Self-supervised Perceiver-VAE pre-trained on 227,000 light curves from MMT-9 and fine-tuned on simulators achieves 85% accuracy and 0.92-0.95 ROC AUC in anomaly detection and motion mode prediction for space objects.
Classification of Low Earth Orbit (LEO) Resident Space Objects’ (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM)
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A Self-Supervised Framework for Space Object Behaviour Characterisation
Self-supervised Perceiver-VAE pre-trained on 227,000 light curves from MMT-9 and fine-tuned on simulators achieves 85% accuracy and 0.92-0.95 ROC AUC in anomaly detection and motion mode prediction for space objects.