# Trees and Forests in Nuclear Physics

arXiv: https://arxiv.org/abs/2002.10290 · HTML: https://mlsanity.com/paper/2002.10290/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

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

- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)

## Abstract (arXiv)

We present a simple introduction to the decision tree algorithm using some examples from nuclear physics. We show how to improve the accuracy of the classical liquid drop nuclear mass model by performing Feature Engineering with a decision tree. Finally, we apply the method to the Duflo-Zuker model showing that, despite their simplicity, decision trees are capable of improving the description of nuclear masses using a limited number of free parameters.