{
  "abstract": "We present LUMA, a convolutional neural network (CNN) pipeline for the automated detection of strong gravitational lenses in simulated astronomical imaging. The method combines a physically motivated preprocessing stage, which enhances faint arc and ring features, with a compact three-block CNN trained using class reweighting and modern learning-rate scheduling. On simulated data, the model reaches test accuracies of about $96$\\% and receiver operating characteristic (ROC) area-under-the-curve (AUC) values of $\\simeq 0.99$ for the non-trivial classes, while confusion-matrix analysis shows high completeness and purity for lens candidates. These results demonstrate that relatively lightweight CNN architectures can provide a competitive baseline for strong-lens searches, and they motivate future extensions toward real survey images and transformer-based models.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36857",
  "author_total": 5,
  "authors": [
    {
      "name": "Giovanni Vincenzo Donatiello"
    },
    {
      "name": "Achille A. Nucita"
    },
    {
      "name": "Francesco De Paolis"
    },
    {
      "name": "Antonio Franco"
    },
    {
      "name": "Francesco Strafella"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0qyr143xh1c9v.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36857",
  "fulltext_url": null,
  "id": "0qyr143xh1c9v",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36857",
  "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.GA"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.36857",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0qyr143xh1c9v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36857",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36857"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "LUMA: A CNN for Strong Gravitational Lens Searches in Astronomical Imaging",
  "title_text": "LUMA: A CNN for Strong Gravitational Lens Searches in Astronomical Imaging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:31Z",
  "url": "https://research.draconitic.com/paper/0qyr143xh1c9v",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.ascom.2026.101198}{10.1016/j.ascom.2026.101198",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}