Self-consistent electron–THF cross sections derived using data-driven swarm analysis with a neural network model. (16th October 2020)

• Record Type: Journal Article
• Authors: Stokes, P W; Casey, M J E; Cocks, D G; de Urquijo, J; García, G; Brunger, M J; White, R D
• Is Part Of: Plasma sources science & technology. Volume 29:Number 10(2020:Oct.)
• Publication Date: 2020-10-16
• Subjects: swarm analysis -- machine learning -- artificial neural network -- biomolecule; Plasma (Ionized gases) -- Periodicals; 530.44
• DOI: 10.1088/1361-6595/abb4f6
• Languages: English
• ISSNs: 0963-0252

Abstract

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.