IKEBANA: A Neural-Network approach for the K-shell ionization by electron impact

TL;DR

This paper introduces IKEBANA, a neural network model that accurately predicts K-shell ionization cross sections across elements and energies, providing an accessible tool for researchers.

Contribution

It presents a novel neural network approach trained on extensive experimental data, offering improved predictions and an open-source code for K-shell ionization modeling.

Findings

Neural network achieved high accuracy in predicting experimental data.

Model covers elements from H to U and energies from threshold to relativistic.

The code is openly available for use by the scientific community.

Abstract

A fully connected neural network was trained to model the K-shell ionization cross sections based on two input features: the atomic number and the incoming electron overvoltage. The training utilized a recent, updated compilation of experimental data, covering elements from H to U, and incident electron energies ranging from the threshold to relativistic values. The neural network demonstrated excellent predictive performance, compared with the experimental data, when available, and with full theoretical predictions. The developed model is provided in the ikebana code, which is openly available and requires only the user-selected atomic number and electron energy range as inputs.