论文标题
来自介子的重子：机器学习的角度
Baryons from Mesons: A Machine Learning Perspective
论文作者
论文摘要
量子染色体动力学（QCD）是强相互作用的理论。 QCD，夸克和胶子的基本颗粒具有彩色电荷，并在低能下形成无色结合状态。我们的主要兴趣的习惯状态是介子和重子。从对介子光谱的了解，我们使用神经网络和高斯过程来预测精度为90.3％和96.6％的重子。这些结果与组成夸克模型相比有利。我们还预测了五夸克人和其他异国情调的哈子的质量。
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.
