ASTRA: An Autonomous Framework for Physics-Validated Analysis and Hypothesis Generation in Astrophysics

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.