Muon Scattering Density Map for High-Z Detection
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Solution Overview
Problem
Current detection systems for high atomic number substances, such as radioactive and nuclear materials, face challenges in sensitivity and accuracy due to the separate analysis of X-ray and muon scattering data, which can be overcome by combining these data sources to enhance detection capabilities.
Innovation Solution
A method that integrates X-ray imaging and muon detection systems to reconstruct muon tracks and calculate scattering density maps, using statistical models to combine X-ray and muon data for improved detection of high-Z materials, allowing for a more sensitive and accurate identification of shielded nuclear materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate analysis of X-ray and muon scattering data is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent combines X-ray imaging data and muon scattering data into a unified detection system. The X-ray detector and muon scattering detector operate simultaneously on the same shipping container, with their respective data sets integrated through a threat detection algorithm that processes both data types together to identify high-Z materials with enhanced sensitivity.
Solution Approach 2:
The integrated detection system performs multiple detection functions simultaneously: X-ray imaging for dense material detection, muon scattering for high-Z material identification, and combined analysis for improved threat detection. This multi-functional approach allows a single system to address multiple detection needs without requiring separate independent systems.
2Measurement precision
If combined X-ray and muon data analysis is implemented, then detection sensitivity is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing of both X-ray and muon data simultaneously as they are collected, rather than waiting for complete data sets. The threat detection algorithm begins analyzing patterns and correlations between the two data types during the scanning process itself, reducing the need for extensive post-processing and accelerating overall detection.
3Reliability
If integrated detection system is used, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The integrated detection system is divided into distinct functional modules: an X-ray imaging subsystem, a muon scattering detection subsystem, and a central processing subsystem. Each module operates semi-independently with well-defined interfaces, allowing for easier integration and maintenance while achieving improved detection accuracy through coordinated operation of all subsystems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This integrated approach significantly enhances the sensitivity and accuracy of detecting high-Z materials by correlating X-ray and muon scattering data, improving the ability to identify shielded nuclear materials and contraband in shipping containers.
Implementation Method 1
scanning the volume with an X-ray imaging system to provide X-ray imaging data
Implementation Method 2
A muon scattering detector can be used to reconstruct a three-dimensional image of a volume, for example based on deflections and scattering angles of the muons as measured by planar detectors located above and below the shipping container
Implementation Method 3
Large Scale Gas Electron Multiplier and Detection Method
Data Source
AI summary
Methods of detecting high atomic weight materials in a volume such as a truck or cargo container are disclosed. The volume is scanned with an X-ray imaging system and a muon detection system. Using the output data of the muon detection system, the exit momentum and incoming and outgoing tracks of each muon are reconstructed. A muon scattering statistical model is calculated using the muon exit momentum and the incoming and outgoing tracks of the muon. A most likely scattering density map is determined according to the muon-scattering statistical model and an X-ray statistical model. A visual representation of the most likely scattering density map is displayed.