{
  "abstract": "Little red dots (LRDs) are compact, red sources discovered at high redshifts by JWST, and their physical nature and selection function remain a topic of debate. Here, we investigate whether an unsupervised machine-learning approach applied to multi-band photometry has the capacity to identify LRD-like objects, along with other populations, without relying on predefined colour-cuts. Using UMAP, a manifold-learning (dimensionality-reduction) method, we placed ~242,000 isolated, well-measured sources from the ASTRODEEP-JWST catalogue on a 2D map, where objects with similar broadband colours, morphology, and photometric redshift lie close together. We then used spectroscopically confirmed LRDs to identify where LRD-like objects lie within this map. We compared the resulting areas with published colour cuts and validated our data-driven selection against archival NIRSpec spectra from the DJA. We find that the spectroscopically selected LRDs are concentrated in two well-defined regions with no colour-cut imposed, tracing populations that differ mainly in redshift; a difference that is imprinted on their broadband colours. The main region reaches a purity of ~0.78 at ~0.82 completeness on the spectroscopically classified subset. This result is competitive with (or cleaner than) literature colour-cuts, and yields ~100 additional candidates. We also tested the method as a general tool for population discovery: the manifold recovers the locations of brown dwarfs and broad-line active galactic nuclei with no explicit criterion and isolates rare pathological outliers. Overall, unsupervised manifolds, anchored by sparse high-confidence spectroscopic labels, provide an efficient, assumption-light framework for characterising populations, comparing selection methods on a common basis, and discovering rare objects in large photometric datasets.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.22835",
  "author_total": 12,
  "authors": [
    {
      "name": "Michele Ginolfi"
    },
    {
      "name": "Filippo Mannucci"
    },
    {
      "name": "Alessandro Marconi"
    },
    {
      "name": "Francesco D'Eugenio"
    },
    {
      "name": "Giacomo Venturi"
    },
    {
      "name": "Francesco Belfiore"
    },
    {
      "name": "Giovanni Cresci"
    },
    {
      "name": "Caterina Bracci"
    },
    {
      "name": "Stefano Carniani"
    },
    {
      "name": "Alessandra Cozzi"
    },
    {
      "name": "Roberto Maiolino"
    },
    {
      "name": "Guido Risaliti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c80ppz6d6bqsg.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.22835",
  "fulltext_url": null,
  "id": "c80ppz6d6bqsg",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.22835",
  "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.GA"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2607.22835",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c80ppz6d6bqsg.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:24Z",
      "native_id": "2607.22835",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.22835"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Unsupervised selection and characterisation of little red dots in JWST surveys with manifold learning",
  "title_text": "Unsupervised selection and characterisation of little red dots in JWST surveys with manifold learning",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/c80ppz6d6bqsg",
  "venue": "arXiv",
  "versions": []
}