A Deep Learning Approach to Anomaly Detection in the Gaia Space Mission Data

Alessandro Druetto, Marco Roberti, Rossella Cancelliere, Davide Cavagnino, Mario Gai

Abstract: The data reduction system of the Gaia space mission generates a large amount of intermediate data and plots for diagnostics, beyond practical possibility of full human evaluation. We investigate the feasibility of adoption of deep learning tools for automatic detection of data anomalies, focusing on convolutional neural networks and comparing with a multilayer perceptron. The results evidence very good accuracy ( $$\sim $$ 99.7%) in the classification of the selected anomalies.