Related papers: Deep learning on nuclear mass and $\alpha$ decay h…

Deep neural networks (DNNs) at convergence consistently represent the training data in the last layer via a highly symmetric geometric structure referred to as neural collapse. This empirical evidence has spurred a line of theoretical…

Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving…

Half-lives of beta+ decay and electron capture are studied in some selected superheavy nuclei produced in hot fusion reactions, namely, 290Fl, 293Mc, 294Lv, and 295Ts. The nuclear structure is described microscopically from deformed…

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…

Purpose: Our objective is to apply an improved statistical global model of beta^- decay half-life systematics [1] generated by machine-learning techniques to the prediction of beta half-lives relevant to r-process nuclei. The primary aim of…

We revisit the "interpolating formula" proposed in our previous publication. It allows one to calculate the neutrinoless double beta decay ($0\nu\beta\beta$-decay) half-life for arbitrary neutrino mass without involvement of the complicated…

In present work, we systematically study the $\alpha$ decay half-lives of 170 even-even nuclei with $60 \leqslant Z \leqslant 118$ within the two-potential approach while the $\alpha$ decay preformation factor $P_\alpha$ is obtained by the…

The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of protein models. Most assessment methods rank candidate models using heavily engineered structural features, defined…

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks…

We have calculated the masses, deformations, and other properties of 8979 nuclei ranging from oxygen-16 to Z = 136, A = 339 and extending from the proton drip line to the neutron drip line on the basis of the 1992 version of the…

While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited. In an attempt to bridge the gap, we investigate the decision boundary of a…

The prediction of nuclear observables beyond the finite model spaces that are accessible through modern ab initio methods, such as the no-core shell model, pose a challenging task in nuclear structure theory. It requires reliable tools for…

Neutrinoless double beta decay searches are currently among the major foci of experimental physics. The observation of such a decay will have important implications in our understanding of the intrinsic nature of neutrinos and shed light on…

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…

Within the concept of the dinuclear system (DNS), a dynamical model is proposed for describing the formation of superheavy nuclei in complete fusion reactions by incorporating the coupling of the relative motion to the nucleon transfer…

Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for…

High-precision predictions of nuclear properties are a central objective of ab initio nuclear structure theory. However, state-of-the-art many-body methods rely on truncated model spaces to render the nuclear many-body problem tractable,…

Bayesian neural network (BNN) approach is employed to improve the nuclear mass predictions of various models. It is found that the noise error in the likelihood function plays an important role in the predictive performance of the BNN…

We show that the Liquid Drop Model is best suited to describe the masses of prolate deformed nuclei than of spherical nuclei. To this end three Liquid Drop Mass formulas are employed to describe nuclear masses of eight sets of nuclei with…

In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime.…