An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients
Muccignat, Dale L., Boyle, Gregory G., Garland, Nathan A., Stokes, Peter W., and White, Ronald D. (2024) An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients. Machine Learning: Science and Technology, 5. 015047.
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Abstract
We propose improvements to the artificial neural network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the non-unique nature of its solutions, particularly when there exists multiple cross-sections that each describe similar scattering processes. Considering this, prior methods leveraged existing knowledge of a particular cross-section set to reduce the solution space of the problem. To reduce the need for prior knowledge, we propose the following modifications to the ANN method. First, we propose a multi-branch ANN (MBANN) that assigns an independent branch of hidden layers to each cross-section output. We show that in comparison with an equivalent conventional ANN, the MBANN architecture enables an efficient and physics informed feature map of each cross-section. Additionally, we show that the MBANN solution can be improved upon by successive networks that are each trained using perturbations of the previous regression. Crucially, the method requires much less input data and fewer restrictive assumptions, and only assumes knowledge of energy loss thresholds and the number of cross-sections present.
| Item ID: | 86023 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 2632-2153 |
| Copyright Information: | Original 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. |
| Date Deposited: | 02 Jul 2025 01:24 |
| FoR Codes: | 51 PHYSICAL SCIENCES > 5106 Nuclear and plasma physics > 510699 Nuclear and plasma physics not elsewhere classified @ 100% |
| SEO Codes: | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280120 Expanding knowledge in the physical sciences @ 100% |
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