Fast glitch modelling

Modelling gravitational-wave glitches with autoencoders.


Probabilistic modelling of glitches in gravitational-wave data.

Probabilistic noise estimation

Measuring the amount of non-Gaussian noise in the data.

Non-linear noise subtraction

Broadband noise modelling with dense neural networks.

Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

GW200129 is claimed to be the first-ever observation of the spin-disk orbital precession detected with gravitational waves (GWs) from an individual binary system. However, this claim warrants a cautious evaluation because the GW event coincided with …

Quasi-physical model for removing short glitches from LIGO and Virgo data

Gravitational-wave observatories become more sensitive with each observing run, increasing the number of detected gravitational-wave signals. A limiting factor in identifying these signals is the presence of transient non-Gaussian noise, which …

A sensitive test of non-Gaussianity in gravitational-wave detector data

Methods for parameter estimation of gravitational-wave data assume that detector noise is stationary and Gaussian. Real data deviates from these assumptions, which causes bias in the inferred parameters and incorrect estimates of the errors. We …