Research of Single-Nucleon Separation Energies in the Liquid Drop Model Based on Artificial Neural Networks
Year: 2025
Type: conference-paper
Abstract: This research is based on the Liquid Drop Model (LDM). First, we introduce the artificial neural network (ANN) method to calculate the theoretical predictions of single-nucleon separation energies within the LDM. Subsequently, neural networks are utilized to optimize the theoretical values of single-nucleon separation energies, reducing the root mean square deviation (RMSD) between the theoretical predictions and experimental values. Three partitioning optimization schemes are then implemented: proton magic-number partitioning, neutron magic-number partitioning and proton/neutron parity partitioning. After partitioned optimization training, the RMSD is further reduced. The partitioned training of single-nucleon separation energies yields better results than direct global training, particularly in significantly lowering the RMSD in the light nuclei region. Specifically, single-neutron separation energies exhibit improved accuracy with neutron magic-number partitioning, while single-proton separation energies show better performance with proton magic-number partitioning.
Author: Jinyan Zhang
Institution: University of Shanghai for Science and Technology
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414396180