8 citations · 12 across the 10 of their papers we have counts for
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Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction
Ann-Kristin Malz, Gregory Ashton, Nicolo Colombo
In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes. How…
A story about a tipsy kangaroo: Reversible jump MCMC for model selection in the analysis of gravitational-wave signals from the coalescence of compact objects
Anna Puecher, Tim Dietrich, Hauke Koehn +4
Bayesian inference is commonly employed in the analysis of gravitational-wave signals not only to estimate the source parameters, but also for model selection. The latter provides…
Combining gravitational wave search pipelines to find subthreshold signals in GWTC-5.0
Ann-Kristin Malz, Samuel Russell, Gregory Ashton +1
The detection of transient gravitational wave signals relies on independent search algorithms that analyse detector data and assign significance measures to candidate events. Howev…
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…
Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model
Gregory Ashton, Colm Talbot, Andrew Lundgren +2
Ground-based gravitational wave detectors are now routinely surveying the dark Universe, finding hundreds of collisions between compact objects. However, terrestrial non-Gaussian n…
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…