Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the most commonly used method. In photometric observations, outliers may exist in the obtained light curves due to various reasons. Therefore, preprocessing is required to remove these outliers to obtain high quality light curves. Through statistical analysis, the reasons leading to outliers can be categorized into two main types: first, the brightness of the object significantly increases due to the passage of a star nearby, referred to as “stellar contamination,” and second, the brightness markedly decreases due to cloudy cover, referred to as “cloudy contamination.” The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive. However, we propose the utilization of machine learning methods as a substitute. Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination, achieving F1 scores of 1.00 and 0.98 on a test set, respectively. We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine, then conduct comparative analyses of the results.

中文翻译：

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光曲线数据预处理中基于机器学习的污染图像识别

姿态是空间物体的关键参数之一，在碰撞预测和碎片清除中起着至关重要的作用。分析光变曲线来确定姿态是最常用的方法。在光度观测中，由于各种原因，所获得的光变曲线中可能存在异常值。因此，需要进行预处理来去除这些异常值，以获得高质量的光变曲线。通过统计分析，导致异常值的原因主要分为两类：一是由于附近有恒星经过，物体亮度显着增加，称为“恒星污染”；二是亮度显着下降。由于云层覆盖，称为“云污染”。手动检查图像是否污染的传统方法既耗时又费力。然而，我们建议使用机器学习方法作为替代。采用卷积神经网络和 SVM 来识别恒星污染和云污染案例，在测试集上分别获得 1.00 和 0.98 的 F1 分数。我们还探索了其他机器学习方法，例如 ResNet-18 和 Light Gradient Boosting Machine，然后对结果进行比较分析。