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

Spectrum deconvolution is an important task in ionizing radiation measurements, as the pulse height spectra, or, in general, the measured data from spectrometers or other measuring instruments are usually determined by the convolution of the response function with the fluence spectra. The method presented here for obtaining fluence spectra from the measurements is an application of Bayesian parameter estimation to the deconvolution of X-ray emission data. The problem of choosing the optimal model among several possible models is also considered, as well as an approach to include contributions from various sources of uncertainty, both correlated and uncorrelated. The application is carried out using the Bayesian software WinBUGS.