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

2025/01/28 by Stokes, P.W., Casey, M.J.E., Cocks, D.G. +4
#530 #Artificial neural network #Biomolecule #Machine learning #Swarm analysis

paper · doi:10.34657/17540

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 [1] 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 analyzed swarm transport coefficient measurements to those simulated via the numerical solution of Boltzmann’s equation.

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