Toward a complete and comprehensive cross section database for electron scattering from NO using machine learning

TL;DR

This paper develops a comprehensive electron scattering cross section database for nitric oxide (NO) using experimental data, theoretical analysis, and machine learning to refine the data for better agreement with swarm measurements and electron transport calculations.

Contribution

It introduces a machine learning approach to refine electron-NO cross sections, improving consistency with experimental swarm data and enabling accurate transport coefficient calculations.

Findings

Refined cross section set shows better agreement with experimental swarm data.

Machine learning effectively solves the inverse problem for cross section refinement.

Transport coefficients calculated across a wide electric field range.

Abstract

We review experimental and theoretical cross sections for electron scattering in nitric oxide (NO) and form a comprehensive set of plausible cross sections. To assess the accuracy and self-consistency of our set, we also review electron swarm transport coefficients in pure NO and admixtures of NO in Ar, for which we perform a multi-term Boltzmann equation analysis. We address observed discrepancies with these experimental measurements by training an artificial neural network to solve the inverse problem of unfolding the underlying electron-NO cross sections, while using our initial cross section set as a base for this refinement. In this way, we refine a suitable quasielastic momentum transfer cross section, a dissociative electron attachment cross section and a neutral dissociation cross section. We confirm that the resulting refined cross section set has an improved agreement with the experimental swarm data over that achieved with our initial set. We also use our refined data base to calculate electron transport coefficients in NO, across a large range of density-reduced electric fields from 0.003 Td to 10,000 Td.