RESUMO
This paper introduces a novel approach for automated high-throughput estimation of plasma temperature and density using atomic 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 demonstrated using experimental observations in the field of magneto-inertial fusion, focusing on individual and sequential shot analyses of the Plasma Liner Experiment at LANL. The results demonstrate the effectiveness of our approach in enhancing the accuracy and reliability of plasma parameter estimation and in using the analysis to reveal the deep hidden structure in the data. This study not only offers a new perspective of plasma analysis but also paves the way for further research and applications in nuclear instrumentation and related domains.
RESUMO
We present time-resolved measurements of ion heating due to collisional plasma shocks and interpenetrating supersonic plasma flows, which are formed by the oblique merging of two coaxial-gun-formed plasma jets. Our study is repeated using four jet species: N, Ar, Kr, and Xe. In conditions with small interpenetration between jets, the observed peak ion temperature T_{i} is consistent with the predictions of collisional plasma-shock theory showing a substantial elevation of T_{i} above the electron temperature T_{e} and also the subsequent decrease of T_{i} on the classical ion-electron temperature-equilibration timescale. In conditions of significant interpenetration between jets, such that shocks do not apparently form, the observed peak T_{i} is still appreciable and greater than T_{e} but much lower than that predicted by collisional plasma-shock theory. Experimental results are compared with multifluid plasma simulations.
