An improved set of electron-THFA cross sections refined through a neural network-based analysis of swarm data

TL;DR

This paper refines electron-THFA cross sections using neural network analysis of swarm data, improving consistency between experimental measurements and simulations for electron transport in THFA.

Contribution

It introduces a neural network-based method to refine electron-THFA cross sections, enhancing the accuracy of swarm simulation models.

Findings

Neural network improved the consistency of cross sections with experimental data.

Refined cross sections lead to more accurate electron transport coefficients.

Comparison shows better agreement between simulation and experiment after refinement.

Abstract

We review experimental and theoretical cross sections for electron transport in $\alpha$-tetrahydrofurfuryl alcohol (THFA) and, in doing so, propose a plausible complete set. To assess the accuracy and self-consistency of our proposed set, we use the pulsed-Townsend technique to measure drift velocities, longitudinal diffusion coefficients and effective Townsend first ionisation coefficients for electron swarms in admixtures of THFA in argon, across a range of density-reduced electric fields from 1 Td to 450 Td. These measurements are then compared to simulated values derived from our proposed set using a multi-term solution of Boltzmann's equation. We observe discrepancies between the simulation and experiment, which we attempt to address by employing a neural network model that is trained to solve the inverse swarm problem of unfolding the cross sections underpinning our experimental swarm measurements. What results from our neural network-based analysis is a refined set of electron-THFA cross sections, which we confirm is of higher consistency with our swarm measurements than that we initially proposed. We also use our data base to calculate electron transport coefficients in pure THFA, across a range of reduced electric fields from 0.001 Td to 10,000 Td.