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

We present a method to extrapolate nuclear binding energies from known values for neighbouring nuclei. We select four specific mass relations constructed to eliminate smooth variation of the binding energy as function nucleon numbers. The…

There is an increasing interest in quantifying the predictive power in nuclear structure calculations. We discuss how both experimental and systematic errors at the NN-level can be used to estimate the theoretical uncertainties by rather…

It is fascinating to predict the mass and width of the ordinary and exotic mesons solely based on their quark content and quantum numbers. Such prediction goes beyond conventional methodologies traditionally employed in hadron physics for…

$Q_\beta$ represents one of the most important factors characterizing unstable nuclei, as it can lead to a better understanding of nuclei behavior and the origin of heavy atoms. Recently, machine learning methods have been shown to be a…

Evaluated $2\beta^{-}(2\nu)$ half-lives and their systematics were reexamined in the framework of a phenomenological approach. Decay rate dependence on nuclear deformation, decay energy, shape coexistence, and forbidden transitions was…

Beta-decay rates of extreme neutron-rich nuclei remain largely unknown experimentally, while they are critical inputs for $r$-process nucleosynthesis. We present first ab initio calculations of total beta-decay half-lives, with a focus on…

Superheavy nuclei represent the heaviest atoms and nuclides known at the limit of mass and charge. The observed superheavy nuclei are all proton-rich; they decay primarily by emitting $\alpha$ particles and fission, with a possible small…

A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to…

Neural networks (NNs) have great potential in solving the ground state of various many-body problems. However, several key challenges remain to be overcome before NNs can tackle problems and system sizes inaccessible with more established…

In recent years, artificial neural networks and their applications for large data sets have became a crucial part of scientific research. In this work, we implement the Multilayer Perceptron (MLP), which is a class of feedforward artificial…

We present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model and predict nuclear dipole responses. We train the network on a dataset of experimentally measured…

In this work, we deep-learn light charged Higgs signal in top quark decays which poses difficulties due to strong W boson contamination. We construct Deep Neural Networks (DNN) with appropriate architecture and determine signal extraction…

We implement machine learning algorithms to nuclear data. These algorithms are purely data driven and generate models that are capable to capture intricate trends. Gradient boosted trees algorithm is employed to generate a trained model…

Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical…

Nuclear mass contains a wealth of nuclear structure information, and has been widely employed to extract the nuclear effective interactions. The known nuclear mass is usually extracted from the experimental atomic mass by subtracting the…

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…

The set of parameters for alpha-nucleus potential is derived by using the data for both the alpha-decay half-lives and the fusion cross-sections around the barrier for reactions alpha+40Ca, alpha+59Co, alpha+208Pb. The alpha-decay…

Theoretical decay half-lives of the heaviest odd-Z nuclei are calculated using the experimental Q value. The barriers in the quasimolecular shape path are determined within a Generalized Liquid Drop Model (GLDM) and the WKB approximation is…

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z=118), it is estimated that as many as about 3000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but…

Nuclear masses are machine-learned as a function of proton and neutron numbers. The neural network with additive Gaussian process regression-optimized activation functions (GPR-NN) method is employed for the first time for this purpose.…