Trees and Islands -- Machine learning approach to nuclear physics
This work addresses nuclear physics challenges by applying existing machine learning methods to new data, making it incremental in nature.
The authors tackled the problem of predicting various nuclear properties, such as level density parameters for superheavy elements, using a purely data-driven machine learning approach, achieving predictions with standard deviations ranging from 0.00035 to 0.73.
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.