Two-component Bayesian modeling in the data analysis of collective Thomson scattering

Abstract

We apply Bayesian inference to the analysis of collective Thomson scattering (CTS) spectra, a diagnostic technique for determining local plasma parameters. Using the population annealing Monte Carlo (PAMC) method, we evaluate the posterior probability density functions (PDFs) for six plasma parameters. The analysis is performed separately for the ion acoustic wave (IAW) and electron plasma wave (EPW) features and jointly for both features. The results show that the combined analysis provides constraints on all six parameters, although the degree of constraint depends on the parameter, whereas the separate analyses provide strong constraints only on complementary subsets of the parameters. In particular, the combined analysis provides a clear constraint on the ion charge state, which is not determined by either separate analysis alone. These findings demonstrate the importance of Bayesian modeling that accounts for the two-component nature of plasmas, consisting of ions and electrons.

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BibTeX RIS

BibTeX

@misc{nakano2026twocomponent,
  title = {{Two-component Bayesian modeling in the data analysis of collective Thomson scattering}},
  author = {Akito Nakano and Kentaro Sakai and Hantao Ji and Lan Gao and Brandon K. Russell and Takeo Hoshi},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.32033},
  eprint = {2610.32033},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.32033},
}

RIS

TY  - UNPB
TI  - Two-component Bayesian modeling in the data analysis of collective Thomson scattering
AU  - Akito Nakano
AU  - Kentaro Sakai
AU  - Hantao Ji
AU  - Lan Gao
AU  - Brandon K. Russell
AU  - Takeo Hoshi
PY  - 2026
DA  - 2026/10/23
PB  - arXiv
DO  - 10.48550/arxiv.2610.32033
UR  - https://arxiv.org/abs/2610.32033
AB  - We apply Bayesian inference to the analysis of collective Thomson scattering (CTS) spectra, a diagnostic technique for determining local plasma parameters. Using the population annealing Monte Carlo (PAMC) method, we evaluate the posterior probability density functions (PDFs) for six plasma parameters. The analysis is performed separately for the ion acoustic wave (IAW) and electron plasma wave (EPW) features and jointly for both features. The results show that the combined analysis provides constraints on all six parameters, although the degree of constraint depends on the parameter, whereas the separate analyses provide strong constraints only on complementary subsets of the parameters. In particular, the combined analysis provides a clear constraint on the ion charge state, which is not determined by either separate analysis alone. These findings demonstrate the importance of Bayesian modeling that accounts for the two-component nature of plasmas, consisting of ions and electrons.
ER  -

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