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.

Anomaly Detection Techniques in the Gaia Space Mission Data

Roberti M.;Druetto A.;Cancelliere R.;Cavagnino D.;
2021-01-01

Abstract

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.
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https://link.springer.com/article/10.1007/s11265-021-01688-6
Anomaly Detection; Astrometry; Big Data; Deep learning
Roberti M.; Druetto A.; Busonero D.; Cancelliere R.; Cavagnino D.; Gai M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1804597
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