Related papers: Nuclear binding energy predictions based on BP neu…
We investigate the impact of the model estimating the masses of exotic nuclei on r-process nucleosynthesis, assessing the dependence of the abundance distribution on the specific properties of nuclear masses. By decomposing theoretical…
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…
New global statistical models of nuclidic (atomic) masses based on multilayered feedforward networks are developed. One goal of such studies is to determine how well the existing data, and only the data, determines the mapping from the…
In many instances, single nucleon removal reactions from neutron-proton asymmetric projectile nuclei populate final states in the residual nuclei that are very weakly bound. Familiar examples include neutron removal reactions from…
Interactions of neutrinos with nuclei in the energy ranges relevant for the MiniBooNE, T2K, NO$\nu$A, MINER$\nu$A and MINOS experiments are discussed. It is stressed that any theoretical treatment must involve all the relevant reaction…
Neutrino experiments are often limited by low statistics, sizable systematic uncertainties, and coarse observable binning, which can hinder discrimination among competing beyond-the-Standard-Model (BSM) explanations of anomalous signals. In…
We propose a novel approach for predicting protein-peptide interactions using a bi-modal transformer architecture that learns an inter-facial joint distribution of residual contacts. The current data sets for crystallized protein-peptide…
The quantitative understanding of neutrino interactions with nuclei and nuclear matter is needed to the study of many different problems. In the astrophysics environment, neutrino-nucleon and neutrino-nucleus reaction rates are used as…
The interaction of condensed phase systems with external electric fields is crucial in myriad processes in nature and technology ranging from the field-directed motion of cells (galvanotaxis), to energy storage and conversion systems…
We have studied the residual interaction effects theoretically on the deeply bound pionic states in Pb and Sn isotopes. We need to evaluate the residual interaction effects carefully in order to deduce the nuclear medium effects for pion…
Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the…
The density dependencies of various effective interaction strengths in the relativistic mean field are studied and carefully compared for nuclear matter and neutron stars. The influences of different density dependencies are presented and…
1. Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions nonparametrically from time…
High-energy neutrino-nucleus interactions are discussed by considering neutrino-oscillation experiments and ultra-high-energy cosmic neutrino interactions. The largest systematic error for the current neutrino oscillation measurements comes…
Binding energy is a fundamental thermodynamic property that governs molecular interactions, playing a crucial role in fields such as healthcare and the natural sciences. It is particularly relevant in drug development, vaccine design, and…
A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the correct interpretation of neutrino oscillation experiments.…
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…
Ensemble learning algorithms, the gradient boosting and bagging regressors, are employed to correct the residuals of nuclear mass excess for a diverse set of six nuclear mass models. The weighted average of these corrected residuals reduces…
A model describing nuclear collisions at intermediate energies is presented and the results are compared with recently measured double differential cross sections in C-C reactions at 95 MeV/nucleon. Results show the key role played by…
Accurate modeling of neutron-induced (n,p) reaction cross sections is essential for diverse applications in nuclear physics, including reactor design, nuclear astrophysics, and radionuclide production. However, experimental data are often…