Path-Planning System for Radioisotope Identification Devices Using 4π Gamma Imaging Based on Random Forest Analysis.

Area of Science:

• Nuclear instrumentation
• Radiation detection and measurement
• Computational physics

Background:

• Accurate identification of radioactive sources is crucial for security and environmental monitoring.
• Current methods may require numerous measurements, increasing time and complexity.
• Optimizing measurement strategies can significantly improve efficiency in radiation source identification.

Purpose of the Study:

• To develop an intelligent path-planning system for radiation source identification devices.
• To enhance the efficiency of radioactive source localization and activity estimation.
• To reduce the number of required measurement positions for accurate source identification.

Main Methods:

• Utilized 4π gamma imaging for data acquisition.
• Employed an integrated simulation model for source location and activity estimation.
• Developed a prediction model using random forest analysis to estimate identification probability.
• Verified the path-planning system through integrated simulation and experimental measurements of a Cesium-137 (¹³⁷Cs) point source.

Main Results:

• The path-planning system successfully guided measurements to identify ¹³⁷Cs point sources.
• Fewer measurement positions were required compared to traditional methods.
• The system demonstrated high accuracy in source localization and activity estimation.
• The random forest prediction model effectively guided the selection of optimal measurement locations.

Conclusions:

• The developed path-planning system significantly enhances the efficiency of radiation source identification.
• Intelligent path planning based on predictive modeling is effective for optimizing gamma imaging measurements.
• The system offers a promising solution for rapid and accurate detection of radioactive materials in various scenarios.