Parameter estimation with the current generation of phenomenological waveform models applied to the black hole mergers of GWTC-1
Reviewed by Pithpith:TELACHDTopen to challenge →
read the original abstract
We consider the ten confidently detected gravitational-wave signals in the GWTC-1 catalog which are consistent with mergers of binary black hole systems, and perform a thorough parameter estimation re-analysis. This is made possible by using computationally efficient waveform models of the current (fourth) generation of the IMRPhenom family of phenomenological waveform models, which consists of the IMRPhenomX frequency-domain modelsand the IMRPhenomT time-domain models. The analysis is performed with both precessing and non-precessing waveform models with and without subdominant spherical harmonic modes. Results for all events are validated with convergence tests, discussing in particular the events GW170729 and GW151226. For the latter and the other two lowest-mass events, we also compare results between two independent sampling codes, Bilby and LALInference. We find overall consistent results with the original GWTC-1 results, with all Jensen-Shannon divergences between the previous results using IMRPhenomPv2 and our default IMRPhenomXPHM posteriors below 0.045 bits, but we also discuss cases where including subdominant harmonics and/or precession influences the posteriors.
This paper has not been read by Pith yet.
Forward citations
Cited by 2 Pith papers
-
Polarization Analysis of Ringdown Signals
Constrained polarization model for Kerr ringdown modes enables inclination inference from two-detector data for non-precessing mergers but introduces biases when applied to precessing systems.
-
Parameter inference of millilensed gravitational waves using neural spline flows
Neural spline flows perform fast posterior inference on 11-dimensional millilensed GW parameters with accuracy comparable to dynesty for most quantities and a 3-day to 0.8-second speedup.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.