Institutional Sponsorship for Glasgow

Lead Research Organisation: University of Glasgow
Department Name: School of Engineering

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Publications

10 25 50
 
Title Normalising flow for rapid neutron star inference 
Description Normalising flow is a form of machine learning which maps data to a normally distributed latent space. Here, the flow is used to map the observed properties of the neutron stars during a binary neutron star merger to an equation of state for nuclear matter under these extreme conditions. The advantage of using a normalising flow is that it is very rapid and can be used to inform further astronomical observations of the neutron star merger system. 
Type Of Material Data analysis technique 
Year Produced 2022 
Provided To Others? No  
Impact The technique was developed in collaboration with staff from the Saha Institute in India and has allowed us to build collaborative links with the institute. We expect the technique and associated results to be published in 2023. 
 
Description Collaboration with Saha Institute, India 
Organisation Saha institute of nuclear physics
Country India 
Sector Academic/University 
PI Contribution This project was performed in collaboration with a research partner from the Saha Institute in India and has allowed us to build collaborative links with the institute.
Collaborator Contribution Our partner at the Saha Institute contributed expertise on the neutron star equation of state physics which was critical to the project.
Impact We developed a machine learning technique for rapid inference of neutron star equation of state through the observation of binary neutron star mergers in gravitational waves.
Start Year 2019