论文标题
使用多层感知来预测具有数据增强的外来黑龙的质量
Predicting the Masses of Exotic Hadrons with Data Augmentation Using Multilayer Perceptron
论文作者
论文摘要
最近，神经网络发生了重大发展，这导致了物理文献中经常使用神经网络。这项工作的重点是预测外来的哈德子的质量，这些hadron的质量是使用经过实验确定的梅森和巴属质量训练的神经网络双重迷人和底层的重子。原始数据集已使用最近提出的人工数据增强方法扩展。我们已经观察到神经网络的预测能力随着使用增强数据而增加。结果表明，数据增强技术在改善神经网络预测中起着至关重要的作用。此外，神经网络可以对异国情调的哈德子做出合理的预测，双重迷人和双重底层的重子。结果也与高斯过程和组成夸克模型相媲美。
Recently, there have been significant developments in neural networks, which led to the frequent use of neural networks in the physics literature. This work is focused on predicting the masses of exotic hadrons, doubly charmed and bottomed baryons using neural networks trained on meson and baryon masses that are determined by experiments. The original data set has been extended using the recently proposed artificial data augmentation methods. We have observed that the neural network's predictive ability increases with the use of augmented data. The results indicated that data augmentation techniques play an essential role in improving neural network predictions; moreover, neural networks can make reasonable predictions for exotic hadrons, doubly charmed, and doubly bottomed baryons. The results are also comparable to Gaussian Process and Constituent Quark Model.
