Zihao Lin, Jef Heynen, Mick Wright, Thomas C. K. Ng, Chris Van Den Broeck, Justin Janquart

As the rate of gravitational wave detections continues to grow, the probability of observing a gravitationally lensed gravitational wave also increases. When the Schwarzschild radius of the lens is comparable in size to the gravitational wave wavelength, wave-optics effects induce a frequency- dependent modulation on the signal. CPU-based parameter estimation analyses for wave-optics lensing are computationally intensive, limiting the scale of studies to determine the significance of any potential lensing candidate. In this work, we implement a point-mass lens model in Jim, a high-performance GPU-based parameter estimation pipeline. This enables, for the first time, GPU-accelerated Bayesian inference in full-wave optics, including the computation of Bayes factors, without the need for training data. We call this framework Jim-lensing. Using this framework, we are able to speed up a full parameter estimation run under the point-mass lens hypothesis from more than 10 hours to the order of 10 minutes compared to the current state-of-the-art CPU-based pipeline. We show that our implementation is stable and obtains comparable results to this state-of- the-art pipeline. The substantial speed gain over existing CPU pipelines paves the way for analyzing gravitational wave data under a broader range of lensing models with greater capacity to produce robust frequentist significance estimates.