Data fusion methods for improving resolution from a gamma-ray scintillator network

Abstract

Abstract Enhancements in gamma-ray spectral resolution have been realized through hardware development, including low-defect scintillation materials, solid-state detector technology, and low-noise read-out electronics. Enhanced resolution has also been provided by deconvoluting hardware response through the application of spectral unfolding techniques. However, fusing data from multiple independent measurements, both before and after spectral unfolding, to obtain more information than exists from a single measurement represents a largely unexplored path. This work provides a statistical foundation from which data acquired from an arbitrary number of independent gamma-ray spectra can be fused to yield higher resolution than the constituent measurements yield in isolation. Specifically, three data fusion techniques are considered: (i) pulse-height spectrum fusion, (ii) unfolded fluence linear opinion pool, and (iii) unfolded fluence logarithmic opinion pool. We explore the qualitative characteristics of these methods. We then apply them to spectra of thallium-doped sodium iodide [NaI(Tl)] and bismuth germanate (BGO), where all spectra are provided by MCNP simulations. Our results demonstrate improved resolution, thus establishing the methods as an inexpensive alternative to hardware upgrades. Moreover, our results demonstrate that these results have the potential to expand the capability of cutting-edge technology to provide currently unachievable resolution.