Adaptive Modeling Pulsed Neutron Burst Shape for Acquiring Net Inelastic Gamma Spectra

Abstract

Compact pulsed neutron generators emit fast neutrons that interact with the medium by inelastic scattering, producing characteristic gamma rays that reflect the concentrations of important elements, such as carbon and oxygen. The detection of these elements is crucial for applications such as detecting explosives, organic carbon, and density. In actual measurement, a proportion of the capture gamma rays is subtracted from the gamma rays collected during the neutron burst to obtain a net inelastic gamma spectrum. However, the different shapes of pulsed neutron burst due to hardware limitations can affect the subtraction factors of capture gamma rays. To address this challenge, an adaptive method for acquiring the net inelastic gamma energy spectra based on pulsed neutron burst shape modeling is proposed. The distribution function of capture gamma ray over time in the pulse period is derived based on modeling of the pulsed neutron burst shape and convolution. The fall and stable point on the pulsed neutron burst shape are adaptively identified through gradients and autocorrelation coefficients. Finally, a more accurate net inelastic gamma spectrum is obtained by calculating the subtraction factors during the burst based on the fit capture gamma time spectra. The different pulsed neutron burst shapes and environmental parameters are considered, and the adaptability and accuracy of our proposed method are verified through the Monte Carlo simulation.