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BAYESIAN SPECTRUM DECONVOLUTION INCLUDING UNCERTAINTIES AND MODEL SELECTION: APPLICATION TO X-RAY EMISSION DATA USING

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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.

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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.