{
  "abstract": "Multi-messenger astronomy (MMA), the joint study of cosmic sources through gravitational waves (GWs), electromagnetic (EM) radiation, neutrinos, and cosmic rays, is rapidly reshaping time-domain astrophysics. Realizing the promise of MMA will require coordinating heterogeneous observing resources and automating the chain from alert to analysis to follow-up. RADAR (Radio Afterglow Detection and AI-driven Response) is a federated, privacy-enhancing framework for the radio follow-up of GW events, previously validated on GW170817. Here, we extend it along three axes. First, we benchmark three large language models (LLMs; GPT-5.5, Claude-Opus-4.7, and Gemini-3.5-Flash) against the GW170817 radio light curve dataset. GPT-5.5 attains the highest event-level $F_1$ score, the harmonic mean of precision and recall, at $0.893 \\pm 0.010$, and the highest GCN-level recall, $0.794 \\pm 0.013$, improving on previous GPT-4.1 results by 16\\% and 10\\%, respectively, while Claude-Opus-4.7 achieves the highest precision, $0.978 \\pm 0.014$. Second, we introduce concurrent likelihood evaluation, which speeds up the MCMC computation by a factor of $40\\times$ over our previous results. Third, we present an LLM-driven scheme that converts natural-language observing requests into submission-ready scheduling blocks for the Karl G. Jansky Very Large Array. Together, these developments advance RADAR toward a scalable, largely autonomous system for GW radio follow up.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.18233",
  "author_total": 9,
  "authors": [
    {
      "name": "Mihael Hategan-Marandiuc"
    },
    {
      "name": "Tanner O'Dwyer"
    },
    {
      "name": "Alessandra Corsi"
    },
    {
      "name": "Eliu Huerta"
    },
    {
      "name": "Zilinghan Li"
    },
    {
      "name": "Ian T. Foster"
    },
    {
      "name": "Kyle Chard"
    },
    {
      "name": "Ryan Chard"
    },
    {
      "name": "Amal Gueroudji"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d9n56ny0tsd6m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.18233",
  "fulltext_url": null,
  "id": "d9n56ny0tsd6m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.18233",
  "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.HE"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.18233",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d9n56ny0tsd6m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "2609.18233",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.18233"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Toward Autonomous Radio Follow-up of Multi-messenger Transients with RADAR: From Alert Parsing to Inference and Observation Scheduling",
  "title_text": "Toward Autonomous Radio Follow-up of Multi-messenger Transients with RADAR: From Alert Parsing to Inference and Observation Scheduling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/d9n56ny0tsd6m",
  "venue": "arXiv",
  "versions": []
}