Gravitational wave astrophysics with binary black holes
Lead Research Organisation:
University of Glasgow
Department Name: School of Physics and Astronomy
Abstract
In 2015, one hundred years after Einstein's General Theory of Relativity predicted the existence of gravitational waves, Advanced LIGO detected the first ever gravitational wave signal from binary black hole mergers.
While all binary black hole observations made since have been for black holes in stable binary systems, we also expect gravitational wave emissions from other sources such black hole encounters. Black holes moving through dense astrophysical environments such as globular clusters, can scatter off each other's gravitational potential or become captured in a bound orbits before merging.
Aims and objectives:
This project will explore binary black hole astrophysics through the analysis of the gravitational wave signals from individual black hole mergers as well as studying the overall properties of the binary black hole population observed by the Advanced LIGO detectors and its upgrade
Novelty of the research methodology:
The project will employ advanced Bayesian inference techniques such as hierarchical modelling and model selection. It will also explore the use of machine learning for detection and astrophysics.
While all binary black hole observations made since have been for black holes in stable binary systems, we also expect gravitational wave emissions from other sources such black hole encounters. Black holes moving through dense astrophysical environments such as globular clusters, can scatter off each other's gravitational potential or become captured in a bound orbits before merging.
Aims and objectives:
This project will explore binary black hole astrophysics through the analysis of the gravitational wave signals from individual black hole mergers as well as studying the overall properties of the binary black hole population observed by the Advanced LIGO detectors and its upgrade
Novelty of the research methodology:
The project will employ advanced Bayesian inference techniques such as hierarchical modelling and model selection. It will also explore the use of machine learning for detection and astrophysics.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/V506692/1 | 01/10/2020 | 30/09/2024 | |||
| 2446745 | Studentship | ST/V506692/1 | 01/10/2020 | 31/03/2024 | Leigh Smith |