Detection: camouflage-by-taxonomy (catalog-residual mining).
hypothesisdetection_strategyScientific Instrumentation & Sensor Data
Hide as a 'known natural category'; detect by mining survey catalogs for objects whose fit to a natural class is statistically too perfect, or that carry small non-gravitational trajectory terms.
Relaxes the assumption that an artificial object would register as anomalous. The 'Oumuamua non-gravitational acceleration shows natural/artificial ambiguity is a real regime. Actionable on existing minor-planet/transient/UAP datasets. HYPOTHESIS. [DEEP DIVE] Now a runnable, false-positive-controlled prototype: research/detection/catalog_residual_miner.py fits the natural r^-2 outgassing law and flags anomalous-scaling residuals with a Bonferroni family-wise threshold (selftest: 0/200 false positives, 20/20 recall). Falsification contract: null=r^-2 outgassing; artificial signature=non-r^-2 acceleration; retire if real-null calibration flags nothing across surveys. Limit: fires only on measurable maneuver (a ballistic object leaves no residual - the coupling limit). [REAL DATA] Applied to the real JPL SBDB: the 580 asteroids with transverse-only A2 are the Yarkovsky thermal-recoil confuser; requiring a RADIAL A1 term narrows to 22 objects, whose own confusers (radiation pressure on tiny NEOs; 362P active-belt outgassing) are named and removed -- leaving 'Oumuamua as the residual. See research_outputs/detection-realdata.md + research/detection/catalog_real_data.py.