Related papers: IKEBANA: A Neural-Network approach for the K-shell…
Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning algorithms have been shown to be an…
A comprehensive compilation of experimental K-shell ionization cross sections induced by electron impact has been assembled, including results up to December 2024. The data are organized according to the target atomic number and to the…
Electron-impact ionization cross sections of atoms and molecules are essential for plasma modelling. However, experimentally determining the absolute cross sections is not easy, and ab initio calculations become computationally prohibitive…
Theoretical expressions for ionization cross sections by electron impact based on the binary encounter Bethe (BEB) model, valid from ionization threshold up to relativistic energies, are proposed. The new modified BEB (MBEB) and its…
We introduce a Bayesian protocol based on artificial neural networks that is suitable for modeling inclusive electron-nucleus scattering on a variety of nuclear targets with quantified uncertainties. Unlike previous applications in the…
The International Atomic Energy Agency (IAEA) stopping power database is a highly valued public resource compiling most of the experimental measurements published over nearly a century. The database-accessible to the global scientific…
A theoretical study of the K-shell ionization of hydrogen-like ions, colliding with bare nuclei, is performed within the framework of the time-dependent Dirac equation. Special emphasis is placed on the ionization probability that is…
A convolutional neural network-based classifier is elaborated to retrace the initial orientation of deformed nucleus-nucleus collisions by integrating multiple typical experimental observables. The isospin-dependent…
Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the details of one process, and…
A new expression for the total K-shell ionization cross section by electron impact based on the relativistic extension of the binary encounter Bethe (RBEB) model, valid from ionization threshold up to relativistic energies, is proposed. The…
We are concerned with improving the diagnostic potential of the K lines and edges of elements with low cosmic abundances that are observed in the X-ray spectra of supernova remnants, galaxy clusters and accreting black holes and neutron…
We propose a model for description of the process of Nuclear Excitation by Electron Transition (NEET) near the $K$-shell ionization threshold of an atom. We explain the experimental results for the $^{197}$Au cross section excitation…
Absolute {\it K}-shell photoionization cross sections for atomic nitrogen have been obtained from both experiment and state-of-the-art theoretical techniques. Due to the difficulty of creating a target of neutral atomic nitrogen, no…
The H\"uckel Hamiltonian is an incredibly simple tight-binding model famed for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only…
Electronic excitations and ionisations produced by electron impact are key processes in the radiation-induced damage mechanisms in materials of biological relevance, underlying important medical and technological applications, including…
Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials. In this work we extend the neural message passing model with an edge update…
An accurate impact parameter determination in a heavy ion collision is crucial for almost all further analysis. The capabilities of an artificial neural network are investigated to that respect. A novel input generation for the network is…
A one parameter expression for the single ionization cross-section of atoms by electron impact is presented. Using this expression, the agreement obtained with available experimental data for 45 elements (from ionization threshold up to 200…
We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions…
The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to…