Searcharxiv⌕ Search

arXiv subjects

Raphaël Bajou

Publications and source records attributed to Raphaël Bajou.

2 recordsLinked to original sources

Full Characterization of a Mock Nuclear Waste Barrel with Muon Tomography using Micromegas Detectors

Muon tomography based on multiple Coulomb scattering provides a non-destructive method to image dense and shielded objects using naturally occurring cosmic-ray muons. In the context of nuclear waste characterization, we present the experimental imaging of a 205-L mock waste barrel using a dedicated 1m$^2$ muon scattering tomography test bench. The system employs multiplexed resistive Micromegas detectors, enabling stable and high-precision muon tracking. Monte Carlo simulations are first used to characterize material-dependent scattering signatures and to quantitatively assess identification performance using statistical reconstruction. These simulation-based results are then used to define objective discrimination thresholds, which are subsequently applied to experimental data for the localization and identification of internal anomalies. Using an Angle Statistics Reconstruction algorithm, we achieve a spatial resolution of 10 mm and demonstrate the three-dimensional imaging of an internal structure containing both low- and high-radiation length materials. Material discrimination performance is evaluated using receiver operating characteristic analysis, yielding high identification efficiency for dense metallic inclusions such as lead and steel (AUC $\geq$ 0.96) within acquisition times of a few days, while cavities also exhibit strong contrast. Experimental results show good agreement with detailed Monte Carlo simulations. By establishing a continuous workflow from simulation-based performance characterization to practical application on measured data, this work provides a quantitatively validated framework for muon scattering tomography applied to complex, shielded objects.

physics.ins-det↗

A new versatile method for the reconstruction of scintillator-based muon telescope events

This paper presents a new method to process the data recorded with muon telescopes. We have developed this processing method for the plastic scintillator-based hodoscopes located around the volcano La Soufrière de Guadeloupe, in the French Lesser Antilles, in order to perform muon radiographies of the lava dome region, strongly impacted by the volcanic hydrothermal activity. Our method relies on particle trajectory reconstruction, performing a fit of the recorded hits in the impacted scintillator bars using a Random Sample Consensus algorithm. This algorithm is specifically built to discriminate outlier points, usually due to noise hits, in the data. Thus, it is expected to significantly improve the signal/noise separation in muon track hits and to obtain higher quality estimates of the particles' incident trajectories in our detectors. The first analysis of the RANSAC-reconstructed events offers promising results in terms of average density maps. To illustrate the performances of this algorithm, we provide angular resolution and reconstruction efficiency estimates using a GEANT4 simulation of a telescope equipped with four detection matrices. In addition, we also show preliminary results from open-sky data recorded with such telescope at La Soufrière de Guadeloupe volcano.

physics.geo-ph↗