8 citations · 12 across the 10 of their papers we have counts for
11 papers
Simulation-based inference of profile evolution for high-precision pulsar timing
Sai C. Susarla, Gregory Ashton, A. Parthasarathy +2
Pulsars are exceptional cosmic laboratories, the most stable of which rival atomic clocks. Nearly all of that precision rests on one observable, the average pulse profile unique to…
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…
Maximum Entropy Spectral Analysis: an application to gravitational waves data analysis
Alessandro Martini, Stefano Schmidt, Gregory Ashton +1
The Maximum Entropy Spectral Analysis (MESA) method, developed by Burg, offers a powerful tool for spectral estimation of a time-series. It relies on Jaynes' maximum entropy princi…