Empirical physical formula for potential energy curves of 38-66Ti isotopes by using neural networks

Abstract

Nuclear shape transition has been actively studied in the past decade. In particular, the understanding of this phenomenon from a microscopic point of view is of great importance. Because of this reason, many works have been employed to investigate shape phase transition in nuclei within the relativistic and non-relativistic mean field models by examining potential energy curves (PECs). In this paper, by using layered feed-forward neural networks (LFNNs), we have constructed consistent empirical physical formulas (EPFs) for the PECs of 38-66Ti calculated in Hartree-Fock-Bogoliubov (HFB) method with SLy4 Skyrme forces. It has been seen that the PECs obtained by neural network method are compatible with those of HFB calculations.

15 pages, 3 figures

References in corpus (8)

• Relativistic Continuum Hartree Bogoliubov Theory for Ground State Properties of Exotic Nuclei
• Configuration mixing of angular-momentum and particle-number projected triaxial HFB states using the Skyrme energy density functional
• Microscopic description of nuclear quantum phase transitions
• E(5), X(5), and Prolate to Oblate Shape Phase Transitions in Relativistic Hartree Bogoliubov Theory
• E(5) and X(5) shape phase transitions within a Skyrme Hartree-Fock + BCS approach
• Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines
• A Study on Shape of Te Isotopes in Mean Field Formalism
• A Global Model of ${\beta }^{-}$-Decay Half-Lives Using Neural Networks

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