activity
20192025
most citedThe SXS Collaboration's third catalog of binary black hole simulations

53 citations · 141 across the 6 of their papers we have counts for

collaborators

6 papers

gr-qc2025★ 53 cited

The SXS Collaboration's third catalog of binary black hole simulations

Mark A. Scheel, Michael Boyle, Keefe Mitman +56

We present a major update to the Simulating eXtreme Spacetimes (SXS) Collaboration's catalog of binary black hole simulations. Using highly efficient spectral methods implemented i…

gr-qc2024★ 10 cited

Simulating binary black hole mergers using discontinuous Galerkin methods

Geoffrey Lovelace, Kyle C. Nelli, Nils Deppe +28

Binary black holes are the most abundant source of gravitational-wave observations. Gravitational-wave observatories in the next decade will require tremendous increases in the acc…

gr-qc2021★ 16 cited

Simulating magnetized neutron stars with discontinuous Galerkin methods

Nils Deppe, François Hébert, Lawrence E. Kidder +33

Discontinuous Galerkin methods are popular because they can achieve high order where the solution is smooth, because they can capture shocks while needing only nearest-neighbor com…

astro-ph.HE2021★ 35 cited

Correlations in gravitational-wave reconstructions from eccentric binaries: A case study with GW151226 and GW170608

Eamonn O'Shea, Prayush Kumar

The eccentricity of binary black-hole mergers is predicted to be an indicator of the history of their formation. In particular, eccentricity is a strong signature of dynamical form…

gr-qc2020★ 26 cited

Observation of eccentric binary black hole mergers with second and third generation gravitational wave detector networks

Zhuo Chen, E. A. Huerta, Joseph Adamo +4

[Abridged] We introduce an improved version of the Eccentric, Non-spinning, Inspiral-Gaussian-process Merger Approximant (ENIGMA) waveform model. We find that this ready-to-use mod…

gr-qc2019★ 1 cited

Statistically-informed deep learning for gravitational wave parameter estimation

Hongyu Shen, E. A. Huerta, Eamonn O'Shea +2

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, , and three astrophysical properties of the post-merger compact…