Trees and Forests in Nuclear Physics

Abstract

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.

References in corpus (3)

• Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
• Studies of Boosted Decision Trees for MiniBooNE Particle Identification
• Impact of statistical uncertainties on the composition of the outer crust of a neutron star

Cited by in corpus (7)

• Machine Learning in Nuclear Physics
• Machine learning the nuclear mass
• Decay of superheavy nuclei based on the random forest algorithm
• Extrapolating from neural network models: a cautionary tale
• Machine learning light hypernuclei
• Controlling extrapolations of nuclear properties with feature selection
• Impact of Nuclear Deformation of Parent and Daughter Nuclei on One Proton Radioactivity Lifetimes