{
  "abstract": "Understanding the Universe's large-scale distribution of dark matter is a central objective in the era of precision cosmology. A key technique for the study of dark matter is weak gravitational lensing: a phenomenon where light from distant galaxies is sheared as it passes through the gravitational field of a massive object, like a galaxy cluster. This shear, which manifests as a slight (weak) distortion of shapes over thousands of galaxies, allows astrophysicists to infer the distribution of total matter, including both luminous and dark matter. Obtaining a mass distribution from a catalog of galaxy shears involves reconstructing the convergence ($κ$), the projected surface mass density divided by the critical surface density for the lens-source system. The Shear Mapping in Python (SMPy) package provides a standardized, well-documented, and open-source solution for creating convergence maps from weak lensing galaxy shear measurements. SMPy implements three mapping methods: the classic Kaiser-Squires inversion, aperture mass mapping, and, to our knowledge, the first publicly available implementation of the full KS+ algorithm, which corrects for missing data, field borders, and reduced shear. SMPy was initially developed to support the Superpressure Balloon-borne Imaging Telescope (SuperBIT) and has since evolved into a general-purpose tool suitable for analyzing the weak lensing data from cosmological surveys.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.00225",
  "author_total": 4,
  "authors": [
    {
      "name": "Georgios N. Vassilakis"
    },
    {
      "name": "Jacqueline E. McCleary"
    },
    {
      "name": "Maya Amit"
    },
    {
      "name": "Sayan Saha"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a4nnn37v636gr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.00225",
  "fulltext_url": null,
  "id": "a4nnn37v636gr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.00225",
  "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",
      "astro-ph.IM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.00225",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a4nnn37v636gr.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:59Z",
      "native_id": "2611.00225",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.00225"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Shear Mapping in Python (SMPy): Modular, Extensible, and Accessible Dark Matter Mapping",
  "title_text": "Shear Mapping in Python (SMPy): Modular, Extensible, and Accessible Dark Matter Mapping",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:18Z",
  "url": "https://research.draconitic.com/paper/a4nnn37v636gr",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.21105/joss.10928",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}