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USE CASE

Nuclear AI – Detection of nuclear materials in containers

Evaluation of muon tomography techniques for the detection of nuclear materials in containers

Muon tomography for the inspection of nuclear materials

GOAL

To develop a simulation software that replicates the interaction between muons and different materials and machine learning algorithms for computed tomography for the readings of muons.

TECHNOLOGIES

Machine Learning, Data analytics

ABOUT THE PROJECT

As validated AI Experts and service providers in the European Program STAIRWAI, Logicmelt has collaborated with the company Digafer in the development of an application to detect nuclear materials in containers through the use of muon detectors. The base of this application is that muons pass through almost any type of matter, but interact differently with certain materials. Studying their behavior, we can analyze what materials are inside a container without opening it.

Logicmelt has collaborated with Digafer to study how different geometries and materials interact with muons. To this end, we have developed a particle simulation software that replicates the readings of muon detectors in different scenarios. Using this software, we have trained machine learning algorithms to estimate the density of the materials within the containers, enabling the detection of potential dangerous materials like Uranium or Cesium.

This technology is a step forward in the development of an integral container inspection application for public ports that contributes to safer transport.

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