Research

The group is actively involved in the scientific exploitation of the current generation of gravitational-wave detectors — LIGO and Virgo — and in preparing the analyses for the observatories that follow. Work here spans Bayesian parameter estimation, methods for signal detection, and the machine-learning techniques that increasingly support both.

Real detector output is not stationary Gaussian noise. The group develops robust approaches to transient glitches and to noise correlated across a detector network, together with machine-learning methods for noise mitigation and techniques supporting the calibration of current and future observatories.

A major effort is dedicated to the specific challenges and opportunities that arise from the triangular configuration of the Einstein Telescope, and more broadly to the science case and data challenges of next-generation detectors.

The group investigates gravitational waves from core-collapse supernovae, using large-scale numerical simulations and modern data-analytics tools to explore the strong-field dynamics of spacetime and the behaviour of ultra-dense matter.