A study of thermal NRQCD with machine learning methods - SAMUEL OFFLER (2022)
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.