This work presents a data-driven method for the classification of light curve measurements of Space Objects (SOs) based on a deep learning approach. Here, we design, train, and validate a Convolutional Neural Network (CNN) capable of learning to classify SOs from collected light-curve measurements. The proposed methodology relies on a physics-based model capable of accurately representing SO reflected light as a function of time, size, shape, and state of motion. The model generates thousands of light-curves per selected class of SO, which are employed to train a deep CNN to learn the functional relationship. between light-curves and SO classes. Additionally, a deep CNN is trained using real SO light-curves to evaluate the performance on real data, but limited training set. The CNNs are compared with more conventional machine learning techniques (bagged trees, support vector machines) and are shown to outperform such methods, especially when trained on real data.

中文翻译：

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使用深度卷积神经网络通过光曲线测量对空间物体进行分类

这项工作提出了一种基于数据驱动的方法，用于基于深度学习方法的空间物体（SO）光曲线测量的分类。在这里，我们设计，训练和验证了卷积神经网络（CNN），该网络能够学习根据收集的光曲线测量结果对SO进行分类。所提出的方法依赖于基于物理的模型，该模型能够根据时间，大小，形状和运动状态准确表示SO反射光。该模型为每个选定的SO类生成数千个光曲线，这些光曲线用于训练深层CNN以学习功能关系。在光曲线和SO类之间。此外，使用真实的SO光曲线对深层CNN进行训练，以评估真实数据的性能，但训练集有限。