Advanced signal processing and machine learning techniques for muon orbital hit localization in the ATLAS experiment at CERN

Abstract

This research focuses on enhancing muon hit localization in the Micromegas detectors of the New Small Wheel (NSW), a critical upgrade to the ATLAS Muon Spectrometer at CERN. By integrating advanced signal processing techniques and machine learning algorithms, the study addresses key challenges such as radiation damage, pile-up effects, and radiative energy losses that affect precision measurements in high-luminosity environments. The research develops signal processing methods to suppress noise and correct signal distortions while applying state-of-the-art machine learning models to high-fidelity Monte Carlo simulations of muon interactions. A comparative analysis of multiple simulation datasets across different transverse momenta evaluates the robustness of these approaches. Additionally, statistical classification methods are utilized to differentiate signal from background events, thereby improving data quality and minimizing systematic uncertainties. By categorizing muon traces affected by radiation-induced energy loss into signal (cluster signal) and background (cluster background), the study further refines localization accuracy. The findings contribute to optimizing muon detection in ATLAS, supporting more precise particle trajectory reconstruction for high-energy physics experiments.