Bayesian inference on LVK O1-O3 events with eccentric aligned-spin waveforms yields log10 Bayes factors of 1.77-4.75 favoring eccentricity for GW200129, GW190701 and GW200208_22, and >99.5% probability that at least one of 57 events is eccentric under an astrophysically motivated rate prior.
Numerical Relativity Injection Infrastructure
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abstract
This document describes the new Numerical Relativity (NR) injection infrastructure in the LIGO Algorithms Library (LAL), which henceforth allows for the usage of NR waveforms as a discrete waveform approximant in LAL. With this new interface, NR waveforms provided in the described format can directly be used as simulated GW signals ("injections") for data analyses, which include parameter estimation, searches, hardware injections etc. As opposed to the previous infrastructure, this new interface natively handles sub-dominant modes and waveforms from numerical simulations of precessing binary black holes, making them directly accessible to LIGO analyses. To correctly handle precessing simulations, the new NR injection infrastructure internally transforms the NR data into the coordinate frame convention used in LAL.
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Neural network surrogate approximates precessing compact binary gravitational waveforms up to 1000x faster than the base EOB model with validated accuracy.
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citing papers explorer
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Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA
Bayesian inference on LVK O1-O3 events with eccentric aligned-spin waveforms yields log10 Bayes factors of 1.77-4.75 favoring eccentricity for GW200129, GW190701 and GW200208_22, and >99.5% probability that at least one of 57 events is eccentric under an astrophysically motivated rate prior.
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Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries
Neural network surrogate approximates precessing compact binary gravitational waveforms up to 1000x faster than the base EOB model with validated accuracy.
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Computationally efficient models for the dominant and sub-dominant harmonic modes of precessing binary black holes
IMRPhenomXPHM is a new computationally efficient phenomenological model for precessing binary black hole gravitational-wave signals that incorporates higher-order modes via twisting-up maps from non-precessing waveforms.