Related papers: Bayesian neural network with autoencoder for model…

Recently, experimental researches on the $\alpha$ decay with long lifetime are one of hot topics in the contemporary nuclear physics [e.g. N. Kinoshita {\sl et al.} (2012) and J. W. Beeman {\sl et al.} (2012) ]. In this study, we have…

In this work, a microscopic effective nucleon-nucleon interaction based on the Dirac-Brueckner-Hartree-Fock $G$ matrix starting from a bare nucleon-nucleon interaction is used to explore the $\alpha$-decay half-lives of the nuclei near…

Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. Most such models use deterministic attention while stochastic attention is less explored due to the optimization difficulties or complicated…

For $\alpha$ decay half-life calculations in this work, the Coulomb and proximity potential model with a new semiempirical formula for diffuseness parameter developed in previous work [Phys. Rev. C 100, 024601 (2019)] is used. The present…

We reexamine the nuclear structure properties of waiting point nuclei around A70 using the interacting boson model 1 (IBM 1) and the relativistic mean field (RMF) model. Effective density dependent meson exchange functional (DD ME2) and…

Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engineering has been thus far mostly driven by the goal of…

A series of findings in machine learning (ML) and decay theory are captured while exploring the role of deformation and preformation factors in {\alpha} decay. We provide a novel and practical paradigm for developing physics-driven machine…

In this paper, we have calculated the $\alpha$-decay half-lives of superheavy nuclei with $106 \leq Z \leq 126$ and a neutron number of $150 \leq N \leq 200$ within proximity potentials and deformed-spherical Coulomb potentials by using…

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…

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…

We systematically study the competition between {\alpha}-decay and spontaneous fission in even-even superheavy nuclei with (Z=120) and 256 \leq A \leq 304 within the preformed cluster-decay model using microscopic inputs from relativistic…

Experimental $\alpha$-decay half-life, spin, and parity of 398 nuclei in the range 50$\leq$Z$\leq$118 are utilized to propose a new formula (QF) with only 4 coefficients as well as to modify the Tagepera-Nurmia formula with just 3…

Lifetime values for alpha decay in even-even nuclei with $Z=84-98$ and $N=128-152$ have been calculated in the superasymmetric fission model. The interaction between the alpha particle and the daughter nucleus has been formed in the double…

Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing…

Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications,…

A recent proposed method for $\alpha$-decay energies ($Q_\alpha$) [J.M. Dong, W. Zuo, and W. Scheid, Phys. Rev. Lett. \textbf{107}, 012501 (2011)] can reproduce experimental data of superheavy nuclei (SHN) with an $rms$-value of less than…

Convolutional neural network (CNN) has achieved unprecedented success in image super-resolution tasks in recent years. However, the network's performance depends on the distribution of the training sets and degrades on out-of-distribution…

In this study, a Bayesian Network (BN) is considered to represent a nuclear plant mechanical system degradation. It describes a causal representation of the phenomena involved in the degradation process. Inference from such a BN needs to…

A detailed model for the calculation of beta decay rates of the $fp$ shell nuclei for situations prevailing in pre-supernova and collapse phases of evolution of the core of massive stars leading to supernova explosion has been extended for…

Self-consistent proton-neutron quasiparticle random phase approximation based on the spherical nonlinear point-coupling relativistic Hartree-Bogoliubov theory is established and used to investigate the $\beta^+$/EC-decay half-lives of…