Development of a RPC system with detailed performance simulation

TL;DR

This paper presents the development and simulation of RPC detectors for neutrino experiments, improving performance modeling and demonstrating practical detector assembly and testing.

Contribution

It introduces a new simulation framework for RPC performance, integrating advanced space charge models and parallel computing for faster, more accurate results.

Findings

Enhanced RPC simulation accuracy and speed.

Successful prototype development and testing of RPC stacks.

Potential for improved neutrino detection capabilities.

Abstract

Neutrinos, elusive and abundant, interact weakly with matter, making detection challenging. This thesis explores neutrino physics, focusing on mass hierarchy and oscillations, with emphasis on India's upcoming INO facility. The Iron Calorimeter (ICAL) detector, using Resistive Plate Chambers (RPCs) within magnetized iron plates, aims to resolve neutrino mass hierarchy and precisely measure $\theta_{23}$ and $\Delta m^2_{32}$, potentially revealing new physics. The study advances RPC development through simulation techniques. A straight-line model calculates space charge effects during avalanche or streamer processes, improving electric field estimation speed and accuracy. Polarization effects in dielectric electrodes are analyzed, elucidating electric field dependence on electrode parameters. A new class, pAvalancheMC, integrated into Garfield++ software, combines straight-line and image charge models to simulate space charge effects. Using OpenMP for parallel computing, it achieves significant speed-ups in electron-ion pair generation and space charge field calculations. Practically, an eight-RPC stack was developed at VECC to measure cosmic muon scattering angles in high-density materials. A testbench evaluates efficiency, crosstalk, and hit uniformity. A reconstruction algorithm, validated with data from the IICHEP stack in Madurai, supports the VECC stack's potential. This work enhances neutrino physics understanding and advances RPC-based detection technologies.