{
  "abstract": "We present posterior-landscape, a Python framework to identify and characterize morphological features in two-dimensional probability densities. The framework combines concepts from topological data analysis, statistical density-ridge estimation, and scale-space image analysis. It extracts localized concentrations, valleys, ridges, and changes of slope; for Bayesian reconstructions, it propagates uncertainty in feature recovery, geometry, and correspondence across posteriors. The framework also connects two-dimensional structures to features in one-dimensional marginals, and quantifies their correlation with other inferred parameters. We validate the method on controlled densities, and demonstrate its use on gravitational-wave population reconstructions, where it provides a systematic way to identify and interpret population features and assess which ones carry information relevant for cosmology. The resulting catalogues also offer a common morphological representation for future comparisons between predictions and population reconstructions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.40272",
  "author_total": 3,
  "authors": [
    {
      "name": "Michele Mancarella"
    },
    {
      "name": "Viola De Renzis"
    },
    {
      "name": "Alessandro Agapito"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6zr7pw054h1b0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.40272",
  "fulltext_url": null,
  "id": "6zr7pw054h1b0",
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  "landing_url": "https://arxiv.org/abs/2610.40272",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.HE"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "gr-qc"
    ],
    [
      "arxiv",
      "physics.data-an"
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      "arxiv",
      "stat.CO"
    ]
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  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.40272",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6zr7pw054h1b0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:28Z",
      "native_id": "2610.40272",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.40272"
    }
  ],
  "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.CO",
      "subfield": "statistics"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.data-an",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "posterior-landscape: morphological feature finding in two-dimensional probability densities",
  "title_text": "posterior-landscape: morphological feature finding in two-dimensional probability densities",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/6zr7pw054h1b0",
  "venue": "arXiv",
  "versions": []
}