The Basic Shape Classification of Space Debris with Light Curves |
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| Authors: | LU Yao ZHAO Chang-yin |
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| Institution: | 1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023;2. Key Laboratory for Space Object and Debris Observation, Chinese Academy of Sciences, Nanjing 210023;3. University of Chinese Academy of Sciences, Beijing 100049 |
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| Abstract: | We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower. |
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| Keywords: | Space debris Methods: data analysis methods: observational methods: statistical techniques: photometric |
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