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Error Detection and Error Localization

Approaches for radio telescope system health management

The central LOFAR area in the Netherlands with phased array antennas visible as dark and gray patches

Modern radio telescopes are super sensitive, but the scale and complexity of these systems make it increasingly difficult to find and resolve system malfunctions. The LOFAR radio telescope for example produces almost ten thousand spectrogram images per observation that provide information about the quality of the systems. It is not practical to manually go through this large amount of images to detect system errors, which is why research has been conducted into whether artificial intelligence (AI) could offer a solution.

In the ASTRON-NleSC AI project, a neural network has been built, a so-called 'autoencoder', that is able to automatically find different types of system errors in the spectrogram images and cluster them. This approach is particularly suitable when no labeled data (example data) is available, such as at the start of this project. Each cluster corresponds to a certain type of pattern or error visible in the images. Because similar errors are clustered, finding system malfunctions has become much easier: instead of thousands of images, only about twenty clusters now need to be viewed.

A prototype web application has been made available to the LOFAR telescope, and has already led to the discovery of several subtle system errors, which were subsequently quickly resolved.

In addition to the above, the project has also implemented a method to suppress radio interference in time-variable signals from radio telescopes. This was done by efficiently implementing the relevant algorithms in computer hardware for the Time Domain Pipeline (TDP) of the Square Kilometer Array (SKA) radio telescope. This work contributes, for example, to follow-up research into low-frequency gravitational waves recently discovered using pulsar timing arrays.

Participating organisations

Netherlands eScience Center
Natural Sciences & Engineering
Natural Sciences & Engineering



  • 1.
    AI for detecting errors in radio astronomy
    Published in 2019
  • 2.
    Real time GPU-based RFI mitigation for SKA-NIP
    Author(s): Spreeuw, Hanno, Williams, Christopher, Alessio Sclocco, Alessio, Johan Hidding, Johan, Rob Van Nieuwpoort, Rob
    Published in 2017
  • 3.
    Error Detection and Error Localization Approaches for Radio Telescope System Health Management​
    Author(s): Alber-Jan Boonstra, Christiaan Meijer, Hanno Spreeuw, Ronald Nijboer, Elena Ranguelova, Michiel Brentjens, Willeke Mulder, Giovanni Mariani
    Published in 2016
  • 4.
    Using machine learning for checking LOFAR system health
    Author(s): Christiaan Meijer, Alber-Jan Boonstra
  • 5.
    AI for detecting errors in radio astronomy
    Author(s): Christiaan Meijer


Albert-Jan Boonstra
Principal investigator
Netherlands Institute for Radio Astronomy
Christiaan Meijer
Christiaan Meijer
eScience Research Engineer
Netherlands eScience Center
Elena Ranguelova
Elena Ranguelova
Technical Lead
Netherlands eScience Center

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This tool can convert and visualize radio astronomy measurement sets, as well as most LOFAR intermediate data products. It also performs RFI mitigation.

Updated 14 months ago
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