In this paper we deal with classification of anomalous data detected by the data reduction system of the Gaia space mission, in operation since 2013. Given the size and complexity of intermediate data and plots for diagnostics, beyond practical possibility of full human evaluation, the need for automated signal processing tools is becoming more and more relevant. Our classification task consists in discriminating among “normal” data and data affected by anomalies, which at present are grouped into four different classes. We investigate the use of some clever pre-processing approaches that allow the application of a tailored technique based on the Hough transform, and of some machine learning tools, evidencing that the task can be exactly solved in the former case. In the latter case, random forests and support vector machine provide less than satisfactory performance, while convolutional neural networks achieve very good classification accuracy, up to \(91.22\%\). Statistics show satisfactory results also in terms of precision and recall of each class.

在本文中，我们处理自 2013 年开始运行的盖亚太空任务的数据简化系统检测到的异常数据的分类。鉴于中间数据和诊断图的大小和复杂性，超出了全面人类评估的实际可能性，需要自动化信号处理工具变得越来越重要。我们的分类任务包括区分“正常”数据和受异常影响的数据，目前分为四个不同的类别。我们调查了一些巧妙的预处理方法的使用，这些方法允许应用基于霍夫变换的定制技术和一些机器学习工具，证明在前一种情况下可以准确地解决任务。在后一种情况下，\(91.22\%\)。统计数据在每个类别的准确率和召回率方面也显示出令人满意的结果。