8 citations · 12 across the 10 of their papers we have counts for
7 papers · 1 filter
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…