{
  "abstract": "Transdimensional Bayesian inference is becoming a key ingredient in gravitational-wave astronomy. In many relevant applications, the number of components needed to describe the data is not known a priori and must be inferred jointly with their parameters. We present a transdimensional Sequential Monte Carlo (SMC) framework designed to exploit massive parallelism. The method evolves a population of particles through a sequence of tempered distributions and uses the No-U-Turn Sampler for the exploration of fixed-dimensional parameter space while reversible-jump birth and death moves allow the number of components to vary. The same SMC construction also enables posterior samples obtained from a shorter data segment to be updated as additional data become available, enhancing the overall efficiency of the analysis. We validate the approach on two proof-of-concept problems: the recovery of a sequence of Gaussian pulses and a simplified LISA Galactic-binary inference problem. In both cases, the method recovers the injected number of components and produces consistent posterior estimates. These results indicate that the proposed method is a promising avenue toward scalable and parallel Bayesian transdimensional inference for current and future gravitational-wave astronomy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.17674",
  "author_total": 2,
  "authors": [
    {
      "name": "Gabriele Demasi"
    },
    {
      "name": "Walter Del Pozzo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/28xk3nc80mxcw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.17674",
  "fulltext_url": null,
  "id": "28xk3nc80mxcw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.17674",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.CO"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "gr-qc"
    ],
    [
      "openalex",
      "2613|Markov Chains and Monte Carlo Methods"
    ]
  ],
  "openalex": "W7213512161",
  "pdf_url": "https://arxiv.org/pdf/2609.17674",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/28xk3nc80mxcw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "2609.17674",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.17674"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "Massively parallel transdimensional sampling with data assimilation: an application to LISA galactic binaries",
  "title_text": "Massively parallel transdimensional sampling with data assimilation: an application to LISA galactic binaries",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/28xk3nc80mxcw",
  "venue": "arXiv",
  "versions": []
}