{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.26619",
  "author_total": 6,
  "authors": [
    {
      "name": "Zihao Lin"
    },
    {
      "name": "Jef Heynen"
    },
    {
      "name": "Mick Wright"
    },
    {
      "name": "Thomas C. K. Ng"
    },
    {
      "name": "Chris Van Den Broeck"
    },
    {
      "name": "Justin Janquart"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cjwgn56mzv8hn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.26619",
  "fulltext_url": null,
  "id": "cjwgn56mzv8hn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.26619",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "gr-qc"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7223009007",
  "pdf_url": "https://arxiv.org/pdf/2610.26619",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cjwgn56mzv8hn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2610.26619",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.26619"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "GPU-accelerated Bayesian inference for diffracted gravitational wave signals",
  "title_text": "GPU-accelerated Bayesian inference for diffracted gravitational wave signals",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/cjwgn56mzv8hn",
  "venue": "arXiv",
  "versions": []
}