Author ORCID Identifier
https://orcid.org/0000-0001-5384-4256
Date Available
12-20-2024
Year of Publication
2024
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Arts and Sciences
Department/School/Program
Physics and Astronomy
Advisor
Dr. Renee Fatemi
Abstract
Hadronization, the process by which colored quarks and gluons shower from high energy collisions and recombine to form stable, experimentally-observable particles, is a fundamental aspect of Quantum Chromodynamics (QCD) that is not yet fully understood. Fragmentation functions, typically measured in electron-positron collisions, encapsulate this hadronization process well for quarks. Studying proton-proton collisions offers direct access to gluon fragmentation that other channels like electron-positron do not. Recent theoretical developments have proposed the study of hadronic showers in groupings called jets, introducing the concept of multi-dimensional jet fragmentation functions. This thesis presents the extraction of collinear and transverse momentum-dependent fragmentation functions for charged pions reconstructed in jets produced in sqrt(s) = 200 GeV proton-proton collisions at STAR. These multi-dimensional jet fragmentation functions must be “unfolded” to correct for detector effects. Limitations of traditional unfolding methods motivate the application of OmniFold, a novel machine-learning based algorithm that allows for the simultaneous, unbinned unfolding of multiple variables for a single observable. This unfolding with OmniFold provides a promising proof-of-concept for further implementation of multidimensional machine-learning based unfolding in a wide variety of collider physics analyses.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2024.513
Funding Information
This research was supported in part by the National Science Foundation Grants "Using Muons and Protons to Probe the Structure of the Universe" (no. 2110293 from 2021-2024 and no. 1812417 from 2018-2022) and "Spin as a Probe into the Structure of the Universe" (no. 1504099 in 2019).
Recommended Citation
Harrison-Smith, Hannah A., "The application of novel machine learning algorithms to study multi-dimensional fragmentation functions of hadrons in jets at STAR" (2024). Theses and Dissertations--Physics and Astronomy. 131.
https://uknowledge.uky.edu/physastron_etds/131
Included in
Elementary Particles and Fields and String Theory Commons, Numerical Analysis and Scientific Computing Commons
