Precision QCD and effective field theories with machine learning
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Authors
Iranipour, Shayan
Advisors
Date
2022-04-01Awarding Institution
University of Cambridge
Type
Thesis
Metadata
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Iranipour, S. (2022). Precision QCD and effective field theories with machine learning (Doctoral thesis). https://doi.org/10.17863/CAM.84461
Abstract
The Standard Model (SM) serves as one the best descriptions of fundamental physics
we have and the quest for its falsification has led to it being tested to an unprecedented
degree. Despite its flawless performance, there are many theoretical and phenomenological
indications that the SM cannot be a complete description of nature; though, so
far, no direct evidence for new physics at the TeV scale has been gathered at colliders.
Far from being discouraging, the precision level reached by current experiments gives
us the unique opportunity to investigate the effects of new particles whose masses are
far above the TeV scale, but still produce observable effects at the scales within the
direct kinematical reach of the Large Hadron Collider (LHC). Unlike for direct searches,
which are limited by the energy reach of the collider, indirect searches are limited only
by the theoretical and experimental control over the processes under inspection.
A robust understanding of Quantum Chromodynamics (QCD) is crucial in order to
achieve precision theoretical predictions in the era of initial state hadron colliders such
as the LHC. An important ingredient therein are the parton distribution functions
(PDFs) which parameterize the proton structure in terms of its elementary quark and
gluon constituents. These quantities are non-perturbative and obtained from data
using a global QCD analysis. In tandem, Effective Field Theories (EFTs), provide a
convenient framework to capture the indirect effects of possible BSM resonances in
low energy observables. Constraints on the EFT then translate to constraints on the
nature of BSM physics.
This manuscript serves to marry these two endeavours. We present machine
learning-based approaches to PDF determination and specifically highlight how deep
learning algorithms form ideal candidates to parameterize the PDFs in an unbiased
fashion. We present the NNPDF4.0 PDF set which serves as the latest and most precise
determination of proton structure delivered by such a methodology. We show how a precise determination of the PDFs has important consequences on LHC phenomenology
by presenting a precision determination of the strange content of the proton and a
number of key phenomenological applications.
We then discuss the interplay between EFT dynamics and the PDFs; analysing
the extent to which the fit of PDFs may absorb possible BSM signals and assess the
implications a consistent treatment of PDFs in EFT fits has on phenomenological
studies. For this, we use legacy deep inelastic scattering data from HERA and later
some more modern measurements from high-mass Drell-Yan observables at the Large
Hadron Collider (LHC) to investigate the back-reaction of EFT dynamics on the PDFs.
The considerations presented in the above study then act as an impetus to develop a
methodology that is capable of simultaneously determining proton structure alongside
BSM dynamics in a consistent framework. We present a novel methodology, SIMUnet,
which delivers a robust and accurate determination of PDFs and general theory
parameters, of which BSM dynamics are a subset. We show how this state-of-the-art
methodology can, for the first time, extract and disentangle the PDFs from BSM
dynamics from a global dataset paving the way for a truly global and simultaneous
interpretation of indirect searches in the context of precision physics.
Keywords
QCD, Parton Distribution Functions, Effective Field Theories
Sponsorship
Royal Society (RGF/EA/180148)
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.84461
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