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Title: Proton spin structure from Monte Carlo global QCD analyses

Abstract

Although significant progress has been made in recent years in understanding the composition of the proton's spin from its quark and gluon constituents, a complete picture has yet to emerge. Such information is encoded in spin-dependent parton distribution functions (PDFs) that, as a consequence of being inherently nonperturbative, must be extracted through global QCD analyses of polarized lepton-nucleon and proton-proton collisions. Experiments that measure a final state hadron from these reactions are particularly useful for separating the individual quark and anti-quark polarizations, but require knowledge of parton-to-hadron fragmentation functions (FFs) to describe theoretically.In this thesis, we present a new approach to global QCD analyses, that were performed recently by the Jefferson Lab Angular Momentum (JAM) Collaboration to determine the spin PDFs and FFs from deep inelastic scattering (DIS), semi-inclusive DIS, and single inclusive electron-positron annihilation observables. While previous global QCD studies typically used a single !2 minimization procedure, the JAM Collaboration applies a robust Monte Carlo fitting methodology to extract the central values and uncertainties of the relevant distributions. The results from these JAM global QCD analyses, which include a first ever simultaneous fit of the spin PDFs and FFs, resolve a long-standing puzzle regarding the strange quark polarization andmore » provide new information about the proton spin structure.« less

Authors:
Publication Date:
Research Org.:
Thomas Jefferson National Accelerator Facility, Newport News, VA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP) (SC-26)
OSTI Identifier:
1468927
Report Number(s):
JLAB-THY-18-2800; DOE/OR/23177-4531
DOE Contract Number:  
AC05-06OR23177
Resource Type:
Thesis/Dissertation
Country of Publication:
United States
Language:
English

Citation Formats

None, None. Proton spin structure from Monte Carlo global QCD analyses. United States: N. p., 2018. Web. doi:10.2172/1468927.
None, None. Proton spin structure from Monte Carlo global QCD analyses. United States. doi:10.2172/1468927.
None, None. Tue . "Proton spin structure from Monte Carlo global QCD analyses". United States. doi:10.2172/1468927. https://www.osti.gov/servlets/purl/1468927.
@article{osti_1468927,
title = {Proton spin structure from Monte Carlo global QCD analyses},
author = {None, None},
abstractNote = {Although significant progress has been made in recent years in understanding the composition of the proton's spin from its quark and gluon constituents, a complete picture has yet to emerge. Such information is encoded in spin-dependent parton distribution functions (PDFs) that, as a consequence of being inherently nonperturbative, must be extracted through global QCD analyses of polarized lepton-nucleon and proton-proton collisions. Experiments that measure a final state hadron from these reactions are particularly useful for separating the individual quark and anti-quark polarizations, but require knowledge of parton-to-hadron fragmentation functions (FFs) to describe theoretically.In this thesis, we present a new approach to global QCD analyses, that were performed recently by the Jefferson Lab Angular Momentum (JAM) Collaboration to determine the spin PDFs and FFs from deep inelastic scattering (DIS), semi-inclusive DIS, and single inclusive electron-positron annihilation observables. While previous global QCD studies typically used a single !2 minimization procedure, the JAM Collaboration applies a robust Monte Carlo fitting methodology to extract the central values and uncertainties of the relevant distributions. The results from these JAM global QCD analyses, which include a first ever simultaneous fit of the spin PDFs and FFs, resolve a long-standing puzzle regarding the strange quark polarization and provide new information about the proton spin structure.},
doi = {10.2172/1468927},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {5}
}

Thesis/Dissertation:
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