CTD2022: Application of machine learning in muon scattering tomography for better image reconstruction

Muon Scattering Tomography (MST) is a non-destructive imaging technique that uses cosmic ray muon to probe three-dimensional objects. It is based on the multiple Coulomb scattering suffered by the muons while crossing an object. Muons deflect and decelerated depending upon the density and the atomic number of the material of the object. Therefore, by studying the deflection of the muons, the information about the test object may be obtained. We plan to construct an MST setup using two sets of Resistive Plate Chambers (RPCs) for tracking muons before and after their interaction to identify the material of the test object. The RPC is preferred due to its simple design, ease of construction, cost-effective production of large detection area, along with very good temporal, spatial resolutions, and detection efficiency. NINO ASICs have been used as a discriminator and its Time Over Threshold (TOT) property has been used to achieve better position information. A Field Programmable Gate Array (FPGA) based multi-parameter Data Acquisition System (DAQ) has been developed in this context for the collection of position information from the tracking RPCs and subsequent track reconstruction. It offers a simple, low-cost, scalable readout solution for the present MST setup. In parallel, a numerical simulation has been carried out for optimizing the design and performance of the MST setup for material identification using the Geant4 package. Two sets of RPCs, each consisting of three RPCs detectors, have been placed above and below the test object. Cosmic Ray Library (CRY) has been used as a muon generator and the GEANT4 package with FTFP_BERT physics list has been implemented for simulating the muon interaction in the MST setup. Two track reconstruction algorithms, namely, the Point of Closest Approach (PoCA) and Binned Cluster Algorithm (BCA), have been used to compare their effectiveness in MST. These algorithms have been used to determine scattering vertices and scattering angles for each muon. In this project, we try to develop a material identification method using machine learning algorithms for better material identification and implement this in our MST setup. The performance of the technique may be compared to a few other methods, such as the Metric Discriminator method, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Pattern Recognition Method (PRM), etc.