{
  "abstract": "We present a control-variate method for reducing the variance of power spectrum covariance matrix estimates from simulations of large-scale structure. The idea is to pair each mock simulation with a cheap Zeldovich-approximation realization sharing the same initial conditions, and to use the known statistical properties of the Zeldovich field to remove correlated sample variance from the covariance estimator. Under a Gaussian disconnected approximation, we derive fully analytic expressions for both the optimal control-variate coefficient, $β(k,\\ell;k',\\ell')$, and the corresponding correlation, $ρ(k,\\ell;k',\\ell')$, in terms of the auto- and cross-power spectra of the target and control fields. In the monopole case, the correlation takes the simple form $ρ(k,k') = r^2(k),r^2(k')$, where $r(k)$ is the standard cross-correlation coefficient between the target and Zeldovich fields, implying that covariance estimation remains highly efficient whenever the two fields are strongly correlated. For masked redshift-space lognormal mocks, resembling LRGs from DESI, we find that the control-variate estimator reduces the variance of the covariance matrix by approximately an order of magnitude on large scales, $k\\lesssim 0.05\\,h\\,{\\rm Mpc}^{-1}$, precisely where accurate covariance estimation is most challenging. The gains are smaller for higher $k$ but typically accelerate convergence by a factor of 2-3, substantially lowering the computational cost of covariance estimation for current and upcoming large-scale structure surveys. After a cheap parametric-bootstrap calibration of the precision-matrix bias, the control-variate covariance yields a $χ^2$ distribution consistent with the expected number of degrees of freedom. This method is readily implementable in current imaging and spectroscopic surveys (e.g., DESI, \\textit{Euclid}, LSST, PFS, \\textit{SPHEREx}).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.28817",
  "author_total": 2,
  "authors": [
    {
      "name": "Boryana Hadzhiyska"
    },
    {
      "name": "Martin White"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f1nmxjpgkd840.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.28817",
  "fulltext_url": null,
  "id": "f1nmxjpgkd840",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.28817",
  "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"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.28817",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f1nmxjpgkd840.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:34Z",
      "native_id": "2605.28817",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.28817"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    }
  ],
  "title": "Fewer simulations, sharper covariances: Reducing mock covariance noise with Zeldovich approximation control variates",
  "title_text": "Fewer simulations, sharper covariances: Reducing mock covariance noise with Zeldovich approximation control variates",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:34Z",
  "url": "https://research.draconitic.com/paper/f1nmxjpgkd840",
  "venue": "arXiv",
  "versions": []
}