Probing stop pair production at the LHC with graph neural networks
Abstract Top-squarks (stops) play a crucial role for the naturalness of supersymmetry (SUSY). However, searching for the stops is a tough task at the LHC. To dig the stops out of the huge LHC data, various expert-constructed kinematic variables or cutting-edge analysis techniques have been invented....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-08-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/JHEP08(2019)055 |
| _version_ | 1818510665231368192 |
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| author | Murat Abdughani Jie Ren Lei Wu Jin Min Yang |
| author_facet | Murat Abdughani Jie Ren Lei Wu Jin Min Yang |
| author_sort | Murat Abdughani |
| collection | DOAJ |
| description | Abstract Top-squarks (stops) play a crucial role for the naturalness of supersymmetry (SUSY). However, searching for the stops is a tough task at the LHC. To dig the stops out of the huge LHC data, various expert-constructed kinematic variables or cutting-edge analysis techniques have been invented. In this paper, we propose to represent collision events as event graphs and use the message passing neutral network (MPNN) to analyze the events. As a proof-of-concept, we use our method in the search of the stop pair production at the LHC, and find that our MPNN can efficiently discriminate the signal and back-ground events. In comparison with other machine learning methods (e.g. DNN), MPNN can enhance the mass reach of stop mass by several tens of GeV to over a hundred GeV. |
| first_indexed | 2024-12-10T23:23:02Z |
| format | Article |
| id | doaj.art-72eeae42ec5c4175b4ad4cd8cce81c67 |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-12-10T23:23:02Z |
| publishDate | 2019-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-72eeae42ec5c4175b4ad4cd8cce81c672022-12-22T01:29:39ZengSpringerOpenJournal of High Energy Physics1029-84792019-08-012019811410.1007/JHEP08(2019)055Probing stop pair production at the LHC with graph neural networksMurat Abdughani0Jie Ren1Lei Wu2Jin Min Yang3Department of Physics and Institute of Theoretical Physics, Nanjing Normal UniversityCAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of SciencesDepartment of Physics and Institute of Theoretical Physics, Nanjing Normal UniversityCAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of SciencesAbstract Top-squarks (stops) play a crucial role for the naturalness of supersymmetry (SUSY). However, searching for the stops is a tough task at the LHC. To dig the stops out of the huge LHC data, various expert-constructed kinematic variables or cutting-edge analysis techniques have been invented. In this paper, we propose to represent collision events as event graphs and use the message passing neutral network (MPNN) to analyze the events. As a proof-of-concept, we use our method in the search of the stop pair production at the LHC, and find that our MPNN can efficiently discriminate the signal and back-ground events. In comparison with other machine learning methods (e.g. DNN), MPNN can enhance the mass reach of stop mass by several tens of GeV to over a hundred GeV.http://link.springer.com/article/10.1007/JHEP08(2019)055Supersymmetry Phenomenology |
| spellingShingle | Murat Abdughani Jie Ren Lei Wu Jin Min Yang Probing stop pair production at the LHC with graph neural networks Journal of High Energy Physics Supersymmetry Phenomenology |
| title | Probing stop pair production at the LHC with graph neural networks |
| title_full | Probing stop pair production at the LHC with graph neural networks |
| title_fullStr | Probing stop pair production at the LHC with graph neural networks |
| title_full_unstemmed | Probing stop pair production at the LHC with graph neural networks |
| title_short | Probing stop pair production at the LHC with graph neural networks |
| title_sort | probing stop pair production at the lhc with graph neural networks |
| topic | Supersymmetry Phenomenology |
| url | http://link.springer.com/article/10.1007/JHEP08(2019)055 |
| work_keys_str_mv | AT muratabdughani probingstoppairproductionatthelhcwithgraphneuralnetworks AT jieren probingstoppairproductionatthelhcwithgraphneuralnetworks AT leiwu probingstoppairproductionatthelhcwithgraphneuralnetworks AT jinminyang probingstoppairproductionatthelhcwithgraphneuralnetworks |