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

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This study refines electron scattering cross sections for nitric oxide (NO) using artificial neural networks. The improved cross sections better match experimental data and electron swarm transport coefficients.

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Area of Science:

  • Atomic and Molecular Physics
  • Plasma Physics
  • Computational Physics

Background:

  • Accurate electron scattering cross sections are crucial for understanding electron transport in gases like nitric oxide (NO).
  • Previous cross section sets for NO showed discrepancies with experimental swarm data, necessitating refinement.

Purpose of the Study:

  • To develop a comprehensive and accurate set of electron scattering cross sections for nitric oxide (NO).
  • To improve the agreement between theoretical calculations and experimental measurements of electron transport in NO.

Main Methods:

  • Review of experimental and theoretical electron scattering cross sections for NO.
  • Multi-term Boltzmann equation analysis of electron swarm transport coefficients in pure NO and NO/Ar mixtures.
  • Artificial neural network training to refine cross sections by solving the inverse problem against swarm data.

Main Results:

  • A refined set of electron-NO cross sections, including quasielastic momentum transfer, dissociative attachment, and neutral dissociation.
  • Demonstrated improved agreement of the refined cross sections with experimental electron swarm transport data.
  • Calculation of electron transport coefficients in NO across a wide range of reduced electric fields (0.003–10,000 Td).

Conclusions:

  • The refined electron-NO cross section set provides a more accurate representation for electron transport studies.
  • Artificial neural network-based refinement is an effective method for improving cross section databases.
  • The study provides a reliable dataset for electron-NO interactions relevant to various applications.