Self-consistent electron-THF cross sections derived using data-driven swarm analysis with a neural network model
Abstract
We present a set of self-consistent cross sections for electron transport in gaseous tetrahydrofuran (THF), that refines the set published in our previous study [J. de Urquijo et al., J. Chem. Phys. 151, 054309 (2019)] by proposing modifications to the quasielastic momentum transfer, neutral dissociation, ionisation and electron attachment cross sections. These adjustments are made through the analysis of pulsed-Townsend swarm transport coefficients, for electron transport in pure THF and in mixtures of THF with argon. To automate this analysis, we employ a neural network model that is trained to solve this inverse swarm problem for realistic cross sections from the LXCat project. The accuracy, completeness and self-consistency of the proposed refined THF cross section set is assessed by comparing the analysed swarm transport coefficient measurements to those simulated via the numerical solution of Boltzmann's equation.
Cite
@article{arxiv.2007.02762,
title = {Self-consistent electron-THF cross sections derived using data-driven swarm analysis with a neural network model},
author = {Peter W. Stokes and Madalyn J. E. Casey and Daniel G. Cocks and Jaime de Urquijo and Gustavo García and Michael J. Brunger and Ronald D. White},
journal= {arXiv preprint arXiv:2007.02762},
year = {2020}
}
Comments
20 pages, 8 figures, submitted to Plasma Sources Science and Technology