{
  "abstract": "Future gravitational-wave observatories such as the Einstein Telescope will detect tens of thousands of binary neutron star mergers per year, making gravitational waves a dominant probe of the neutron-star equation of state in the coming decades. However, extracting this information requires hierarchical inference across a rapidly growing catalog of events, and existing methods must restart from scratch whenever a new event arrives, making them impractical at this scale. In this Letter, we introduce a hybrid sequential Monte Carlo algorithm that combines data and likelihood tempering to adaptively update the equation-of-state posterior in batches, reusing previous inference results instead of restarting from a wide prior. Accelerated by GPU hardware, our method infers the equation of state from a simulated month of around 1500 binary neutron star mergers in a few hours on a single GPU, and we project that a full year of detections could be processed in a few days on current hardware. This establishes sequential Monte Carlo as a practical route for real-time hierarchical inference with future gravitational-wave detectors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.07975",
  "author_total": 7,
  "authors": [
    {
      "name": "Thibeau Wouters"
    },
    {
      "name": "Thomas C. K. Ng"
    },
    {
      "name": "Hauke Koehn"
    },
    {
      "name": "Fabian Gittins"
    },
    {
      "name": "Peter T. H. Pang"
    },
    {
      "name": "Tim Dietrich"
    },
    {
      "name": "Chris Van Den Broeck"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/53rxjwr2c6fd6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.07975",
  "fulltext_url": null,
  "id": "53rxjwr2c6fd6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.07975",
  "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.HE"
    ],
    [
      "arxiv",
      "gr-qc"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7220948370",
  "pdf_url": "https://arxiv.org/pdf/2610.07975",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/53rxjwr2c6fd6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:22Z",
      "native_id": "2610.07975",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.07975"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "Scalable and sequential inference of the neutron star equation of state with the Einstein Telescope",
  "title_text": "Scalable and sequential inference of the neutron star equation of state with the Einstein Telescope",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:22Z",
  "url": "https://research.draconitic.com/paper/53rxjwr2c6fd6",
  "venue": "arXiv",
  "versions": []
}