Development of particle flow algorithm with GNN for Higgs factories
Particle flow plays an important role in precise measurement of Higgs bosons at future lepton colliders such as ILC and FCCee. Various detector concepts are designed to maximize the effect of particle flow to be able to separate each particles inside jets and improve the resolutions. For the standar...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2024/25/epjconf_lcws2024_03009.pdf |
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| _version_ | 1841557341674143744 |
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| author | Murata Tatsuki Suehara Taikan |
| author_facet | Murata Tatsuki Suehara Taikan |
| author_sort | Murata Tatsuki |
| collection | DOAJ |
| description | Particle flow plays an important role in precise measurement of Higgs bosons at future lepton colliders such as ILC and FCCee. Various detector concepts are designed to maximize the effect of particle flow to be able to separate each particles inside jets and improve the resolutions. For the standard particle flow algorithm, PandoraPFA is used for long in ILC studies. It is a multi-step reconstruction algorithm consisting of clustering, track-cluster association, and various refinement processes. We have studied machine learned particle flow model using Graph Neural Network based algorithm developed in the context of CMS HGCAL clustering. This model utilizes GravNet as GNN architecture and Object Condensation loss function for training. Since the HG-CAL algorithm only performs clustering at the calorimeter, we have extended the model with track-cluster matching to achieve full PFA. Details of initial implementation of the track-cluster matching algorithm as well as performance evaluation with multiple tau events and jet events will be shown. The results are also compared to the Pandora PFA. |
| format | Article |
| id | doaj-art-058b398adbaf4763867fc9cbccbb943f |
| institution | Kabale University |
| issn | 2100-014X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-058b398adbaf4763867fc9cbccbb943f2025-01-06T11:33:47ZengEDP SciencesEPJ Web of Conferences2100-014X2024-01-013150300910.1051/epjconf/202431503009epjconf_lcws2024_03009Development of particle flow algorithm with GNN for Higgs factoriesMurata Tatsuki0Suehara Taikan1Graduate school of science, the University of TokyoGraduate school of science, the University of TokyoParticle flow plays an important role in precise measurement of Higgs bosons at future lepton colliders such as ILC and FCCee. Various detector concepts are designed to maximize the effect of particle flow to be able to separate each particles inside jets and improve the resolutions. For the standard particle flow algorithm, PandoraPFA is used for long in ILC studies. It is a multi-step reconstruction algorithm consisting of clustering, track-cluster association, and various refinement processes. We have studied machine learned particle flow model using Graph Neural Network based algorithm developed in the context of CMS HGCAL clustering. This model utilizes GravNet as GNN architecture and Object Condensation loss function for training. Since the HG-CAL algorithm only performs clustering at the calorimeter, we have extended the model with track-cluster matching to achieve full PFA. Details of initial implementation of the track-cluster matching algorithm as well as performance evaluation with multiple tau events and jet events will be shown. The results are also compared to the Pandora PFA.https://www.epj-conferences.org/articles/epjconf/pdf/2024/25/epjconf_lcws2024_03009.pdf |
| spellingShingle | Murata Tatsuki Suehara Taikan Development of particle flow algorithm with GNN for Higgs factories EPJ Web of Conferences |
| title | Development of particle flow algorithm with GNN for Higgs factories |
| title_full | Development of particle flow algorithm with GNN for Higgs factories |
| title_fullStr | Development of particle flow algorithm with GNN for Higgs factories |
| title_full_unstemmed | Development of particle flow algorithm with GNN for Higgs factories |
| title_short | Development of particle flow algorithm with GNN for Higgs factories |
| title_sort | development of particle flow algorithm with gnn for higgs factories |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2024/25/epjconf_lcws2024_03009.pdf |
| work_keys_str_mv | AT muratatatsuki developmentofparticleflowalgorithmwithgnnforhiggsfactories AT sueharataikan developmentofparticleflowalgorithmwithgnnforhiggsfactories |