LCDC: Bridging Science and Machine Learning for Light Curve Analysis

2025-04-14

AI Summary

Photometric light-curve analysis of space objects—satellites, rocket bodies, and debris—suffers from poor reproducibility, lack of standardized benchmarks, and a disconnect between physics-based modeling and AI-driven approaches. Method: This paper introduces LCDC, an open-source, lightweight toolchain integrating time-series filtering, normalization, handcrafted feature engineering (e.g., periodicity, amplitude, shape statistics), and modeling via Scikit-learn/TensorFlow. It also presents RoBo6—the first standardized benchmark dataset for rocket-body classification. Contribution/Results: LCDC enables end-to-end scientific analysis: inferring surface albedo (e.g., Atlas 2AS Centaur) and reconstructing rotational dynamics (e.g., Delta IV). Its unified, modular framework significantly enhances methodological reproducibility, facilitates fair multi-model evaluation, and advances AI-enabled characterization of space debris—supporting sustainable space operations.

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Abstract

The characterization and analysis of light curves are vital for understanding the physical and rotational properties of artificial space objects such as satellites, rocket stages, and space debris. This paper introduces the Light Curve Dataset Creator (LCDC), a Python-based toolkit designed to facilitate the preprocessing, analysis, and machine learning applications of light curve data. LCDC enables seamless integration with publicly available datasets, such as the newly introduced Mini Mega Tortora (MMT) database. Moreover, it offers data filtering, transformation, as well as feature extraction tooling. To demonstrate the toolkit's capabilities, we created the first standardized dataset for rocket body classification, RoBo6, which was used to train and evaluate several benchmark machine learning models, addressing the lack of reproducibility and comparability in recent studies. Furthermore, the toolkit enables advanced scientific analyses, such as surface characterization of the Atlas 2AS Centaur and the rotational dynamics of the Delta 4 rocket body, by streamlining data preprocessing, feature extraction, and visualization. These use cases highlight LCDC's potential to advance space debris characterization and promote sustainable space exploration. Additionally, they highlight the toolkit's ability to enable AI-focused research within the space debris community.

Problem

• Facilitates preprocessing and analysis of light curve data
• Addresses lack of reproducibility in space object classification
• Enables advanced scientific analyses of space debris properties

Innovation

• Python toolkit for light curve preprocessing
• Integrates with public datasets like MMT
• Enables AI research in space debris