Laboratory for Semiconductor Physics · Leuven Gravity Institute · KU Leuven · Leuven, Belgium
Listening to spacetime.
The Gravitational Wave Group is actively involved in the scientific exploitation of the current generation of gravitational-wave detectors — LIGO and Virgo — and in the preparation of next-generation observatories, particularly the Einstein Telescope.
- 7Members
- 4Research themes
Our research follows several interconnected lines. We develop advanced data-analysis techniques for gravitational-wave signals, including Bayesian parameter estimation, machine-learning methods for noise mitigation, and methods for signal detection. We work on robust approaches to realistic detector noise — transient glitches, and noise correlated across a network — and on supporting detector calibration for current and future observatories. A major effort is dedicated to the challenges and opportunities arising from the triangular configuration of the Einstein Telescope. We also investigate gravitational waves from core-collapse supernovae, using large-scale numerical simulations and modern data-analytics tools to explore the strong-field regime of general relativity.
We build our methods in the open: the software and datasets behind our results are released publicly, and our publication list is generated directly from our members’ bibliographic records.
The group sits within the Laboratory for Semiconductor Physics and is part of the Leuven Gravity Institute. It is one of several groups in the institute; this site covers our own work rather than the institute as a whole.
Research
-
Data analysis for current and future observatories
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.
Tjonnie G. F. Li, Isaac C. F. Wong, Arthur Offermans, Francesco Cireddu, Milan Wils
-
Detector noise, data quality, and calibration
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.
Tjonnie G. F. Li, Isaac C. F. Wong, Brecht Evens, Francesco Cireddu, Milan Wils, Tom Colemont
-
The Einstein Telescope
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.
Tjonnie G. F. Li, Isaac C. F. Wong, Francesco Cireddu, Milan Wils
Selected publications
-
Spingarn’s method and progressive decoupling beyond elicitable monotonicity
Computational Optimization and Applications (2026) journal
-
Modified Bryson-Frazier Smoothing and Hyperparameter Learning for Temporal Gaussian Process Regression
arXiv (Cornell University) (2026) preprint
-
The impact of physically motivated calibration errors on search pipeline detection parameters for broadband burst Signals
arXiv:2607.17439 preprint