Neural networks have become popular in many fields of science since they serve as promising, reliable and powerful tools. In this work, we study the effect of data augmentation on the predictive power of neural network models for nuclear physics data. We present two different data augmentation techniques, and we conduct a detailed analysis in terms of different depths, optimizers, activation functions and random seed values to show the success and robustness of the model. Using the experimental uncertainties for data augmentation for the first time, the size of the training data set is artificially boosted and the changes in the root-mean-square error between the model predictions on the test set and the experimental data are investigated. Our results show that the data augmentation decreases the prediction errors, stabilizes the model and prevents overfitting. The extrapolation capabilities of the MLP models are also tested for newly measured nuclei in AME2020 mass table, and it is shown that the predictions are significantly improved by using data augmentation.

翻译：神经网络在科学的许多领域已经变得很受欢迎,因为它们是充满希望、可靠和强大的工具。在这项工作中,我们研究了数据增强对核物理数据神经网络模型预测力的影响。我们介绍了两种不同的数据增强技术,并详细分析了不同深度、优化器、激活功能和随机种子值,以显示模型的成功性和稳健性。首次利用数据增强的实验不确定性,人工提升了培训数据集的规模,并调查了测试数据集模型预测与实验数据之间的根值差值差值变化。我们的结果显示,数据增加减少了预测误差,稳定了模型,防止了过度匹配。MLP模型的外推法能力也在AME2020质量表中测试了新测量的核值,并显示,利用数据增强,预测大大改进了。