4 citations · 7 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
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…
Joint inference for gravitational wave signals and glitches using a data-informed glitch model
Ann-Kristin Malz, John Veitch
Gravitational wave data are often contaminated by non-Gaussian noise transients, glitches, which can bias the inference of astrophysical signal parameters. Traditional approaches e…
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…