Bayesian Neural Network improvements to nuclear mass formulae and predictions in the SuperHeavy Elements region

A systematic study based on the Bayesian Neural Network (BNN) statistical approach is introduced to improve the predictive power of current nuclear mass formulae when applied to nuclides not yet experimentally detected. In a previous work by the present authors, the methodology was introduced considering only the Duflo-Zuker mass model (Duno J. and ZUKER A., Phys. Rev. C, 52 (1995) R23) to explore the SuperHeavy Elements (SHE) region, with focus on the alpha-decay process. Due to the discrepancy among different mass formulae we decided to extend in the present calculation the application of the Bayesian Neural Network methodology to other mass formula models and to discussing their implications on predictions of SHE alpha-decay half-lives. The Q(alpha)-value prediction using a set of ten different. mass models has been greatly improved for all models when compared to the available experimental data. In addition, we have used the improved Q(alpha)-value to determine the SHE alpha-decay half-lives with a well-succeeded model in the literature, currently employed for different hadronic nuclear decay modes of heavy nuclei, the Effective Liquid Drop Model (ELDM). Possible SHE candidates recently investigated are explicitly calculated (specially the (298,299,300) 120 isotopes, and results present a promising via of research for these nuclei through alpha-decay process. Copyright (C) EPLA, 2019