Data di Pubblicazione:
2014
Citazione:
UNBIASED SPIN-DEPENDENT PARTON DISTRIBUTION FUNCTIONS / E.r. Nocera ; tutor: S. Forte ; co-tutor: J. Rojo ; supervisore: R.D. Ball. DIPARTIMENTO DI FISICA, 2014 Feb 28. 26. ciclo, Anno Accademico 2013. [10.13130/nocera-emanuele-roberto_phd2014-02-28].
Abstract:
We present the first unbiased determination of
spin-dependent, or polarized, parton distribution functions
of the proton. A statistically sound representation of the
corresponding uncertainties is achieved by means of the NNPDF
methodology, formerly developed for unpolarized distributions and
generalized to the polarized here for the first time.
The features of the procedure, based on robust statistical tools -
Monte Carlo sampling for error propagation,
neural networks for PDF parametrization, genetic algorithm
for their minimization and possibly reweighting for including new data
samples without refitting - and their implementation are illustrated
in detail.
Sets of polarized parton distributions are obtained
at next-to-leding order accuracy in perturbative quantum chromodynamics,
based on both fixed-target inclusive deeply-inelastic scattering
and the most recent polarized hadron collider data.
A quantitative appraisal on the potential role of future measurements
at an Electron-Ion Collider is also presented.
We study the stability of our results upon the variation of several
theoretical and methodological assumptions
and we present a detailed investigation of
the first moments of our polarized parton distributions,
compared to other recent analyses.
We find that the uncertainty on the gluon distribution
from available data was substantially underestimated in
previous determinations; in particular, we emphasize that a large
contribution to the gluon may arise from the unmeasured small-x region,
against the common belief that this is actually rather small.
We demonstrate that an Electron-Ion Collider would provide evidence
of a possible large gluon contribution to the nucleon spin, though with
a sizable residual uncertainty.
Tipologia IRIS:
Tesi di dottorato
Keywords:
Spin ; Parton Distribution Functions (PDF) ; Neural Networks ; NNPDF ; High-energy Physics
Elenco autori:
E.R. Nocera
Link alla scheda completa:
Link al Full Text: