A Self-Supervised Framework for Space Object Behaviour Characterisation.

A Self-Supervised Framework for Space Object Behaviour Characterisation. | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 September 2025 V1 Latest version Share on A Self-Supervised Framework for Space Object Behaviour Characterisation. Authors : Ian Groves 0000-0001-6317-8226 [email protected] , Andrew Campbell 0000-0002-4439-3630 , James Fernandes , Diego Ramírez Rodríguez , Paul Murray , Massimiliano Vasile , and Victoria Nockles Authors Info & Affiliations https://doi.org/10.22541/au.175852316.64092876/v1 171 views 109 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Foundation Models, which leverage large neural networks pre-trained on unlabelled data before fine-tuning for specific tasks, are increasingly being applied to specialised domains. Recent examples include ClimaX for climate and Clay for satellite Earth observation, but a Foundation Model for Space Object Behavioural Analysis has not yet been developed. As orbital populations grow, automated methods for characterising space object behaviour are crucial for space safety. Here, we present a Space Safety and Sustainability Foundation Model focusing on space object behavioural analysis using light curves. To build our Foundation Model, we implemented a Perceiver-Variational Autoencoder (VAE) architecture, pre-trained with self-supervised reconstruction and masked reconstruction on 227,000 light curves from the MMT-9 observatory. The VAE enables anomaly detection, space object motion prediction, and generation of synthetic light curves. We fine-tuned the model for anomaly detection & motion prediction using two independent light curve simulators (CASSANDRA and GRIAL respectively), using CAD models of boxwing, Sentinel-3, SMOS, and Starlink platforms. Our pre-trained model achieved a reconstruction mean squared error of 0.009, identifying potentially anomalous light curves through reconstruction difficulty. After fine-tuning, the model scored 88% and 82% accuracy, with 0.90 and 0.95 ROC AUC scores respectively in both anomaly detection and motion mode prediction (e.g., sun-pointing, spin, tumbling etc.). Analysis of high-confidence anomaly predictions on real data revealed distinct patterns including characteristic object profiles and satellite glinting. The motion mode prediction model successfully differentiated between various movement behaviours such as sun-pointing, spin, and tumbling. Our work demonstrates how self-supervised learning can simultaneously enable anomaly detection, motion prediction, and synthetic data generation from rich representations learned in pre-training. More broadly, our work supports space safety and sustainability through automated monitoring and simulation capabilities. Supplementary Material File (a_self_supervised_framework_for_space_object_behaviour_characterisation___preprint___submitted-2.pdf) Download 4.58 MB Information & Authors Information Version history V1 Version 1 22 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords generative ai for space light curve anomaly detection self-supervised learning space domain awareness ai space foundation model space object behavioural analysis space situational awareness ai Authors Affiliations Ian Groves 0000-0001-6317-8226 [email protected] The Alan Turing Institute View all articles by this author Andrew Campbell 0000-0002-4439-3630 University of Strathclyde Department of Electronic and Electrical Engineering View all articles by this author James Fernandes GMV Innovating Solutions SL View all articles by this author Diego Ramírez Rodríguez GMV Innovating Solutions SL View all articles by this author Paul Murray University of Strathclyde Department of Electronic and Electrical Engineering View all articles by this author Massimiliano Vasile University of Strathclyde View all articles by this author Victoria Nockles The Alan Turing Institute View all articles by this author Metrics & Citations Metrics Article Usage 171 views 109 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ian Groves, Andrew Campbell, James Fernandes, et al. A Self-Supervised Framework for Space Object Behaviour Characterisation.. Authorea . 22 September 2025. DOI: https://doi.org/10.22541/au.175852316.64092876/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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