiv.org/abs/1706.01826v1","url_pdf":"http://arxiv.org/pdf/1706.01826v1.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":"efficient-antihydrogen-detection-in","repo_url":"https://github.com/bayesianGirl/Antihydrogen-Detection-by-Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}