An Improved Charged Particle Track Reconstruction Algorithm for the Micromegas Detectors of the New Small Wheel for the ATLAS Experiment at the Large Hadron Collider

The forthcoming High-Luminosity Large Hadron Collider upgrade brings potential for the discovery of new physics. However, the HL-LHC also brings new technological challenges as the Collider increases its luminosity beyond its intended value. As the number of collisions increases, so too does the hit rate in the ATLAS Experiment’s muon spectrometer. To tackle this challenge, the New Small Wheel is being commissioned to provide a dedicated trigger and precision tracker to the end-cap region of the ATLAS Experiment’s muon system. Two technologies: small-strip thin gap chambers (sTGCs) and micro-mesh gaseous structures (micromegas) provide these triggering and tracking capabilities, respectively. Before the micromegas are installed, they are tested at CERN using cosmic ray muons. This requires an efficient set of data analysis tools so that the detector’s performance can be evaluated. This thesis presents a set of algorithms which are used to determine that these detectors work and how well they work. A divide-and-conquer approach yields an efficient algorithm for locating tracks of charged particles in micromegas detector data. The parameters which describe each charged particle’s trajectory can then be fit to the data in linear time using an analytical solution to $\chi^{2}$ minimization. Analyzing the Hessian matrix of this $\chi^2$ function also allows for improved bounds to be placed on the micromegas detector’s precision. This removes poor-quality tracks from detector data, and it helps ensure that the detectors are satisfying the requirements set forth in the New Small Wheel’s Technical Design Report. A discussion of the track identification algorithm’s utility in disappearing-track-based searches for long-lived supersymmetric particles is also presented.