ASTRON has turned 75!
75 years ago today, SRZM (Stichting Radiostraling van Zon en Melkweg/Netherlands Foundation for Radio Astronomy) was founded. This organization would later become ASTRON.
ASTRON launches database of female experts
Today marks International Women’s Day. This year’s theme is ‘Inspire Inclusion’.
LOFAR ERIC: Distributed Research Infrastructure for European Astronomical Research Launched
LOFAR ERIC (European Research Infrastructure Consortium) has been officially launched at its first Council meeting today. The world-leading LOFAR (LOw Frequency ARray) Distributed Research Infrastructure has already revolutionised low-frequency radio astronomy research, resulting in an avalanche of scientific publications in the past decade. LOFAR ERIC is now a single legal entity across the European Union. The LOFAR ERIC statutory seat is in Dwingeloo, the Netherlands, hosted by NWO-I/ASTRON (Netherlands Institute for Radio Astronomy; the original designer of LOFAR).
Telescope quartet reveals surprising statistics of cosmic flashes
Scientists led by Chalmers astronomer Franz Kirsten have studied a famous source of repeating fast radio bursts – a still unexplained cosmic phenomenon. Comparing with earlier measurements, the scientists draw a conclusion with far-reaching consequences: any source of fast radio bursts will repeat, if watched long enough and carefully enough. The research team, a unique collaboration between professional and amateur radio astronomers, used four telescopes in northern Europe, amongst which ASTRON’s Westerbork Synthesis Radio Telescope.
Michael Mesarcik - Machine learning-based anomaly detection for radio telescopes
© ASTRON / UvA
In modern radio telescopes such as LOFAR, system health management is crucial for early detection of errors and for remedying them. As radio telescope data volumes are ever increasing, manually searching for system errors is becoming untenable. AI approaches to detect and classify error patterns are potentially much more accurate and complete than the manual inspection route. In the Efficient Deep Learning (EDL) PhD project at the UvA and ASTRON, Michael Mesarcik developed AI tools to detect and cluster different error patterns and regular events in LOFAR spectrogram data. The picture shows an example of nine clusters of event types that were detected with high accuracy. Following this success, a pilot proposal was presented to the LOFAR stakeholders and is awaiting implementation and testing. All details can be found in Michael Mesarcik's thesis (*) which he successfully defended Wednesday April 24. Misha, congrats!
(*) https://hdl.handle.net/11245.1/4e90ce44-5d87-402b-a33c-60297be96dae
Toegepaste RF-techniek
Mon 04 Nov 2024 - Thu 07 Nov 2024
De cursus Toegepaste RF-techniek bestaat uit een theoriegedeelte (75%) en hands-on sessies in ons eigen lab (25%).
Deelnemers aan deze cursus dienen een hbo werk- en denkniveau te hebben. De deelnemer kent de basisbegrippen van elektronica. Parate kennis van wiskundige concepten is niet vereist, maar komt wel aan de orde bij de transmissielijntheorie. In de cursus wordt ook complexe rekenwijze toegepast.