Institutional Sponsorship for Glasgow
Lead Research Organisation:
University of Glasgow
Department Name: School of Engineering
Abstract
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People |
ORCID iD |
Margaret Lucas (Principal Investigator) |
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 |