Related papers: Bayesian optimization and nonlocal effects method …
Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. While they could capture more accurately the posterior…
Recently, V.Yu. Denisov proposed a new empirical formula incorporating the deformation of the daughter nucleus, which has significantly improved the description of $\alpha$-decay half-lives for even-even nuclei compared to formulas…
Charge radii can be generally used to encode information about various fine structures of finite nuclei. In this work, a constructed Bayesian neural network based on the Monte Carlo dropout approach is proposed to accurately describe the…
How nuclides decay in the superheavy region is key information for investigating new elements beyond oganesson and the island of stability. The Random Forest algorithm is applied to study the competition between different decay modes in the…
Nuclear masses are predicted with the Bayesian neural networks by learning the mass surface of even-even nuclei and the correlation energies to their neighbouring nuclei. By keeping the known physics in various sophisticated mass models and…
We report microscopic calculation of key $\beta$-decay properties for some of the crucial waiting point species having neutron closed magic shells 50 and 82. Our calculation bear astrophysical significance vis-\'{a}-vis speeding of the…
This article tackles the old problem of prediction via a nonparametric transformation model (NTM) in a new Bayesian way. Estimation of NTMs is known challenging due to model unidentifiability though appealing because of its robust…
The interaction potential between the alpha particle and the deformed parent nucleus was used for description of the decay of superheavy nuclei. It consists of centrifugal, nuclear and Coulomb parts suitably modified for deformed nuclei.…
Mutiple experimental results indicate the existence of cosmophysical effects which influence parameters of nuclear decays and chemical reactions in lab. conditions. In particular, variations of nucleus alpha-,beta-decay parameters with…
The $\alpha$-decay half-lives of superheavy nuclei with $100 \leq Z \leq 120$ are comprehensively analyzed using the axially deformed relativistic mean field (RMF) formalism for the NL3$^*$ parameter set. We employ RMF binding energies to…
The alpha decay half-lives of the recently produced isotopes of the 112, 114, 116 and 118 nuclei and decay products have been calculated in the quasi-molecular shape path using the experimental Qalpha value and a Generalized Liquid Drop…
This paper addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring…
In recent years, several successful applications of the Artificial Neural Networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry, meteorology, and other fields of science. A major goal of…
We introduce a new method, called CNNAS (convolutional neural networks for atomistic systems), for calculating the total energy of atomic systems which rivals the computational cost of empirical potentials while maintaining the accuracy of…
Theoretical estimates for the lifetimes of several isotopes of heavy elements with Z=102-120 are presented by calculating the quantum mechanical tunneling probability in a WKB framework and using microscopic nucleus-nucleus potential…
We employ, within the framework of Skyrme energy-density functional theory, the subtracted second random-phase approximation, recently developed for charge-exchange excitations, to compute $\beta$-decay half-lives in four nuclei, $^{24}$O,…
We investigate the possibility to discriminate between different pairs of CP non-conserving mechanisms inducing the neutrinoless double beta $\betabeta$-decay by using data on $\betabeta$-decay half-lives of nuclei with largely different…
Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show…
Predictions of nuclear properties far from measured data are inherently imprecise because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model…
We analyze the nearest neighbor spacing distributions of low-lying 2+ levels of even-even nuclei. We grouped the nuclei into classes defined by the quadrupole deformation parameter (Beta2). We calculate the nearest neighbor spacing…