{
  "abstract": "Initial Orbit Determination (IOD) from Very Short Arcs (VSAs) remains one of the most challenging open problems in asteroid surveillance and celestial mechanics. Classical methods require angular observations spanning a sufficient fraction of the orbit, and become ill-conditioned or fail outright when only a single observing night is available, as is the case for most newly discovered Near-Earth Objects (NEOs). We present a comparative analysis of two Neural Network (NN) models that attack the ranging problem directly: both ingest a triplet of time-tagged angular measurements $(t_i,α_i,δ_i)$, $i=1,2,3$, from a single VSA and predict the geocentric range and range-rate $(ρ,\\dotρ)$, thus completing the orbital state vector. The first is a Multi-Layer Perceptron trained on a purely data-driven objective; the second augments it with a physics-informed loss. Both are trained, under an object-disjoint partition, on a sample of the $568\\,127$ VSAs of $39\\,031$ real NEOs available in the NEODyS-2 catalogue. Each model returns an estimate on every arc by construction, so we assess instead whether that estimate is dynamically admissible: on a held-out test set of $56\\,773$ arcs the physics-informed model places $85.1\\%$ of its predictions inside the admissible region, against $76.5\\%$ for the data-driven baseline, whereas Gauss's and Laplace's methods return a solution on only $45.0\\%$ and $47.3\\%$ of the same arcs and collapse onto the degenerate root in about nine of those cases out of ten. A stratified analysis over proper motion and true range shows that the physics-informed objective is not uniformly superior to the baseline: it trades a longer error tail for admissibility and for a marked advantage on the fast, nearby arcs that are operationally the most relevant.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.22020",
  "author_total": 4,
  "authors": [
    {
      "name": "Francesco Geroni"
    },
    {
      "name": "Roberto Paoli"
    },
    {
      "name": "Riccardo Massidda"
    },
    {
      "name": "Giacomo Tommei"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0azh64tn2xx5z.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.22020",
  "fulltext_url": null,
  "id": "0azh64tn2xx5z",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.22020",
  "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.EP"
    ],
    [
      "arxiv",
      "math-ph"
    ],
    [
      "arxiv",
      "math.MP"
    ],
    [
      "openalex",
      "2202|Space Satellite Systems and Control"
    ]
  ],
  "openalex": "W7213892035",
  "pdf_url": "https://arxiv.org/pdf/2609.22020",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0azh64tn2xx5z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:03Z",
      "native_id": "2609.22020",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.22020"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.EP",
      "subfield": "planetary"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:math-ph",
      "subfield": "mathematical-physics"
    }
  ],
  "title": "Comparative analysis of Neural Networks approaches for Initial Orbit Determination of Near-Earth Objects",
  "title_text": "Comparative analysis of Neural Networks approaches for Initial Orbit Determination of Near-Earth Objects",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:07Z",
  "url": "https://research.draconitic.com/paper/0azh64tn2xx5z",
  "venue": "arXiv",
  "versions": []
}