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Stellar-mass microlensing of gravitational waves
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Stellar-mass microlensing of gravitational waves
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When gravitational waves pass through the nuclear star clusters of galactic lenses, they may be microlensed by the stars. Such microlensing can cause potentially observable beating patterns on the waveform due to waveform superposition and magnify the signal. On the one hand, the beating patterns and magnification could lead to the first detection of a microlensed gravitational wave. On the other hand, microlensing introduces a systematic error in strong lensing use-cases, such as localization and cosmography studies. We show that diffraction effects are important when we consider GWs in the LIGO frequency band lensed by objects with masses $\lesssim 100 \, \rm M_\odot$. We also show that the galaxy hosting the microlenses changes the lensing configuration qualitatively, so we cannot treat the microlenses as isolated point mass lenses when strong lensing is involved. We find that for stellar lenses with masses $\sim 1 \, \rm M_\odot$, diffraction effects significantly suppress the microlensing magnification. Thus, our results suggest that gravitational waves lensed by typical galaxy or galaxy cluster lenses may offer a relatively clean environment to study the lens system, free of contamination by stellar lenses. We discuss potential implications for the strong lensing science case. More complicated microlensing configurations will require further study.
Forward citations
Cited by 7 Pith papers
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Authors synthesize SVD-based reduced-order models from wave-optics simulations to provide an effective stochastic description of stellar microlensing distortions on lensed gravitational waves.
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Fusing a simulation-trained common-source mass posterior with waveform features raises lensed-event detection efficiency from 20.8% to 35.2% at 1% false-positive rate and lowers the SNR for 50% efficiency from 45.3 to 33.5.
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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.
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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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Simulations of wave-optics lensing of GW150914-like signals by globular clusters recover injected velocity dispersion values for favorable alignments when lens and source parameters are jointly estimated.
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