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BAYESIAN SPECTRUM DECONVOLUTION INCLUDING UNCERTAINTIES AND MODEL SELECTION: APPLICATION TO X-RAY EMISSION DATA USING
1Physikalisch-Technische Bundesanstalt, Bundesallee 100, Braunschweig, Germany.
Radiation Protection Dosimetry
|January 10, 2019
Summary
This study presents a Bayesian approach for spectrum deconvolution in X-ray measurements. The method accurately estimates fluence spectra from measured data, accounting for model selection and uncertainties.
Area of Science:
- Nuclear physics
- Spectroscopy
- Computational science
Background:
- Spectrum deconvolution is crucial for ionizing radiation measurements.
- Measured spectra result from the convolution of response functions and fluence spectra.
Purpose of the Study:
- To apply Bayesian parameter estimation for deconvolution of X-ray emission data.
- To develop a method for obtaining fluence spectra from measurements.
- To address model selection and uncertainty quantification.
Main Methods:
- Bayesian parameter estimation applied to deconvolution.
- Consideration of optimal model selection among alternatives.
- Inclusion of correlated and uncorrelated uncertainty sources.
- Utilized the Bayesian software WinBUGS for application.
Main Results:
- Successfully obtained fluence spectra from X-ray emission data.
- Demonstrated a robust method for spectrum deconvolution.
- Provided a framework for incorporating complex uncertainties.
Conclusions:
- The Bayesian deconvolution method is effective for X-ray spectral analysis.
- The approach handles model uncertainty and various error sources.
- This method enhances the accuracy of fluence spectra determination.
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