Modern UAP morphology is reframed as a multi-sensor, state-dependent framework that tracks how form changes across spectral bands, luminosity, motion, and observer interaction while distinguishing form (objective configuration) from shape (perceived result), treating Skywatcher’s provisional nine-class taxonomy (Tetra, Tic Tac, Blob, Beam, Manta Ray, Bright Star, Jellyfish, Hornet, Egg) as evolving, behavior- and sensor-signature–driven categories—with Dr. Garry Nolan among its collaborators—and exemplified by the Long Island Nightcrawler observations of John Joseph and Gerald Tedesco using radar, multispectral EO devices, infrared, RF, magnetic, acoustic, and environmental sensors that revealed recurring IR-dominant luminous spheroids (1.5–3 meters, sometimes polyhedral as brightness declined), thereby overlapping with Skywatcher classes without full taxonomy validation; reinforced by the Scientific Coalition for UAP Studies’ 2025 cluster analysis showing morphology as a strong clustering driver when paired with size, hovering, electromagnetic effects, and sound; supported by the Galileo Project’s multimodal observatory framework that integrates wide-field and narrow-field instruments, radar, RF analyzers, microphones, and environmental sensors; and operationalized through a five-variable morphology stack—Morphology, Spectral band, Kinematics, Interaction/Effects, Recurrence—to capture dynamic presentation states, with the practical takeaway that shape may be the first coordinate, but robust categories emerge from recurring morphology-signature packages tested across instruments, observers, and sites, cautiously verified by models like Skywatcher’s Discovery Framework and related research networks.

Morphology-Driven UAP Classification: A Multi-Sensor Framework and Provisional Nine-Class Taxonomy