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
Faculty
Dr. Renee Fatemi
Faculty
Dr. Anatoly Dymarsky
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
