Bulletin of the American Physical Society
64th Annual Meeting of the APS Division of Plasma Physics
Volume 67, Number 15
Monday–Friday, October 17–21, 2022; Spokane, Washington
Session YO08: Measurement and Diagnostic Techniques for Low Temperature Plasmas
9:30 AM–12:30 PM,
Friday, October 21, 2022
Room: 402 ABC
Chair: Sophia Gershman, PPPL
Abstract: YO08.00005 : Bayesian Inference for Plasma Temperature and Density from Emission Spectroscopy
10:18 AM–10:30 AM
Presenter:
Todd A Oliver
(University of Texas at Austin)
Authors:
Todd A Oliver
(University of Texas at Austin)
Craig Michoski
(Sapient AI)
Samuel J Langendorf
(Los Alamos National Laboratory)
density by fitting a model spectrum to observations. In this talk, we
develop a Bayesian inference approach to this problem. The primary
innovation of this work is a technique that accounts for the possible
discrepancy between the mathematical model and the observations.
Specifically, we pose a typical algebraic model of the spectrum as a
sum of lines, each with an associated position, strength, and width.
The discrepancy model is based on plausible perturbations of this
algebraic model---i.e., perturbations of the position, strength, and
width parameters. These plausible perturbations are characterized by
a statistical model, the parameters of which are inferred
simultaneously with the plasma temperature and density, so that they
account for actual discrepancies between the observed and modeled
spectrum. By constructing the discrepancy model in this way it is
able to account for mismatches between the modeled and observed
spectra that may arise due to experimental noise, contaminants in the
plasma, and modeling errors. This protects against overly certain
inferences for the parameters of interest, leading to more realistic
uncertainty in the inferred quantities. As an example, the process is
applied to a series of observed spectra recorded at the Plasma Liner
Experiment (PLX) facility.
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