Funding provider: UK Research and Innovation (UKRI)
Subject areas: Physics
Project start date: 1 October 2023 (Enrolment open from mid-September)
Aligned programme of study: PhD in Physics
Mode of study: Full-time only
Project supervisors:
- Professor Chris Allton
- Professor Gert Aarts
- Professor Tim Burns
Project description:
Artificial Intelligence, Machine Learning and Advance Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
Research theme: T1 - data from large science facilities
The strong interaction is the fundamental force that holds together nuclear matter. This force is described by Quantum Chromodynamics, a non-Abelian gauge theory whose fundamental particles are six fermion flavours (the quarks) interacting with eight-gauge bosons (the gluons). Quarks and gluons never appear in final states of the strong interaction. This phenomenon, for which we lack an explanation, is known as colour confinement. Elegant mechanisms of colour confinement have been proposed that are based on the prominent role of field configurations with non-trivial topological properties. However, the underlying arguments are semiclassical, and would hence need to be supported by first-principal calculations such as those performed in the framework of Lattice Field Theories. The latter in turn would need to be guided by topological arguments and optimisation procedures and would benefit from advanced visualisation methods that enable us to inspect the relevant configurations. The goal of this highly interdisciplinary project is to develop a methodology for detecting configurations with non-trivial topological properties combining large-scale numerical simulations, optimisation procedures, methods in topological data analysis and visualisation, in order to assess their relevance in mechanisms of colour confinement. Lattice QCD provides a first-principles approach to relate the basic degrees of freedom in the theory of the strong interactions (QCD), namely quarks and gluons, to observables probed in elementary particle and heavy-ion collider experiments. This includes spectral quantities (hadrons, transport) as well as thermodynamic quantities (pressure, entropy, susceptibilities, order parameters). In recent years, machine learning (ML) has increased in popularity also in the lattice QCD community [1], resulting in the exploration of many ML methods to analyse large data sets, both for regression and classification tasks. In this project, we will explore these methods to data obtained in the context of QCD at nonzero temperature and the FASTSUM collaboration.
References:
[1] Applications of Machine Learning to Lattice Quantum Field Theory, D.Boyda, G.Aarts et al, contribution to SNOWMASS 2022, https://arxiv.org/abs/2202.05838
More information can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing (AIMLAC) website.
Please quote the project code (e.g. RS191 - AIMLAC1) for queries and within the application.
Eligibility
Applicants for PhD must normally hold an undergraduate degree at 2.1 level or a master’s degree with a minimum overall grade at ‘Merit’ (or Non-UK equivalent as defined by Swansea University).
English Language requirements: If applicable – IELTS 6.5 Overall (with no individual component below 6.0) or Swansea University recognised equivalent.
This scholarship is open to candidates of any nationality.