RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification
This provides a common benchmark for researchers in space debris identification, though it is incremental as it standardizes existing data rather than introducing new methods.
The paper tackles the lack of a standardized benchmark for rocket body classification in space debris by introducing the RoBo6 dataset, which includes 5,676 training and 1,404 test samples across six classes, and reports Astroconformer as the best-performing model.
Space debris presents a critical challenge for the sustainability of future space missions, emphasizing the need for robust and standardized identification methods. However, a comprehensive benchmark for rocket body classification remains absent. This paper addresses this gap by introducing the RoBo6 dataset for rocket body classification based on light curves. The dataset, derived from the Mini Mega Tortora database, includes light curves for six rocket body classes: CZ-3B, Atlas 5 Centaur, Falcon 9, H-2A, Ariane 5, and Delta 4. With 5,676 training and 1,404 test samples, it addresses data inconsistencies using resampling, normalization, and filtering techniques. Several machine learning models were evaluated, including CNN and transformer-based approaches, with Astroconformer reporting the best performance. The dataset establishes a common benchmark for future comparisons and advancements in rocket body classification tasks.