4 citations · 4 across the 2 of their papers we have counts for
2 papers
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★ 4 cited
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…