{
  "abstract": "Constellation Monte Carlo results depend on the orbital population used to evaluate a tasking policy. With scarce reference trajectories, replay limits geometric diversity, while independent orbital-element jitter can violate physical constraints. We study constrained diffusion for orbital-population augmentation. A force-conditioned diffusion model learns a 13-dimensional orbital prior, recovering semimajor axis from perigee altitude and eccentricity; Basilisk propagates each sample under one of five force-model tiers. Using 800 reference trajectories, we compare diffusion with jittered bootstrap, per-tier Gaussian mixtures, and a conditional variational autoencoder, and evaluate distributional fidelity, support shift, classical astrodynamics diagnostics, and 4,000 paired GoDSAT-compatible campaigns. The largest diffusion model achieves held-out trajectory MMD of 0.0171 +/- 0.0239, similar to bootstrap (0.0170) and the mixture (0.0198), but samples farther from training priors (median nearest-training distance 2.7 versus 0.10 standardized units). All generators fail to match the shifted-blind population (classifier AUC 0.991-0.998). Endpoint-conditioned samples satisfy Lambert boundaries but have greater interior error than the matched Lambert reference (29.2 versus 5.7 km mean RMSE). Residual diffusion improves selected sparse forecasts and catalog-mode recall but does not outperform classical estimators on custody ranking. In a fixed 16-satellite configuration, diffusion yields similar mean custody to replay and bootstrap, while the force-tier mixture shifts custody by about 5.5 percentage points. Constrained diffusion supports local, in-support augmentation but cannot replace orbital dynamics or serve as an operational posterior. Orbital-population construction is a consequential source of uncertainty in constellation analysis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.09323",
  "author_total": 6,
  "authors": [
    {
      "name": "Omar Ramadan"
    },
    {
      "name": "Sam Siavoshian"
    },
    {
      "name": "Amir Kashif Saeed"
    },
    {
      "name": "Benjamin A. Johnson"
    },
    {
      "name": "Amin M. E. -A. Diab"
    },
    {
      "name": "Benjamin M. Rodriguez"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dyvfqahtmmyrt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.09323",
  "fulltext_url": null,
  "id": "dyvfqahtmmyrt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.09323",
  "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.EP"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "openalex",
      "2202|Space Satellite Systems and Control"
    ]
  ],
  "openalex": "W7221025445",
  "pdf_url": "https://arxiv.org/pdf/2610.09323",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dyvfqahtmmyrt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2610.09323",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.09323"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.EP",
      "subfield": "planetary"
    }
  ],
  "title": "Constrained Diffusion for Data-Scarce Orbital Monte Carlo in Constellation Tasking",
  "title_text": "Constrained Diffusion for Data-Scarce Orbital Monte Carlo in Constellation Tasking",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/dyvfqahtmmyrt",
  "venue": "arXiv",
  "versions": []
}