{
  "abstract": "Bayesian inference plays a central role in the new field of gravitational-wave astronomy. However, traditional Bayesian inference with stochastic samplers is computationally expensive, taking hours to days per event. Transformative changes are therefore required to enable the science of next-generation observatories whose event rates and signal-to-noise ratios will increase significantly over the current generation. Recent work has shown that neural posterior estimation (NPE) is a promising path forward. A neural net is trained to approximate the posterior distribution of gravitational-wave parameters, allowing generation of posterior samples in a fraction of the time required by stochastic samplers. In this work, we introduce $\\texttt{BilbyFlow}$, which harnesses the power of NPE in the popular $\\texttt{Bilby}$ code suite. We use $\\texttt{BilbyFlow}$ to analyze a subset of 38 high-mass events from the third LIGO-Virgo-KAGRA Gravitational-Wave Transient Catalog (GWTC-3). For 29 events (76\\%), we obtained an importance-sampling efficiency $>$1%, allowing us to produce reliable posterior distributions within 3 min - 1.5 hours. For the other events, with importance-sampling efficiency $\\ll$1%, the run time can be as long as 35 hours. We achieve a median importance-sampling efficiency of 7%, which is roughly comparable to the $\\texttt{DINGO}$ package. We aim to significantly improve this efficiency with further development to make the runtime more reliably $O(\\text{min})$. $\\texttt{BilbyFlow}$ is open source and $\\texttt{pip}$-installable.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.00766",
  "author_total": 4,
  "authors": [
    {
      "name": "Liam Pinchbeck"
    },
    {
      "name": "Eric Thrane"
    },
    {
      "name": "Csaba Balazs"
    },
    {
      "name": "Paul Lasky"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3rkbgf9ry04qg.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.00766",
  "fulltext_url": null,
  "id": "3rkbgf9ry04qg",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.00766",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7206171147",
  "pdf_url": "https://arxiv.org/pdf/2609.00766",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3rkbgf9ry04qg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:00Z",
      "native_id": "2609.00766",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.00766"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "$\\texttt{BilbyFlow}$: user-friendly neural posterior estimation for gravitational-wave astronomy",
  "title_text": "BilbyFlow: user-friendly neural posterior estimation for gravitational-wave astronomy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:00Z",
  "url": "https://research.draconitic.com/paper/3rkbgf9ry04qg",
  "venue": "arXiv",
  "versions": []
}