SciPost Submission Page
Parallel Nested Slice Sampling for Gravitational Wave Parameter Estimation
by David Yallup, Metha Prathaban, James Alvey, Will Handley
Submission summary
| Authors (as registered SciPost users): | David Yallup |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2509.24949v1 (pdf) |
| Date submitted: | Sept. 30, 2025, 9:36 a.m. |
| Submitted by: | David Yallup |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
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| Academic field: | Physics |
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| Approach: | Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
Gemini 2.5 used in final review and drafting stage of manuscript
Abstract
Inferring parameters and testing hypotheses from gravitational wave signals is a computationally intensive task central to modern astrophysics. Nested sampling, a Bayesian inference technique, has become an established standard for this in the field. However, most common implementations lack the ability to fully utilize modern hardware acceleration. In this work, we demonstrate that when nested sampling is reformulated in a natively vectorized form and run on modern GPU hardware, we can perform inference in a fraction of the time of legacy nested sampling implementations whilst preserving the accuracy and robustness of the method. This scalable, GPU-accelerated approach significantly advances nested sampling for future large-scale gravitational-wave analyses.