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Gravelamps: Gravitational Wave Lensing Mass Profile Model Selection
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Gravelamps: Gravitational Wave Lensing Mass Profile Model Selection
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We present the package Gravelamps which is designed to analyse lensed gravitational wave signals in order to constrain the mass density profile of the lensing object. Gravelamps does this via parameter estimation using the framework of bilby, which enables estimation of both the lens and the source parameters. The package can be used to study both microlensing and macrolensing cases -- where the lensing mass distribution is described by a point mass and extended mass density profile respectively -- and allows the user to easily and freely switch between the full wave optics and approximate geometric optics description. The performance of Gravelamps is demonstrated via simulated analysis of both mircolensing and macrolensing events, illustrating its capability for both parameter estimation and model selection in the wave optics and hybrid environments. To further demonstrate the utility of the package, the real gravitational-wave event GW170809 was analysed using Gravelamps; this event was found to yield no strong evidence supporting the lensing hypothesis, consistent with previously published results.
Forward citations
Cited by 4 Pith papers
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The diffraction-lensing interpretation of GW231123 with astrophysical priors
GW231123 is better fit by a lower-mass merger diffracted by an isolated ~1000 M_sun lens, but astrophysical priors and a frequentist rate estimate make this lensing interpretation unlikely.
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Across the Universe: GW231123 as a magnified and diffracted black hole merger
GW231123's extreme mass and spins may be explained by a point-mass microlens embedded in a galaxy, reducing the inferred source mass to about 100-180 solar masses.
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Discovering gravitational waveform distortions from lensing: A deep dive into GW231123
GW231123's apparent gravitational-lensing signal has a false-alarm probability around 4σ, so the event cannot be claimed as lensed under the two-image wave-optics model.
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Accelerated inference of microlensed gravitational waves with machine learning
A neural posterior estimator trained on wave-optics-microlensed gravitational-wave signals recovers source and lens parameters and Bayes factors consistent with Bilby, about 10 times faster.
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