{
  "abstract": "We present ASTRA, an autonomous framework for physics-validated analysis and hypothesis generation in astrophysics without per-step human intervention. It integrates dimensional analysis, structural causal models, Bayesian evidence computation, and multi-wavelength data fusion in a single automated workflow. Five test cases on real observational data recover known results: Herschel filament scaling, Chandra Deep Field South cross-matching, SDSS galaxy correlations, Gaia causal inference, and Bayesian virial model selection. A sixth case runs unsupervised on synthetic galaxy data. The main sequence and mass-metallicity relation are recovered, and the outlier detector, with a false-discovery-rate threshold and no contamination parameter, recovers the blind injected fraction on ten sealed datasets (purity > 0.99) and flags nothing on the 3,000-galaxy sample. A knowledge-isolation ablation shows pattern detection and physical validation are invariant to removal of domain knowledge, which affects only interpretation. In a direct comparison, agentic-LLM and pure-ML baselines match the statistical fitting but not the physics-validation stage. They rarely derive the equilibrium benchmark, split evenly on the physical verdict, and adopt a supplied erroneous benchmark in all six trap runs, an astrophysics-specific fine-tuned model completes zero of six runs. On live SDSS DR16 data it flags 197 of 4,999 galaxies at a data-determined threshold, ranked extremes classifiable from withheld spectroscopy. These are recovery validations and capability demonstrations rather than discoveries, with hypothesis generation demonstrated qualitatively.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36144",
  "author_total": 1,
  "authors": [
    {
      "name": "Glenn J. White"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7gnahy763gpzp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36144",
  "fulltext_url": null,
  "id": "7gnahy763gpzp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36144",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2610.36144",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7gnahy763gpzp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.36144",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36144"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "ASTRA: An Autonomous Framework for Physics-Validated Analysis and Hypothesis Generation in Astrophysics",
  "title_text": "ASTRA: An Autonomous Framework for Physics-Validated Analysis and Hypothesis Generation in Astrophysics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/7gnahy763gpzp",
  "venue": "arXiv",
  "versions": []
}