Trees and Forests in Nuclear Physics
This work incrementally improves nuclear physics modeling by applying existing machine learning methods to domain-specific data.
The authors applied decision tree algorithms to improve nuclear mass models, achieving enhanced accuracy in both the classical liquid drop model and the Duflo-Zuker model with a limited number of free parameters.
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.