Muon Portal project: Tracks reconstruction, automated object recognition and visualization techniques for muon tomography data analysis

Abstract

The present Ph.D. thesis is contextualized within the Muon Portal project, a project dedicated to the creation of a tomograph for the control and scanning of containers at the border in order to reveal smuggled fissile material by means of the cosmic muons scattering. This work aims to extend and consolidate the research in the field of muon tomography in the context of applied physics. The main purpose of the thesis is to investigate new techniques for reconstruction of muon tracks within the detector and new approaches to the analysis of data from muon tomography for the automatic objects recognition and the 3D visualization, thus making possi- ble the realization of a tomography of the entire container. The research work was divided into different phases, described in this thesis document: from a prelimi- nary speculative study of the state of the art on the tracking issue and on the tracks reconstruction algorithms, to the study on the Muon Portal detector performance in the case of particle tracking at low and high multiplicity. A substantial part of the work was devoted to the study of different image reconstruction techniques based on the POCA algorithm (Point of Closest Approach) and the iterative EM-LM algorithm (Expectation-Maximization). In addition, more advanced methods for the tracks reconstruction and visualization, such as data-mining techniques and clustering algorithms have been the subject of the research and development ac- tivity which has culminated in the development of an unsupervised multiphase clustering algorithm (modified-Friends-of-Friends) for the muon tomography data analysis.