Project SADIM-V2 represents a paradigm shift in astronomical data processing, moving from static archival searches to dynamic "Galactic Computing." This framework reimagines the ESA Gaia DR3 archive a collection of trillions of data points as a Living Digital Map. By employing a pre-trained neural-statistical model with 77 million parameters, SADIM converts raw stellar metadata into a High-Dimensional Neural Vector Space. Each celestial object is encoded into a 512-dimensional latent vector, creating a unique "Digital Fingerprint" that represents the star's statistical essence and its deviation from the cosmic norm. This approach moves beyond traditional tabular queries, allowing for the autonomous identification of rare stellar phenomena through latent manifold analysis. The system achieves a 99.9% reduction in query latency, proving that the future of discovery lies in the neural representation of the universe. This publication outlines the architecture of the SADIM-77M autoencoder and its capacity to "understand" galactic structures through high-dimensional inference.
Houssem Fatnassi (Fri,) studied this question.