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

P. W. Stokes, M. J.E. Casey, D. G. Cocks, J. de Urquijo, G. García, M. J. Brunger, R. D. White

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
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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.

Original languageEnglish
Article number105008
Number of pages10
JournalPlasma Sources Science and Technology
Volume29
Issue number10
DOIs
Publication statusPublished - 16 Oct 2020

Keywords

  • Artificial neural network
  • Biomolecule
  • Machine learning
  • Swarm analysis

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