{
  "abstract": "Nonparametric equation-of-state (EOS) inference evaluates the likelihood on a finite ensemble drawn from a prescribed functional prior. Reweighting that ensemble suffices for the binary neutron stars detected so far, and loses effective sample size only once the likelihood concentrates --- which needs third-generation catalogues, or extra electromagnetic and nuclear-theory information folded into the same target. We develop and validate an adaptive-proposal procedure that leaves the base prior fixed: data-informed generations are combined by multiple importance sampling and, where that degenerates at large catalogue size, by a calibrated Gaussian projection. We implement it in two representations --- a compressed principal-component parametrization of the neutron-star sequence, whose draws need no stellar-structure solve, and a sound-speed parametrization with a differentiable Tolman--Oppenheimer--Volkoff solver --- both on a constrained low-density-to-pQCD bridge. Because that anchor, and not the bridge, fixes the low-density prior, we rebuild the ensemble with a crust-plus-chiral-EFT anchor in place of the wide nuclear metamodel: the maximum-mass prior is unmoved, the radius gains a hard prior ceiling at the measured NICER value. GW170817 with the specified NICER and radio factors gives the calibration-conditional $R(1.4\\,M_\\odot)=11.7\\pm0.4$km; in a synthetic $1000$-event catalogue it reproduces the mean fixed-prior $Λ(1.4\\,M_\\odot)$ to $0.4$%, $1.5$ times wider, as the fixed archive's effective sample size falls from $\\sim\\!470$ to 22.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.07424",
  "author_total": 2,
  "authors": [
    {
      "name": "R. O'Shaughnessy"
    },
    {
      "name": "Z. Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/93t60989bh7pk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.07424",
  "fulltext_url": null,
  "id": "93t60989bh7pk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.07424",
  "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",
      "astro-ph.HE"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7221052142",
  "pdf_url": "https://arxiv.org/pdf/2610.07424",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/93t60989bh7pk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:22Z",
      "native_id": "2610.07424",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.07424"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Adaptive proposals for nonparametric equation-of-state inference with RIFT: iterative Gaussian-process refinement in a compressed sequence representation",
  "title_text": "Adaptive proposals for nonparametric equation-of-state inference with RIFT: iterative Gaussian-process refinement in a compressed sequence representation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:22Z",
  "url": "https://research.draconitic.com/paper/93t60989bh7pk",
  "venue": "arXiv",
  "versions": []
}