{
  "abstract": "In the age of large automated surveys conducted at facilities like the NSF-DOE Vera C. Rubin Observatory, delivering precision science goals demands highly efficient scheduling strategies that remain agile and dynamic to changing environments on the summit. On site, a variety of telemetry informs the scheduler of the best next pointing given the atmosphere's transmission. This live monitoring is provided by the Dutch Rubin Enhanced Atmospheric Monitor (DREAM) all-sky camera. Currently, dynamic scheduling uses short-term predictive algorithms, such as tracking cloud cross-correlations, to anticipate movement at short scheduling timescales. However, many of Rubin's core science objectives require survey strategies to commit to observations 20--40,min in advance (pair mode), which remains out of reach because clouds at those timescales can arrive from beyond the horizon. We developed a single monolithic multimodal strategy that folds asynchronous data from both the DREAM camera and weather satellite imagery to forecast multi-horizon sky transmission at 90,s, 5,min, 20,min, and 30,min in advance. We demonstrate that at 20-minute timescales, our model reduces transmission error by 21% and false-clear misses by 22% versus Rubin's frozen-flow tracker, and reduces false-clear misses by 26% versus persistence. We also demonstrate empirically, through ablations, that the DREAM all-sky camera provides short-horizon predictive power while satellite imagery provides long-horizon predictive power. We estimate the optimistic order-of-magnitude gain for survey efficiency improvement to be between 0.1% to 1%.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.22725",
  "author_total": 2,
  "authors": [
    {
      "name": "Peter Xiangyuan Ma"
    },
    {
      "name": "Joshua S. Bloom"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6hfc5jamm4995.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.22725",
  "fulltext_url": null,
  "id": "6hfc5jamm4995",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.22725",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3105|Astronomy and Astrophysical Research"
    ]
  ],
  "openalex": "W7222970062",
  "pdf_url": "https://arxiv.org/pdf/2610.22725",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6hfc5jamm4995.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2610.22725",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.22725"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Multi-modal transmission forecasting for the Vera C. Rubin Observatory",
  "title_text": "Multi-modal transmission forecasting for the Vera C. Rubin Observatory",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:28Z",
  "url": "https://research.draconitic.com/paper/6hfc5jamm4995",
  "venue": "arXiv",
  "versions": []
}