Online Archive of University of Virginia Scholarship
Angular Dependence of Drell-Yan in the SeaQuest Experiment Using Deep Neural Network-Based Reconstruction212 views
Author
Conover, Arthur, Physics - Graduate School of Arts and Sciences, University of Virginia
Advisors
Keller, Dustin, AS-Physics (PHYS), University of Virginia
Abstract
The SeaQuest and SpinQuest experiments at Fermilab were designed to probe the internal dynamics of protons and neutrons via the angular dependence of muons created by colliding 120 GeV protons into stationary targets. To do this, it is necessary to translate the detector data into coherent information about the particles detected in the experiment. This dissertation describes the development and implementation of QTracker, a neural network-based algorithm designed to reconstruct muons.
The application of QTracker to experimental data yields improved reconstruction of muon tracks, allowing for a more precise investigation of the transverse momentum distributions and angular modulations in the Drell-Yan process. This work highlights the potential of neural network-based methods to advance particle tracking and enhance our understanding of nucleon structure.
Conover, Arthur. Angular Dependence of Drell-Yan in the SeaQuest Experiment Using Deep Neural Network-Based Reconstruction. University of Virginia, Physics - Graduate School of Arts and Sciences, PHD (Doctor of Philosophy), 2024-07-30, https://doi.org/10.18130/52mt-p086.
