Scaling LEO lightcurve classification: hyperparameter-tuned boosted decision trees on an expanded dataset

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ID: 339160
2026
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Abstract
Abstract Building on previous low Earth orbit (LEO) satellite classification work, this paper aims to investigate the hyperparameter space of MMT-9 lightcurves and apply the optimal hyperparameters to classification. Both inter-platform and intra-platform lightcurve classification tasks are considered. The best hyperparameters were found to consist of 500 boosting steps, a learning rate of 0.01 and a max tree depth of 6 nodes. These hyperparameters were then used for classification with a lightcurve dataset size of 1000 lightcurves per platform, an order of magnitude greater than previous work, resulting in a slight increase in classification accuracy (87.92% versus 85.65%), suggesting a plateau was already being approached with 100 lightcurves per platform.
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Authors Billy Shrive, D. Pollacco, P. Chote, James A. Blake, Benjamin F Cooke, J. McCormac, R. G. West, Robert Airey, Alex MacManus, Phineas Whitlock
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Journal RAS Techniques and Instruments
Year 2026
DOI
10.1093/rasti/rzag095
URL
https://doi.org/10.1093/rasti/rzag095
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