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.
翻译:最近,神经网络有了重大发展,导致物理文献中经常使用神经网络,这项工作的重点是利用实验所决定的中子和巴伦大体培训神经网络,预测外来大肠杆菌、双层魅力和底部男爵的数量,原始数据集使用最近提议的人工数据增强方法扩展了范围,我们观察到神经网络的预测能力随着扩大数据的使用而提高,结果显示数据增强技术在改进神经网络预测方面发挥着至关重要的作用;此外,神经网络可以对外来大肠杆菌、双层魅力和双层底部男爵做出合理的预测,结果也与高斯进程和立宪夸克模式相似。