Inclusion of Electron Interactions by Rate Equations in Chemical Models

The concept of treating subranges of the electron energy spectrum as species in chemical models is investigated. This is intended to facilitate simple modification of chemical models by incorporating the electron interactions as additional rate equations. It is anticipated that this embedding of fine details of the energy dependence of the electron interactions into rate equations will yield an improvement in computational efficiency compared to other methods. It will be applicable in situations where the electron density is low enough that the electron interactions with chemical species are significant compared to electron–electron interactions. A target application is the simulation of electron processes in the D-region of the Earth’s atmosphere, but it is anticipated that the method would be useful in other areas, including enhancement of Monte Carlo simulation of electron–liquid interactions and simulations of chemical reactions and radical generation induced by electrons and positrons in biomolecular systems. The aim here is to investigate the accuracy and practicality of the method. In particular, energy must be conserved, while the number of subranges should be small to reduce computation time and their distribution should be logarithmic in order to represent processes over a wide range of electron energies. The method is applied here to the interaction by inelastic and superelastic collisions of electrons with a gas of molecules with only one excited vibrational level. While this is unphysical, it allows the method to be validated by checking for accuracy, energy conservation, maintenance of equilibrium and evolution of a Maxwellian electron spectrum

An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients

We propose improvements to the Artificial Neural Network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the non-unique nature of its solutions, particularly when there exists multiple cross-sections that each describe similar scattering processes. Considering this, prior methods leveraged existing knowledge of a particular cross-section set to reduce the solution space of the problem. To reduce the need for prior knowledge, we propose the following modifications to the ANN method. First, we propose a Multi-Branch ANN (MBANN) that assigns an independent branch of hidden layers to each cross-section output. We show that in comparison with an equivalent conventional ANN, the MBANN architecture enables an efficient and physics informed feature map of each cross-section. Additionally, we show that the MBANN solution can be improved upon by successive networks that are each trained using perturbations of the previous regression. Crucially, the method requires much less input data and fewer restrictive assumptions, and only assumes knowledge of energy loss thresholds and the number of cross-sections present

Simulating the Feasibility of Using Liquid Micro-Jets for Determining Electron–Liquid Scattering Cross-Sections

The extraction of electron–liquid phase cross-sections (surface and bulk) is proposed through the measurement of (differential) energy loss spectra for electrons scattered from a liquid micro-jet. The signature physical elements of the scattering processes on the energy loss spectra are highlighted using a Monte Carlo simulation technique, originally developed for simulating electron transport in liquids. Machine learning techniques are applied to the simulated electron energy loss spectra, to invert the data and extract the cross-sections. The extraction of the elastic cross-section for neon was determined within 9% accuracy over the energy range 1–100 eV. The extension toward the simultaneous determination of elastic and ionisation cross-sections resulted in a decrease in accuracy, now to within 18% accuracy for elastic scattering and 1% for ionisation. Additional methods are explored to enhance the accuracy of the simultaneous extraction of liquid phase cross-sections