QCD under extreme conditions: heating up quarks and gluons
Lead Research Organisation:
Swansea University
Department Name: College of Science
Abstract
At low temperature, hadrons are the building blocks of nature. As the temperature increases, hadrons dissolve and quarks and gluons become the relevant degrees of freedom. In this project properties of the new state of matter are studied, using analytical and numerical techniques.
People |
ORCID iD |
Gert Aarts (Primary Supervisor) | |
Samuel Offler (Student) |
Publications
Offler Sam
(2019)
News from bottomonium spectral functions in thermal QCD
in arXiv e-prints
Offler S
(2020)
News from bottomonium spectral functions in thermal QCD
Offler S.
(2022)
Reconstruction of bottomonium spectral functions in thermal QCD using Kernel Ridge Regression
in Proceedings of Science
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/N504464/1 | 30/09/2015 | 30/03/2021 | |||
1950145 | Studentship | ST/N504464/1 | 30/09/2017 | 31/12/2021 | Samuel Offler |
ST/R505158/1 | 30/09/2017 | 31/12/2021 | |||
1950145 | Studentship | ST/R505158/1 | 30/09/2017 | 31/12/2021 | Samuel Offler |
ST/P006779/1 | 30/09/2017 | 29/09/2024 | |||
1950145 | Studentship | ST/P006779/1 | 30/09/2017 | 31/12/2021 | Samuel Offler |
Description | This work resulted in the production of a working machine learning model that could be used to support or oppose a preexisting technique, the maximum entropy method (MEM). Work was done to investigate how to generate suitable training data and hyperparameter tuning for the machine learning model as well in order to improve it. Whilst this was applied to the FASTSUM gen2L data for comparison with other methods and could produce results close to what was expected, I feel the reliability of the model is insufficient for practical use at this stage. |
Exploitation Route | The model created has potential but would need further work to make it a reliable method. Further improvements to the training data or changing how this data is generated is one potential way to continuing this work. Alternatively changing the kernel function used or perhaps investigating whether it is possible to use a more complex regression model than a single kernel function. Finally, the model was only tested on a subset of FASTSUM gen2L data. A more reliable model could be used on the entire dataset. |
Sectors | Education Other |
Description | STFC CDT on Data-Intensive Science |
Organisation | Cardiff University |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Studentship in this CDT |
Collaborator Contribution | Training and placement |
Impact | Training, skills development, placement at industrial partner |
Start Year | 2017 |
Description | STFC CDT on Data-Intensive Science |
Organisation | University of Bristol |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Studentship in this CDT |
Collaborator Contribution | Training and placement |
Impact | Training, skills development, placement at industrial partner |
Start Year | 2017 |
Description | STFC CDT on Data-Intensive Science |
Organisation | We Predict Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | Studentship in this CDT |
Collaborator Contribution | Training and placement |
Impact | Training, skills development, placement at industrial partner |
Start Year | 2017 |