Bayesian Multiscale Deconvolution Applied to Gamma-ray Spectroscopy

Abstract

A common task in gamma-ray astronomy is to extract spectral information, such as model constraints and incident photon spectrum estimates, given the measured energy deposited in a detector and the detector response. This is the classic problem of spectral “deconvolution” or spectral inversion [2]. The methods of forward folding (i.e. parameter fitting) and maximum entropy “deconvolution” (i.e. estimating independent input photon rates for each individual energy bin) have been used successfully for gamma-ray solar flares (e.g. [5]). Nowak and Kolaczyk [4] have developed a fast, robust, technique using a Bayesian multiscale frame-work that addresses many problems with added algorithmic advantages. We briefly mention this new approach and demonstrate its use with time resolved solar flare gamma-ray spectroscopy.

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.