Transformer-based Space Debris Classifier using Observational Light Curves

Abstract

We address space debris classification using light curves. Light curves, time-series data recording the apparent magnitude of debris observed by optical telescopes, are widely used for identifying debris types. In previous studies, 1D-CNNs were the mainstream classifiers for space debris, where they were pre-trained on light curves generated through simulations and fine-tuned using observational light curves. However, CNNs have limitations in capturing long-term temporal dependencies, and simulated models cannot fully replicate real-world observational conditions, leading to discrepancies between simulated and observed light curves. To address these challenges, we propose a Transformer-based space debris classifier and introduce a pre-training method utilizing observational light curves. Experiments show that the F1 score of the Transformer classifier improves by 30% compared to the baseline 1D-CNN. Additionally, pre-training with observational light curves leads to an improvement of 7.8% in F1 score.