Problem
When a satellite breaks up, the fragments enter the catalogue as hundreds of unattributed objects. Reconnecting them to their parent event matters for attribution, for understanding how the breakup happened, and for predicting where the cloud goes next.
Machine-learning methods using proper elements have made progress on this, but they inherit a quiet failure mode: a model trained on an outdated representation of the debris environment degrades as the population evolves around it. There is also a specific technical trap — normalising quaternion-set features for a neural network destroys the orbital size information the classifier needs, and the loss is invisible until accuracy is measured.
Capability
A computational pipeline that generates synthetic fragmentation data from explosive breakup events using a Standard Breakup Model, propagates it under a high-fidelity dynamical model, and extracts proper elements across three representations — modified equinoctial (MEE), Poincaré (PNC) and quaternion (QTN) sets. Neural networks are then trained on combinations of those element sets to decide whether a pair of fragments shares a parent.
Extending beyond the modified-equinoctial space used by previous approaches widens the dynamical fingerprint available to the classifier. The pipeline also includes an augmented quaternion representation, QTNp, which explicitly restores the semi-latus rectum lost during feature normalisation.
Demonstrated result
- ROC-AUC improved from 0.789 to 0.858 in synthetic Starlink-like LEO experiments, comparing the joint MEE + PNC + QTN feature set against the MEE-only baseline, with corresponding gains in accuracy and F1.
- The QTNp augmentation lifted quaternion-set accuracy from 0.31 to 0.60, by restoring the orbital size information that standard normalisation discards — identifying and closing a failure mode that had not previously been characterised.
- Published as a preprint, arXiv:2512.08495, led by undergraduate researcher Michael Ling, and presented at the Australian Space Research Conference 2025 (slides).
External use
The method addresses a recognised gap in space sustainability and space domain awareness: maintaining attribution of fragmentation debris as the circumterrestrial environment evolves. The preprint is openly available, and the finding about quaternion-set normalisation applies to any classifier built on that representation, not only to this pipeline.
Next partnership opportunity
Applying the classifier to a real breakup event of interest to you, or extending it from synthetic training data to your catalogue. This fits most naturally as a sponsored thesis or co-funded project — see Partner with us.