📝 SummarXiv

Abstract

Context: Low-frequency radio observations are heavily impacted by the ionosphere, where dispersive delays can outpace even instrumental clock offsets, posing a serious calibration challenge. Especially below 100 MHz, phase unwrapping difficulties and higher-order dispersion effects can complicate the separation of ionospheric and clock delays. Aims: We address this challenge by introducing a method for reliably separating clock delays from ionospheric effects, even under mediocre to poor ionospheric conditions encountered near solar maximum. Methods: The approach employs a key technique: we modelled our likelihood space using the circular Gaussian distribution (von Mises random variable) rather than non-circular distributions that suffer from $2\pi$ phase ambiguities. This ensures that noisier data are weighted less heavily than cleaner data during the fitting process. Results: The method reliably separates clock delays and ionospheric terms that vary smoothly in time whilst providing a good fit to the data. A comparison with the clock-ionosphere separation approach used in standard LOFAR data processing shows that our technique achieves significant improvements. In contrast to the old algorithm, which often fails to return reliable results below 100 MHz even under good ionospheric conditions, the new algorithm consistently provides reliable solutions across a wider range of conditions. Conclusions: This new algorithm represents a significant advance for large-scale surveys, offering a more dependable way to study ionospheric effects and furthering research in ionospheric science and low-frequency radio astronomy.