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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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.
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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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.