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

Stokes, P.W., Casey, M.J.E., Cocks, D. G., De Urquijo, J., Garcia, G., Brunger, M.J., and White, R.D. (2020) Self-consistent electron-THF cross sections derived using data-driven swarm analysis with a neural network model. Plasma Sources Science and Technology, 29 (10). 105008.

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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.

Item ID: 66094
Item Type: Article (Research - C1)
ISSN: 1361-6595
Keywords: swarm analysis, machine learning, artificial neural network, biomolecule
Copyright Information: © 2020 The Author(s). Published by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Funders: Australian Research Council (ARC), Support Program for Research and Technological Innovation Projects (PAPIIT) - National Autonomous University of Mexico (PAPIIT-UNAM), Ministry of Science, Innovation and Universities (MSIU), El Consejo Superior de Investigaciones Científicas (CSIC)
Projects and Grants: Discovery Projects Scheme Grant DP180101655, PAPIIT-UNAM, Project IN118520, MSIU Project FIS2016-80440, MSIU Project PID2019-104727RB-C21, CSIC Project LINKA 20085
Date Deposited: 25 Nov 2020 07:46
FoR Codes: 51 PHYSICAL SCIENCES > 5102 Atomic, molecular and optical physics > 510201 Atomic and molecular physics @ 100%
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