{
  "abstract": "We present a field-level inference framework for testing gravity with large-scale structure, exploiting the full information of the galaxy distribution. Traditional analyses based on the power spectrum discard non-Gaussian and Fourier phase information, resulting in strong degeneracies between modified gravity (MG) and galaxy bias. Our approach overcomes this limitation by performing a Bayesian analysis directly on the three-dimensional galaxy number counts, jointly constraining MG and bias parameters using both amplitudes and phases. As an illustrative application, we analyse mock data in real space in the context of $f(R)$ gravity and a non-linear galaxy bias model designed to mimic the real-data 2M++ BORG analysis of Jasche&Lavaux (2019). Non-linear structure formation is modelled using COmoving Lagrangian Acceleration (COLA) under different gravity strengths, parameterised by $f_{R0}$. The resulting dark-matter fields are then mapped to mock galaxy catalogues via a non-linear bias prescription. We demonstrate that, with the initial phases assumed known, including non-Gaussian and phase information yields tighter constraints on both $f_{R0}$ and the primary bias parameter $β$ (corresponding to the linear galaxy bias on large scales), relative to power-spectrum-only analyses. Notably, the field-level approach breaks the degeneracies between MG and galaxy bias inherent to two-point statistics. Through a cosmic-web classification into voids, walls, filaments and clusters, we find that under-dense regions are the primary drivers in distinguishing gravity at the field level. Finally, we establish the robustness of our pipeline against variations in initial conditions, Poisson noise, and galaxy-field thresholding, providing a powerful path forward for field-level tests of gravity.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.03514",
  "author_total": 4,
  "authors": [
    {
      "name": "Sophie Hoyland"
    },
    {
      "name": "Daniela Saadeh"
    },
    {
      "name": "Kazuya Koyama"
    },
    {
      "name": "Harry Desmond"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/938kmz9c4qe13.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.03514",
  "fulltext_url": null,
  "id": "938kmz9c4qe13",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.03514",
  "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.CO"
    ],
    [
      "arxiv",
      "gr-qc"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2607.03514",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/938kmz9c4qe13.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2607.03514",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.03514"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "Disentangling modified gravity and galaxy bias with field-level inference",
  "title_text": "Disentangling modified gravity and galaxy bias with field-level inference",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:03Z",
  "url": "https://research.draconitic.com/paper/938kmz9c4qe13",
  "venue": "arXiv",
  "versions": []
}