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arXiv 2003.10445hep-phcs.LGhep-lathep-thphysics.comp-ph
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从介子到重子:机器学习的视角

Baryons from Mesons: A Machine Learning Perspective

University of Oxford(牛津大学);The Alan Turing Institute(艾伦图灵研究所);University of the Witwatersrand(威特沃特斯兰德大学);Universidad de Guanajuato(瓜纳华托大学);OATML(OATML(此处可能为特定机构缩写,无明确通用中文译名));Department of Computer Science(计算机科学系(系级单位,按规则不提取));School of Physics(物理学院(学院级单位,按规则不提取));NITheP(NITheP(此处可能为特定机构缩写,无明确通用中文译名));CoE-MaSS(CoE-MaSS(此处可能为特定机构缩写,无明确通用中文译名));Data Laboratory(数据实验室(实验室级单位,按规则不提取));Mandelstam Institute for Theoretical Physics(曼德尔斯坦理论物理研究所(研究所级单位,按规则不提取))

Yarin Gal, Vishnu Jejjala, Damian Kaloni Mayorga Pena, Challenger Mishra

摘要:

量子色动力学(QCD)是研究强相互作用的理论。QCD的基本粒子,夸克和胶子,携带色荷并在低能量下形成无色束缚态。我们最感兴趣的强子束缚态是介子和重子。从介子谱的知识,我们使用神经网络和高斯过程预测重子的质量与90.3%和96.6%的准确性,分别。这些结果与组分夸克模型相比是有利的。我们也预测了五夸克和其他奇异强子的质量。

英文摘要:

Quantum chromodynamics (QCD) is the theory of the strong interaction. The fundamental particles of QCD, quarks and gluons, carry colour charge and form colourless bound states at low energies. The hadronic bound states of primary interest to us are the mesons and the baryons. From knowledge of the meson spectrum, we use neural networks and Gaussian processes to predict the masses of baryons with 90.3% and 96.6% accuracy, respectively. These results compare favourably to the constituent quark model. We as well predict the masses of pentaquarks and other exotic hadrons.

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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