GPU-accelerated Bayesian inference for diffracted gravitational wave signals
Abstract
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.
Related
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BibTeX
@misc{lin2026gpuaccelerated,
title = {{GPU-accelerated Bayesian inference for diffracted gravitational wave signals}},
author = {Zihao Lin and Jef Heynen and Mick Wright and Thomas C. K. Ng and Chris Van Den Broeck and Justin Janquart},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.26619},
eprint = {2610.26619},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.26619},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - GPU-accelerated Bayesian inference for diffracted gravitational wave signals
AU - Zihao Lin
AU - Jef Heynen
AU - Mick Wright
AU - Thomas C. K. Ng
AU - Chris Van Den Broeck
AU - Justin Janquart
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.26619
UR - https://arxiv.org/abs/2610.26619
AB - 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.
ER -