Self-consistent electron-THF cross sections derived using data-driven swarm analysis with a neural network model

TL;DR

This paper refines electron-THF cross sections using a neural network to analyze swarm data, improving accuracy and self-consistency for electron transport modeling in gaseous THF.

Contribution

It introduces a data-driven neural network approach to derive self-consistent electron-THF cross sections from swarm measurements, enhancing previous models.

Findings

Refined cross sections show improved agreement with experimental swarm data.

Neural network effectively solves the inverse problem for cross section determination.

Enhanced model supports better simulation of electron transport in THF.

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.