Error Detection and Error Localization

Approaches for radio telescope system health management
Image: Afshin Darian – The eight radio telescopes of the Smithsonian Submillimeter Array, located at the Mauna Kea Observatory in Hawaii –

In modern radio telescopes, System Health Management (SHM) systems are crucial for (early) detection of errors and for remedying them. Due to the increasing scale and complexity of the systems involved, the effectiveness and efficiency of current day SHM approaches are limited. Therefore, intelligent automated SHM approaches would significantly improve the quality and availability of the observational systems.

Crucial for scientific results

This is not only beneficial for maintenance, operations, and cost. It also is crucial for the scientific results, as accurate knowledge of the state of the telescope is essential for calibrating the system. Data analytics and more specifically Machine Learning (ML) have shown to be able to “learn” from data.

The purpose of this project is to investigate the applicability of novel approaches such as ML for SHM in radio astronomy. Although this project focuses on application of this technology in radio astronomy, similar problems arise in scientific instruments across many disciplines, such as high-energy physics, ecology, life sciences and urban planning.

A generic methodology

Similar problems also occur in large-scale simulations, for example in water management, computational chemistry and climate science. In this alliance, a generic methodology will be developed which is also applicable in these fields.

Output

1 Computer program

1 Conference paper

  • Pulsar Searches with the SKA

4 Presentations

  • Using machine learning for checking LOFAR system health
  • Real time GPU-based RFI mitigation for SKA-NIP
  • Error Detection and Error Localization Approaches for Radio Telescope System Health Management​
  • AI for detecting errors in radio astronomy

Team

  • Albert-Jan Boonstra
    Netherlands Institute for Radio Astronomy
  • Elena Ranguelova
    Netherlands eScience Center
  • Christiaan Meijer
    Netherlands eScience Center