{
  "abstract": "We present SONDE (SOrted Neighbor Distance Embedding): a scalable manifold learning approach to create a nuanced, continuous, low-dimensional representation of the geometry of galaxies' local environments. SONDE is a variation on Isomap that uses similarity distances between galaxy neighborhoods to create an embedding space, within which galaxies cluster according to their neighborhood geometries. We additionally present a novel method to project different datasets into the same embedding space, enabling comparisons of environmental embeddings across redshifts. We demonstrate this method on the TNG100 simulations at $z = 0 - 5$ and show that the embedding values evolve with redshift and correlate with galaxies' physical properties. We train a neural network to predict physical properties based on environmental features and show that our first embedding dimension outperforms a standard environmental quantifier, the number of neighbors, while the top three dimensions encode as much environmental information as a ten-bin radial profile. Our results are consistent with known density correlations, including galaxy halo mass, central vs. satellite status, and quenching. SONDE provides a straightforward way to encode subtleties of galaxy environments that could have significant implications for future analysis of galaxy-environment interactions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.03850",
  "author_total": 4,
  "authors": [
    {
      "name": "Ana Sofía M. Uzsoy"
    },
    {
      "name": "Claire Lamman"
    },
    {
      "name": "Peixin Zhu"
    },
    {
      "name": "Melanie Weber"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6grbtcfxa8812.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.03850",
  "fulltext_url": null,
  "id": "6grbtcfxa8812",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.03850",
  "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.IM"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7220667306",
  "pdf_url": "https://arxiv.org/pdf/2610.03850",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6grbtcfxa8812.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.03850",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.03850"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Tracing the evolution of galaxy environments with manifold learning",
  "title_text": "Tracing the evolution of galaxy environments with manifold learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/6grbtcfxa8812",
  "venue": "arXiv",
  "versions": []
}