5 papers
Case studies with GPBilby of glitch-contaminated transient gravitational waves
Mattia Emma, Ann-Kristin Malz, Adriana Dias +1
In their fourth observing run, the LIGO--Virgo--KAGRA gravitational-wave observatories have found hundreds of new signals, but many are contaminated by non-Gaussian transient noise…
RNLE: Residual neural likelihood estimation and its application to gravitational-wave astronomy
Mattia Emma, Gregory Ashton
Simulation-based inference provides a powerful framework for Bayesian inference when the likelihood is analytically intractable or computationally prohibitive. By leveraging machin…
Reconstructing and resampling: a guide to utilising posterior samples from gravitational wave observations
Gregory Ashton
The LIGO, Virgo, and KAGRA (LVK) gravitational-wave observatories have opened new scientific research in astrophysics, fundamental physics, and cosmology. The collaborations that b…
Enhancing gravitational-wave detection: a machine learning pipeline combination approach with robust uncertainty quantification
Gregory Ashton, Ann-Kristin Malz, Nicolo Colombo
Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artefacts, and rare astrophysical signals. Multiple se…
Classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction
Ann-Kristin Malz, Gregory Ashton, Nicolo Colombo
With the increasing use of Machine Learning (ML) algorithms in scientific research comes the need for reliable uncertainty quantification. When taking a measurement it is not enoug…