{
  "abstract": "Simulation-based inference and other field-level weak-lensing analyses require intrinsic-alignment (IA) models generating realistic intrinsic-ellipticity fields across two-point and non-Gaussian observables. We develop a halo-based IA prescription with separate central and satellite components and test it against intrinsic galaxy shapes in FLAMINGO. We jointly fit two-point and higher-order statistics involving the IA, lensing, and density fields, including correlations between the intrinsic shapes of distinct galaxies, and compare against field-level nonlinear-alignment and density-weighted NLA prescriptions. We first evaluate the models at the positions of the original FLAMINGO galaxies, fixing the galaxy--matter connection and isolating the IA response. NLA and $δ$NLA leave substantial discrepancies on nonlinear scales and across higher-order observables, whereas the halo model provides a simultaneous description of all statistics. We then remove the FLAMINGO galaxy catalogue and regenerate the source population from the halo field using a low-dimensional halo occupation model; the agreement is preserved even when the detailed occupation is only approximately reproduced. Much of this performance can be retained with a compact four-parameter portable model, and the same architecture transfers successfully to an independent gravity-only N-body realization with different halo definitions and shape measurements. These results show that nonlinear field-level IA modelling can be substantially improved over simple NLA-like prescriptions without introducing a large nuisance-parameter space. The halo model therefore provides a promising route for incorporating intrinsic alignments into simulation-based weak-lensing inference, although further validation across halo masses, hydrodynamical simulations, and realistic survey selections will be needed before application to data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.37254",
  "author_total": 1,
  "authors": [
    {
      "name": "M. Gatti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/468400jrzwx5p.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.37254",
  "fulltext_url": null,
  "id": "468400jrzwx5p",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.37254",
  "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"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7214896559",
  "pdf_url": "https://arxiv.org/pdf/2609.37254",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/468400jrzwx5p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.37254",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.37254"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    }
  ],
  "title": "A halo-based intrinsic-alignment model for simulation-based inference",
  "title_text": "A halo-based intrinsic-alignment model for simulation-based inference",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/468400jrzwx5p",
  "venue": "arXiv",
  "versions": []
}