Astronomical Data Analysis Software & Systems XXXIV

Sub-arcsecond degree-scale imaging pipelines with LOFAR
2024-11-11 , Aula Magna

In recent years, significant efforts have been made to automatically calibrate and image observations conducted with the Dutch high-band antennas from the Low Frequency Array (LOFAR) observing the universe at 150 MHz. These efforts have led to the LOFAR Two-metre Sky Survey (LoTSS; Shimwell et al. 2017, 2019, 2022) and the LoTSS-deep fields (Kondapally et al. 2021; Duncan et al. 2021; Tasse et al. 2021; Sabater et al. 2021), providing wide-field images of the northern sky at 144 MHz and 6” resolutions. However, 90% of the radio sources at 6” remain unresolved at 144 MHz. This necessitates higher resolutions by using data from all of LOFAR’s international stations, extending the maximum baselines to about 2000 km and resulting in sub-arcsecond resolutions.

We now present a calibration and imaging pipeline capable of producing deep sub-arcsecond resolution images, achieving the highest sensitivities and highest resolutions at the lowest frequencies radio frequencies (Morabito et al. 2022; Sweijen et al. 2022; de Jong et al. 2024). Given the challenge of working with hundreds of terabytes of data, we are now focused on reducing computational costs such that we enable the possibility of a near real-time calibration and imaging pipeline. This advancement will allow for LoTSS-type surveys with data from all of LOFAR's international stations, facilitating further research of radio sources at 150 MHz and sub-arcsecond angular scales.

I am a radio astronomer and research software engineer currently working at Leiden Observatory and in the final year of my PhD. I have a background in various fields and industries and have obtained international experience by studying, working, and living in five different countries (currently the Netherlands). My professional experience varies from developing data-driven tools and software for commercial companies to smashing around terabytes of data on high-performance supercomputers to learn something about the universe.