69 citations · 217 across the 10 of their papers we have counts for
10 papers
Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms
Michael Pürrer, Ashwin Girish, Lucy M. Thomas +2
We present a neural network surrogate model that emulates the NRSur7dq4 gravitational waveform model for precessing binary black hole mergers. The surrogate decomposes the waveform…
labrador: A domain-optimized machine-learning tool for gravitational wave inference
Javier Roulet, Marco Crisostomi, Lucy M. Thomas +1
Fast and reliable inference of gravitational-wave source parameters is crucial for analyzing large catalogs that are reaching the size of hundreds of detections, and for identifyin…
Revisiting the Coprecessing Frame in the Presence of Orbital Eccentricity
Lucy M. Thomas, Katerina Chatziioannou, Sam Johar +2
Accurate inclusion of both spin precession and orbital eccentricity effects in gravitational waveform models represents a key hurdle in our ability to fully characterize the proper…
Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants
Lucy M. Thomas, Katerina Chatziioannou, Vijay Varma +1
Surrogate models of numerical relativity simulations of merging black holes provide the most accurate tools for gravitational-wave data analysis. Neural network-based surrogates pr…
Strong-field scattering of two spinning black holes: Numerical Relativity versus post-Minkowskian gravity
Piero Rettegno, Geraint Pratten, Lucy Thomas +2
Highly accurate models of the gravitational-wave signal from coalescing compact binaries are built by completing analytical computations of the binary dynamics with non-perturbativ…
Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks
Lucy M. Thomas, Geraint Pratten, Patricia Schmidt
Gravitational waves from the coalescences of black hole and neutron stars afford us the unique opportunity to determine the sources' properties, such as their masses and spins, wit…