Application of multilayer perceptron with data augmentation in nuclear physics
This work addresses prediction accuracy and robustness in nuclear physics modeling, but it is incremental as it applies existing data augmentation methods to a specific domain.
The study applied data augmentation techniques to multilayer perceptron models for nuclear physics data, showing that it reduces prediction errors, stabilizes the model, prevents overfitting, and improves extrapolation capabilities for newly measured nuclei.
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.