3 papers
gr-qc2025
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…
gr-qc2025
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…
gr-qc2024
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…