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.

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