Lawrence Berkeley National Laboratory

Active neutron interrogation techniques that integrate Gamma-Ray and Neutron Spectroscopy (GRNS) with Associated Particle Imaging (API) offer a highly promising approach for achieving three-dimensional (3D) bulk geochemical analysis of planetary shallow surfaces. While API-GRNS using prompt γ -rays has been explored previously, the complementary use of backscattered fast neutrons within the API framework remains comparatively underexplored. Most prior fast neutron API demonstrations are based on forward scattering measurements in a transmission geometry, whereas planetary surface deployments require a backscatter geometry in which neutrons are measured after being scattered from the target. This study presents a proof-of-concept laboratory demonstration of API-based backscattered fast neutron imaging for material discrimination, with emphasis on distinguishing hydrogen-rich from hydrogen-deficient targets relevant to planetary exploration. Experiments were conducted using a D–T neutron generator-based API system at Lawrence Berkeley National Laboratory (LBNL), employing 6Li-activated CLYC (Cs2LiYCl6:Ce) and EJ-309 scintillators for coincident detection of alpha particles and fast neutrons. The system successfully imaged contrasting materials with high and low mass number 〈 A 〉 based on their neutron scattering characteristics, investigating the feasibility of backscattered fast neutron imaging as a complementary diagnostic to API-based γ -ray spectroscopy and as a method to distinguish hydrogen-bearing materials. Monte Carlo simulations using GEANT4 provide insight into the observed image contrast, highlighting the roles of elastic and inelastic scattering kinematics, macroscopic cross sections, and detector timing resolution. While quantitative hydrogen retrieval and depth sensitivity analyses are beyond the present scope, this work establishes a foundation for future studies aimed at extending API-based fast neutron imaging toward hydrated minerals and water, depth profiling, and integration with other GRNS sensors for planetary shallow subsurface exploration.