Hadronic Top Quark Polarimetry with ParticleNet
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910842972274688 |
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| author | Dong, Zhongtian Gonçalves, Dorival Kong, Kyoungchul Larkoski, Andrew J. Navarro, Alberto |
| author_facet | Dong, Zhongtian Gonçalves, Dorival Kong, Kyoungchul Larkoski, Andrew J. Navarro, Alberto |
| contents | Precision studies for top quark physics are a cornerstone of the Large Hadron Collider program. Polarization, probed through decay kinematics, provides a unique tool to scrutinize the top quark across its various production modes and to explore potential new physics effects. However, the top quark most often decays hadronically, for which unambiguous identification of its decay products sensitive to top quark polarization is not possible. In this Letter, we introduce a jet flavor tagging method to significantly improve spin analyzing power in hadronic decays, going beyond exclusive kinematic information employed in previous studies. We provide parametric estimates of the improvement from flavor tagging with any set of measured observables and demonstrate this in practice on simulated data using a Graph Neural Network (GNN). We find that the spin analyzing power in hadronic decays can improve by approximately 20% (40%) compared to the kinematic approach, assuming an efficiency of 0.5 (0.2) for the network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01663 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hadronic Top Quark Polarimetry with ParticleNet Dong, Zhongtian Gonçalves, Dorival Kong, Kyoungchul Larkoski, Andrew J. Navarro, Alberto High Energy Physics - Phenomenology High Energy Physics - Experiment Precision studies for top quark physics are a cornerstone of the Large Hadron Collider program. Polarization, probed through decay kinematics, provides a unique tool to scrutinize the top quark across its various production modes and to explore potential new physics effects. However, the top quark most often decays hadronically, for which unambiguous identification of its decay products sensitive to top quark polarization is not possible. In this Letter, we introduce a jet flavor tagging method to significantly improve spin analyzing power in hadronic decays, going beyond exclusive kinematic information employed in previous studies. We provide parametric estimates of the improvement from flavor tagging with any set of measured observables and demonstrate this in practice on simulated data using a Graph Neural Network (GNN). We find that the spin analyzing power in hadronic decays can improve by approximately 20% (40%) compared to the kinematic approach, assuming an efficiency of 0.5 (0.2) for the network. |
| title | Hadronic Top Quark Polarimetry with ParticleNet |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2407.01663 |