A multi-tier weak-supervision cascade on 232M TLE records creates 8.6M labeled sequences, enabling a 6.5M-parameter Transformer to reach 55% maneuver recall and 63% decay recall on held-out data for orbital anomaly triage.
Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher R´ e
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Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale
A multi-tier weak-supervision cascade on 232M TLE records creates 8.6M labeled sequences, enabling a 6.5M-parameter Transformer to reach 55% maneuver recall and 63% decay recall on held-out data for orbital anomaly triage.