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arXiv 2201.08458hep-exphysics.ins-det
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利用深度神经网络识别强子τ轻子衰变

Identification of hadronic tau lepton decays using a deep neural network

CMS Collaboration

备注：
Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/TAU-20-001 (CMS Public Pages)
发表信息：
JINST 17 (2022) P07023
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摘要：

提出了一种新的算法来区分CMS探测器中来自真正τ轻子的τ轻子强子衰变（$τ_\mathrm{h}$）和来自夸克或胶子喷注、电子或μ子的$τ_\mathrm{h}$候选衰变。该算法输入来自$τ_\mathrm{h}$候选者附近的所有重构粒子的信息，并采用具有卷积层的深度神经网络来有效地处理输入。该算法导致一个显着改善的性能相比，以前使用的一个。例如，一个真正的$τ_\mathrm{h}$通过反喷流的效率增加了10-30%，对于夸克和胶子喷流的给定效率。此外，一个更有效的$τ_\mathrm{h}$重建引入了额外的强子衰变模式。利用$\sqrt{s} =$ 13 TeV的LHC质子-质子碰撞数据验证了新算法在区分喷注、电子和μ子方面的优越性能以及改进的$τ_\mathrm{h}$重建方法。

英文摘要：

A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons ($τ_\mathrm{h}$) that originate from genuine tau leptons in the CMS detector against $τ_\mathrm{h}$ candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a $τ_\mathrm{h}$ 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 $τ_\mathrm{h}$ to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient $τ_\mathrm{h}$ 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 $τ_\mathrm{h}$ reconstruction method are validated with LHC proton-proton collision data at $\sqrt{s} =$ 13 TeV.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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