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A study of thermal NRQCD with machine learning methods / SAMUEL OFFLER

Swansea University Author: SAMUEL OFFLER

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DOI (Published version): 10.23889/SUthesis.60375

Abstract

The aim of this thesis is to develop a machine learning model capable of the spectral reconstruction of Euclidean lattice correlators at finite temperature. The early part of this thesis is dedicated to a review of the QCD phase diagram and correlation functions to establish the relationship between...

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Published: Swansea 2022
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Aarts, Gert ; Allton, Chris
URI: https://cronfa.swan.ac.uk/Record/cronfa60375
first_indexed 2022-07-04T11:33:36Z
last_indexed 2023-01-13T19:20:27Z
id cronfa60375
recordtype RisThesis
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spelling 2022-07-04T12:47:42.0046428 v2 60375 2022-07-04 A study of thermal NRQCD with machine learning methods 4090e5af8227d7ba7285b53f97fac445 SAMUEL OFFLER SAMUEL OFFLER true false 2022-07-04 The aim of this thesis is to develop a machine learning model capable of the spectral reconstruction of Euclidean lattice correlators at finite temperature. The early part of this thesis is dedicated to a review of the QCD phase diagram and correlation functions to establish the relationship between the Euclidean correlator and spectral function. An analysis of FASTSUM ensembles of Euclidean correlators is performed to determine effective masses and thermal modification for bottomonium states. An initial model using Kernel Ridge Regression is examined and implemented for the Υ state. The latter part of this thesis focuses on improving the generation of training data for the machine learning method and the machine learning method itself. This work concludes with the implementation of the Kernel Ridge Regression for a variety of bottomonium states. E-Thesis Swansea Lattice QCD, Machine Learning 28 6 2022 2022-06-28 10.23889/SUthesis.60375 ORCiD identifier: https://orcid.org/0000-0001-8052-8013 COLLEGE NANME COLLEGE CODE Swansea University Aarts, Gert ; Allton, Chris Doctoral Ph.D STFC; Grant number: 1950145 2022-07-04T12:47:42.0046428 2022-07-04T12:29:57.0373191 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Physics SAMUEL OFFLER 1 60375__24438__fce156cd9ab64deda84c008deb0b9a20.pdf Offler_Samuel_P_PhD_Thesis_Final_Redacted_Signature.pdf 2022-07-04T12:38:11.6675898 Output 7484678 application/pdf E-Thesis – open access true Copyright: The author, Samuel P. Offler, 2022. true eng
title A study of thermal NRQCD with machine learning methods
spellingShingle A study of thermal NRQCD with machine learning methods
SAMUEL OFFLER
title_short A study of thermal NRQCD with machine learning methods
title_full A study of thermal NRQCD with machine learning methods
title_fullStr A study of thermal NRQCD with machine learning methods
title_full_unstemmed A study of thermal NRQCD with machine learning methods
title_sort A study of thermal NRQCD with machine learning methods
author_id_str_mv 4090e5af8227d7ba7285b53f97fac445
author_id_fullname_str_mv 4090e5af8227d7ba7285b53f97fac445_***_SAMUEL OFFLER
author SAMUEL OFFLER
author2 SAMUEL OFFLER
format E-Thesis
publishDate 2022
institution Swansea University
doi_str_mv 10.23889/SUthesis.60375
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Physics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Physics
document_store_str 1
active_str 0
description The aim of this thesis is to develop a machine learning model capable of the spectral reconstruction of Euclidean lattice correlators at finite temperature. The early part of this thesis is dedicated to a review of the QCD phase diagram and correlation functions to establish the relationship between the Euclidean correlator and spectral function. An analysis of FASTSUM ensembles of Euclidean correlators is performed to determine effective masses and thermal modification for bottomonium states. An initial model using Kernel Ridge Regression is examined and implemented for the Υ state. The latter part of this thesis focuses on improving the generation of training data for the machine learning method and the machine learning method itself. This work concludes with the implementation of the Kernel Ridge Regression for a variety of bottomonium states.
published_date 2022-06-28T08:12:22Z
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score 11.080252

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