Conveners
Applications in Muon Tomography
- Pablo Martinez Ruiz Del Arbol (Universidad de Cantabria and CSIC (ES))
- Tommaso Dorigo (Universita e INFN, Padova (IT))
Applications in Muon Tomography
- Maxime Lagrange (CP3 Universite Catholique de Louvain)
The recent MODE whitepaper*, proposes an end-to-end differential pipeline for the optimisation of detector designs directly with respect to the end goal of the experiment, rather than intermediate proxy targets. The TomOpt python package is the first concrete endeavour in attempting to realise such a pipeline, and aims to allow the optimisation of detectors for the purpose of muon tomography...
Muon Scattering Tomography (MST) has been through a fruitful period of development which led to many applications in various fields such as border controls, nuclear waste characterization, nuclear reactor monitoring and preventive maintenance of industrial facilities. Whatever the use case, MST detector conception aims at reaching the best performance that respects specific constraints,...
Muon tomography applications often require detection of material contrast. One example of such application is the detection of contraband at the border. Another example is the detection of steel rebars inside reinforced concrete blocks. Sensitivity of material discrimination depends on the detector configuration, exposure time and clutter. We explore how machine learning techniques can be used...
We investigate the problem of distinguishing materials by scattering tomography with an R implementation of the EM (Expectation Maximization) algorithm and we compare the results with those from the PoCA (Point of Closest Approach) method.
Several applications of scattering muon tomography require the estimation of a limited number of key parameters associated to a given sample. In this presentation we explore the use of the quantiles of the angular and spatial deviation distributions as the input to Deep Neural Networks regressing on the parameters of interest. We provide examples related to the measurement of the position of...
This presentation explores the possibility of using Generative Adversarial Neural Networks (GANN) in order to simulate the propagation of muons through material without using a complete simulation of the physical processes. In order to achieve this goal, Generative Adversarial Neural Networks have been used to simulate muon tomography data applied to the measurement of the thicknes of isolated...
