{
  "abstract": "This paper addresses the important problem of estimating the noise spectral density of next-generation gravitational-wave detectors, such as LISA and the Einstein Telescope (ET), where cross-channel correlations must be accounted for to avoid biased parameter estimation of gravitational-wave signals. Unlike approaches that estimate test-mass and optical-metrology-system noise separately at the single-link level and then map them to the Time-Delay Interferometry (TDI) channels through known transfer functions, we develop a Bayesian nonparametric method that directly estimates the spectral density matrix of the XYZ channels, thereby accommodating additional sources of uncertainty. Our approach combines a flexible matrix-gamma process prior on the matrix-valued coefficients of a Bernstein polynomial basis expansion with a blocked multivariate Whittle likelihood. The prior guarantees Hermitian positive definiteness of the spectral estimate at every frequency. To avoid reversible-jump methods, we use an adaptive Markov chain Monte Carlo (MCMC) algorithm for posterior sampling. The proposed framework can also be used to correct misspecified parametric noise models. Results from a simulation study and simulated correlated-noise data for both LISA and ET demonstrate the effectiveness of the proposed method.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.26619",
  "author_total": 7,
  "authors": [
    {
      "name": "Yixuan Liu"
    },
    {
      "name": "Renate Meyer"
    },
    {
      "name": "Nelson Christensen"
    },
    {
      "name": "Jeung Eun Lee"
    },
    {
      "name": "Jianan Liu"
    },
    {
      "name": "Patricio Maturana-Russel"
    },
    {
      "name": "Avi Vajpeyi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/74hybt6w5ejcz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.26619",
  "fulltext_url": null,
  "id": "74hybt6w5ejcz",
  "issns": [],
  "landing_url": "https://hal.science/hal-05733690v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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  "pdf_url": "https://arxiv.org/pdf/2607.26619",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/74hybt6w5ejcz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "hal-05733690",
      "source": "hal",
      "url": "https://hal.science/hal-05733690v1"
    },
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2607.26619",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.26619"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    },
    {
      "field": "physics",
      "origin": "hal:2.phys.phys.phys-data-an",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors",
  "title_text": "Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/74hybt6w5ejcz",
  "venue": "HAL",
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}