Division of Electronics, Detectors and Computing for Physics (DEDIP)

This PhD thesis focuses on developing an advanced analysis framework for inspecting nuclear waste containers using muon tomography, particularly the scattering method. Muon tomography, which leverages naturally occurring muons from cosmic rays to scan dense structures, has proven valuable in areas where traditional imaging methods fail. CEA/Irfu, with expertise in muon detectors, seeks to harness AI and Machine Learning (ML) to optimize muon data analysis, particularly to reduce long exposure times and improve image reliability.

The project will involve familiarizing with muography (muon tomography image) principles, simulating muon interactions with waste containers, and developing ML-based data augmentation and image processing techniques. The outcome should yield efficient tools to interpret muon images, enhance analysis speed, and classify container contents reliably. The thesis aims to improve nuclear waste inspection’s safety and reliability by producing cleaner, faster, and more interpretable muon tomography data through innovative analysis methods.