Exploring the properties of nuclear matter in extreme environments.
Lead Research Organisation:
University of Glasgow
Department Name: School of Physics and Astronomy
Abstract
In 2015, Advanced LIGO and Virgo detected the first ever gravitational wave signal from inspiralling binary neutron stars. This breakthrough detection allowed us to explore the properties of dense nuclear matter within neutron stars. However, the underlying model describing nuclear matter at extremely high pressures and densities found within neutron stars is currently unknown though it is expected to be highly complex and non-linear.
We propose a project with LIGO-India collaborators to probe the properties of nuclear matter in extreme environments. This project will exploit world-leading, STFC-funded expertise on gravitational waves and machine learning at the University of Glasgow to develop a means of modelling the state of matter (equation of state) within neutron stars based on the gravitational wave signals observed as a result of binary neutron star inspiral and merger.
Novelty of the research methodology:
Machine learning approaches, in particular normalising flows, have been shown to be well-suited to exploring the parameter space of non-linear systems. Additionally, the use of machine learning will allow for rapid inference of the equation of state of neutron stars from observed gravitational wave signals. Rapid inference will be crucial for astronomers across the globe who will use this information to plan their searches for electromagnetic counterparts associated with the binary neutron star mergers, where time is of the essence for rapidly fading counterpart signals.
Alignment to Research Council's strategies and research areas:
STFC have funded the development of key elements of the Advanced LIGO and A+ projects. One of the key targets for Advanced LIGO and A+ are gravitational waves from binary neutron stars. The developed analysis should become a key component for the delivery of public alerts of gravitational wave transients observed by Advanced LIGO and the global gravitational wave network.
Any companies or collaborators involved:
We will be collaborating with members of LIGO-India for this project.
Additionally, The student and supervisor will be working within the LIGO Scientific Collaboration
We propose a project with LIGO-India collaborators to probe the properties of nuclear matter in extreme environments. This project will exploit world-leading, STFC-funded expertise on gravitational waves and machine learning at the University of Glasgow to develop a means of modelling the state of matter (equation of state) within neutron stars based on the gravitational wave signals observed as a result of binary neutron star inspiral and merger.
Novelty of the research methodology:
Machine learning approaches, in particular normalising flows, have been shown to be well-suited to exploring the parameter space of non-linear systems. Additionally, the use of machine learning will allow for rapid inference of the equation of state of neutron stars from observed gravitational wave signals. Rapid inference will be crucial for astronomers across the globe who will use this information to plan their searches for electromagnetic counterparts associated with the binary neutron star mergers, where time is of the essence for rapidly fading counterpart signals.
Alignment to Research Council's strategies and research areas:
STFC have funded the development of key elements of the Advanced LIGO and A+ projects. One of the key targets for Advanced LIGO and A+ are gravitational waves from binary neutron stars. The developed analysis should become a key component for the delivery of public alerts of gravitational wave transients observed by Advanced LIGO and the global gravitational wave network.
Any companies or collaborators involved:
We will be collaborating with members of LIGO-India for this project.
Additionally, The student and supervisor will be working within the LIGO Scientific Collaboration
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/W507477/1 | 01/10/2021 | 30/09/2025 | |||
| 2604882 | Studentship | ST/W507477/1 | 01/10/2021 | 31/03/2025 | Jessica Irwin |