Kernel-Based Machine Learning for Background Estimation of NaI Low-Count Gamma-Ray Spectra

Virtually all gamma-ray spectrometry measurements contain background components due to the ubiquitous presence of primordial radionuclides in the Earth´s crust and cosmic radiation interactions high in the Earth´s atmosphere. In principle, spectral signatures due to radiation source(s) of actual interest can be extracted from the measured gamma-ray spectrum by background subtraction. However, if separate background measurements are unavailable or infeasible, and particularly for measurements exhibiting low signal-to-noise ratio (SNR), background subtraction is nontrivial, and it requires accurate background estimation . An example application of gamma-ray spectroscopy with low SNR is the “source search” scenario, where the position of a source is sought using measurements taken over very short time intervals by a detector in motion. We have developed an algorithm for background estimation in low-count gamma-ray spectra using kernel-based Gaussian processes (GP) taken from the field of machine learning. We have evaluated the performance of our algorithm using a group of three kernels tested against a dataset composed of background spectra measured in an urban environment using a mobile sodium iodide (NaI) detector. We have also simulated datasets containing nonbackground gamma-ray sources in an urban background measured with a NaI detector. The simulated scenarios employed a variety of source-detector distances and different types of source shielding. As a metric of algorithm performance, we calculated correlation coefficients, Theil inequality coefficients, and count difference statistics between estimated and actual backgrounds. We concluded that our method adequately estimates the gamma-ray background, but we also observed a strong dependence of the algorithm´s performance on the selected kernel.