Bayesian Spectral Moment Estimation and Uncertainty Quantification

Cao, Norman M.; Sciortino, Francesco

doi:10.1109/tps.2019.2946952

Bayesian Spectral Moment Estimation and Uncertainty Quantification

Journal Article · Tue Oct 22 00:00:00 EDT 2019 · IEEE Transactions on Plasma Science

DOI:https://doi.org/10.1109/tps.2019.2946952· OSTI ID:1804042

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Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); OSTI
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

We present a Bayesian spectral fitting method developed for spectroscopic data analysis, particularly (but not solely) in the context of fusion energy research. The presented techniques are particularly valuable to estimating moments and corresponding uncertainties whenever the spectra result from line-integrated measurements in nonuniform plasmas, for which the approximation of atomic line shapes being ideal Gaussians gives poor estimates. We decompose multiple, potentially overlapping spectral lines into a sum of Gauss-Hermite polynomials, whose properties allow efficient truncation and uncertainty quantification, often with only three free parameters per atomic emission line. Tests with both synthetic and experimental data demonstrate the effectiveness and robustness where more standard nonlinear fitting routines may experience difficulties. A parallelized version of our implementation is publicly released under an open source license.

View Accepted Manuscript (DOE)

Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: SC0014264

OSTI ID:: 1804042

Journal Information:: IEEE Transactions on Plasma Science, Journal Name: IEEE Transactions on Plasma Science Journal Issue: 1 Vol. 48; ISSN 0093-3813

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Bayesian Spectral Moment Estimation and Uncertainty Quantification

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References (18)

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