Application of multilayer perceptron with data augmentation in nuclear physics

TL;DR

This paper demonstrates that data augmentation techniques significantly improve the predictive accuracy, stability, and extrapolation ability of multilayer perceptron models in nuclear physics applications.

Contribution

The study introduces novel data augmentation methods using experimental uncertainties and analyzes their impact on neural network performance in nuclear physics.

Findings

Data augmentation reduces prediction errors.

It stabilizes neural network training.

It enhances extrapolation to new nuclei.

Abstract

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.