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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.12010 |
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| _version_ | 1866911982833106944 |
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| author | Gao, Lin |
| author_facet | Gao, Lin |
| contents | A deep neural network (DNN) is utilized to study the mass of the pseudoscalar glueball in lattice QCD based on Monte Carlo simulations. To obtain an accurate and stable mass value, I constructed a new network. The results show that this DNN provides a more precise and stable mass estimate compared to the traditional least squares method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12010 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Study of the mass of pseudoscalar glueball with a deep neural network Gao, Lin High Energy Physics - Lattice A deep neural network (DNN) is utilized to study the mass of the pseudoscalar glueball in lattice QCD based on Monte Carlo simulations. To obtain an accurate and stable mass value, I constructed a new network. The results show that this DNN provides a more precise and stable mass estimate compared to the traditional least squares method. |
| title | Study of the mass of pseudoscalar glueball with a deep neural network |
| topic | High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2407.12010 |