Automated Estimation of Plasma Temperature and Density from Emission Spectroscopy

TL;DR

This paper presents a new automated method combining Bayesian inference and physical models to accurately estimate plasma temperature and density from emission spectroscopy data, validated through experiments.

Contribution

It introduces a novel Bayesian framework for plasma diagnostics that improves estimation accuracy and reliability over existing methods.

Findings

Enhanced accuracy in plasma parameter estimation

Validated approach through experimental data

Potential applications in nuclear instrumentation

Abstract

This paper introduces a novel approach for automated estimation of plasma temperature and density using emission spectroscopy, integrating Bayesian inference with sophisticated physical models. We provide an in-depth examination of Bayesian methods applied to the complexities of plasma diagnostics, supported by a robust framework of physical and measurement models. Our methodology is validated through experimental observations, focusing on individual and sequential shot analyses. The results demonstrate the effectiveness of our approach in enhancing the accuracy and reliability of plasma parameter estimation, marking a significant advancement in the field of emission spectroscopy for plasma diagnostics. This study not only offers a new perspective in plasma analysis but also paves the way for further research and applications in nuclear instrumentation and related domains.