Identification of hadronic tau lepton decays using a deep neural network
文献类型：期刊论文
| 作者 | The CMS collaboration |
| 刊名 | Journal of Instrumentation
 |
| 出版日期 | 2022 |
| 卷号 | 17期号:7页码:7023 |
| DOI | 10.1088/1748-0221/17/07/P07023 |
| 文献子类 | Article |
| 英文摘要 | A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τ h) that originate from genuine tau leptons in the CMS detector against τ h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τ h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine τ h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τ h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved τ h reconstruction method are validated with LHC proton-proton collision data at s = 13 TeV. © 2022 CERN. |
| 电子版国际标准刊号 | 17480221 |
| 语种 | 英语 |
| 源URL | [http://ir.ihep.ac.cn/handle/311005/299047]  |
| 专题 | 高能物理研究所_实验物理中心 |
| 作者单位 | 中国科学院高能物理研究所 |
| 推荐引用方式 GB/T 7714 | The CMS collaboration. Identification of hadronic tau lepton decays using a deep neural network[J]. Journal of Instrumentation,2022,17(7):7023. |
| APA | The CMS collaboration.(2022).Identification of hadronic tau lepton decays using a deep neural network.Journal of Instrumentation,17(7),7023. |
| MLA | The CMS collaboration."Identification of hadronic tau lepton decays using a deep neural network".Journal of Instrumentation 17.7(2022):7023. |
入库方式： OAI收割
来源：高能物理研究所
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