Material Identification with Cosmic Ray Muons using RPCs

TL;DR

This paper explores a simulation-based approach using RPCs and cosmic ray muons to identify materials through Coulomb scattering, employing three reconstruction algorithms to analyze imaging performance.

Contribution

It introduces a detailed simulation framework for material identification using RPCs and compares three reconstruction algorithms for effective imaging.

Findings

Successful simulation of material imaging with RPCs

Comparison of three reconstruction algorithms' effectiveness

Analysis of time efficiency for target discrimination

Abstract

We plan to build an imaging setup for material identification utilizing the Coulomb scattering of cosmic ray muons due to their interaction with the materials and tracking their trajectories with RPCs. To begin with, we consider a setup of six RPCs stacked in a parallel manner to read the position and timing information of the muons before and after their interaction with a phantom of a given material using a set of three RPCs for each phase. Here we present a simulation work carried out to study the image formation of the phantom of several materials using the present setup. A detailed modeling of the imaging system consisting of six RPCs was done using GEANT4. Cosmic Ray Library (CRY) was used for generation of particles with the appropriate distribution of energies and momenta. Three reconstruction algorithms were followed for material identification and image reconstruction, viz. Point of Closest Approach (POCA), Iterative POCA and the Binned Clustering Algorithm. A weighted metric discriminator was calculated for target object identification. Using the algorithms, the imaging of the area of interest lying between the two layers of RPCs was done. The time required to discriminate target objects and do the image reconstruction has been studied.