Researchers have utilized Monte Carlo simulations to characterize the performance of a neutron-based sensor, named SABAT (Submersible Autonomous Body for Aquatic Threat detection), designed to detect hazardous substances in underwater environments. This sensor is integrated into an underwater drone, enabling autonomous operation in exploring areas such as ports, waterways, and coastal zones to identify chemical, radiological, biological, nuclear, and explosive (CBRNE) threats.

The study focused on evaluating SABAT's capability to detect the presence of specific materials, such as explosives and chemical agents, through the interaction of neutrons with the atomic nuclei of these compounds. Monte Carlo simulations, a computational technique that uses random sampling to obtain numerical results, allowed for precise modeling of neutron and photon transport through water and target materials, providing crucial data on the system's sensitivity and range.

The simulation results detail the detector's effectiveness under various conditions and configurations, optimizing the neutron source and radiation detectors to maximize the probability of identifying hazardous substances. This characterization is fundamental for the safe development and deployment of SABAT technology, ensuring it can operate effectively and reliably in detecting underwater threats, minimizing risks to human personnel and the environment.