# Application of multilayer perceptron with data augmentation in nuclear physics

arXiv: https://arxiv.org/abs/2205.07953 · HTML: https://mlsanity.com/paper/2205.07953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Improving Deep Learning using Generic Data Augmentation](https://mlsanity.com/paper/1708.06020/) (2017)
- [A Comparison of Optimization Algorithms for Deep Learning](https://mlsanity.com/paper/2007.14166/) (2020)
- [Efficient Augmentation via Data Subsampling](https://mlsanity.com/paper/1810.05222/) (2018)
- [Bayesian approach to model-based extrapolation of nuclear observables](https://mlsanity.com/paper/1806.00552/) (2018)
- [Hydranet: Data Augmentation for Regression Neural Networks](https://mlsanity.com/paper/1807.04798/) (2018)

## Built on by (most cited first, 1 of 1)

- [Predicting the Masses of Exotic Hadrons with Data Augmentation Using Multilayer Perceptron](https://mlsanity.com/paper/2208.09538/) (2022)

## Abstract (arXiv)

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.