Gravitational waves (GWs) can be distorted by intervening mass distributions while propagating, leading to frequency-dependent modulations that imprint a distinct signature on the observed waveforms. Bayesian inference for GW lensing with conventional sampling methods is costly, and the problem is exacerbated by the rapidly growing GW catalog. Moreover, assessing the statistical significance of lensed candidates requires thousands, if not millions, of simulations to estimate the background from noise fluctuations and waveform systematics, which is infeasible with standard samplers. We present a novel method, dingo-lensing, for performing inference on lensed GWs, extending the neural posterior estimation framework dingo. By comparing our results with those using conventional samplers, we show that the compute time of parameter estimation of lensed GWs can be reduced from weeks to seconds, while preserving accuracy both in the posterior distributions and the evidence ratios. We train our neural networks with LIGO detector noise at design sensitivity and a lens model that accommodates two overlapping images with a constant $\pi /2$ phase shift. We show that the lensing parameters are recovered with millisecond precision for the time delays. We also demonstrate that our network can identify signals diffracted by point masses, highlighting its flexibility for searches. By simulating thousands of lensed and nonlensed events, we determine how the detectability changes with different source properties. dingo-lensing provides a scalable and efficient avenue for identifying and characterizing gravitationally lensed GW events in the upcoming observing runs.

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• Gravitational lenses
• Gravitational wave detection
• Gravitational waves
• Deep learning