Machine Learning Probes of Black Hole Quantum Structure: From Gaia Astrometric Jitter to Graviton Echo Detection

Black holes occupy a unique position at the intersection of astrophysics and fundamental physics, offering both observational challenges and theoretical puzzles. Recent advances in large-scale surveys, such as Gaia DR3, and gravitational wave detections from LIGO–Virgo–KAGRA have opened unprecedented opportunities to probe black hole structure and dynamics. This article explores the role of machine learning (ML) as a bridge between data-driven astronomy and quantum gravity phenomenology. On one front, unsupervised learning methods are applied to astrometric jitter in Gaia DR3 to identify candidate binary supermassive black holes (SMBHs), providing insights into galaxy evolution and potential precursors of gravitational wave sources. On another, supervised and unsupervised ML techniques are investigated as tools for detecting graviton echoes, hypothesized signatures of quantum hair that may extend beyond the classical no-hair theorem. By integrating these two domains, we present a framework in which ML-driven analyses can simultaneously enhance the discovery of astrophysical black hole systems and test theoretical models addressing the black hole information paradox. The convergence of astrometric surveys, gravitational wave astronomy, and machine intelligence suggests a promising pathway toward probing the quantum structure of spacetime.