# Trees and Islands -- Machine learning approach to nuclear physics

## TL;DR

This paper applies machine learning, specifically gradient boosted trees, to nuclear physics data to predict various nuclear properties, including those of superheavy elements, demonstrating the method's accuracy and potential for scientific insights.

## Contribution

It introduces a data-driven machine learning approach using gradient boosted trees to predict complex nuclear physics parameters, including for superheavy elements.

## Key findings

- Predicted nuclear properties with low standard deviation.
- Successfully modeled level density parameters for superheavy elements.
- Demonstrated machine learning's effectiveness in nuclear physics prediction tasks.

## Abstract

We implement machine learning algorithms to nuclear data. These algorithms are purely data driven and generate models that are capable to capture intricate trends. Gradient boosted trees algorithm is employed to generate a trained model from existing nuclear data, which is used for prediction for data of damping parameter, shell correction energies, quadrupole deformation, pairing gaps, level densities and giant dipole resonance for large number of nuclei. We, in particular, predict level density parameter for superheavy elements which is of great current interest. The predictions made by the machine learning algorithm is found to have standard deviation from 0.00035 to 0.73.