Bayesian inference
Turning noisy detector data into astrophysics: Bayesian inference, model selection, transdimensional inference, and neural posterior estimation.
Research
I work on a number of topics under the broad heading of gravitational astrophysics. Below are the themes I’m actively researching, followed by earlier topics I have published in. Please get in touch if you’re interested in details about any of them.
01Themes
Turning noisy detector data into astrophysics: Bayesian inference, model selection, transdimensional inference, and neural posterior estimation.
What gravitational-wave catalogues reveal about how binary black holes form, including distinct subpopulations at different masses.
Post-merger signals, equations of state, magnetars behind short gamma-ray bursts, and continuous waves from spinning neutron stars.
The permanent deformation of spacetime left behind by a passing wave, and how LIGO–Virgo–KAGRA can detect it.
Probing Einstein’s theory in the strong-field regime, from tests of the no-hair theorem to extra polarisations and modified gravity.
Nanohertz gravitational waves from supermassive black-hole binaries, and phase-coherent maps of the gravitational-wave sky.
02Earlier work
Other topics I have published in over the years.