1. Simulation-Informed Tests of Modified Gravity
Primary Supervisor: Dr Michalis Agathos
Waveform modelling in GR has been developed for decades to sufficiently high accuracy across all stages of binary coalescence. Recent advancements in theory and numerical simulations beyond GR (thanks to researchers at QMUL) are now allowing us to achieve similar accuracy and coverage for some alternative theories of gravity as well.
This project aims to leverage our new insights on GW phenomenology of alternative theories, to develop novel data analysis techniques that will search the observed data for signs of violation of GR. The PhD student will work on the interface between numerical relativity, data analysis and theory, towards developing these new tools.
The student will utilise data from simulations in alternative theories of gravity in tandem with theoretical calculations, to 1. understand strong-field GW effects beyond GR, 2. develop and optimise data analysis methods for detecting these effects, and 3. apply these methods to analyse real observed data from the current network of detectors, as well as simulataed data from next generation detectors.
Further information:
How to apply
Entry requirements
Fees and funding
2. Developing the Next-Generation GW Tests for Fundamental Physics
A diverse arsenal of methods has been developed and employed to test the agreement of GW data with GR and our standard model of cosmology, and to improve our current understanding of the nature of black holes and neutron star matter. As the sensitivity of our detector network improves, and with the advent of next-generation detectors (both ground-based and in space), we expect to detect much more, much louder and much longer signals, promising a wealth of new scientific discoveries.
However, these advances will present new challenges, as current models and data analysis methods will need to meet higher demands for both accuracy and computational efficiency, even with improved hardware. This project aims to develop the next generation of methods that will allow us to continue to perform reliable measurements with future GW observations, that are essential for advancing fundamental physics. The PhD student will work on the cutting edge of GW data analysis, towards developing these new tools.
The student will employ novel Bayesian inference and machine learning techniques to develop the next-generation GW methods for testing GR and inferring the properties of black holes and neutron stars. They will apply these new methods on real observed data from current detectors, and will test their performance on simulated data from next generation detectors.