RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference -- Application to pulsar observations
This addresses the issue of information loss in RFI mitigation for radio astronomy, particularly benefiting pulsar timing studies, though it is incremental as it builds on existing image-denoising networks.
The paper tackles the problem of radio frequency interference (RFI) in pulsar observations by proposing a joint detection and restoration method using a deep convolutional network, which restores dynamic spectra to achieve pulsar time-of-arrival accuracy close to that from RFI-free signals.
Radio frequency interference (RFI) have been an enduring concern in radio astronomy, particularly for the observations of pulsars which require high timing precision and data sensitivity. In most works of the literature, RFI mitigation has been formulated as a detection task that consists of localizing possible RFI in dynamic spectra. This strategy inevitably leads to a potential loss of information since parts of the signal identified as possibly RFI-corrupted are generally not considered in the subsequent data processing pipeline. Conversely, this work proposes to tackle RFI mitigation as a joint detection and restoration that allows parts of the dynamic spectrum affected by RFI to be not only identified but also recovered. The proposed supervised method relies on a deep convolutional network whose architecture inherits the performance reached by a recent yet popular image-denoising network. To train this network, a whole simulation framework is built to generate large data sets according to physics-inspired and statistical models of the pulsar signals and of the RFI. The relevance of the proposed approach is quantitatively assessed by conducting extensive experiments. In particular, the results show that the restored dynamic spectra are sufficiently reliable to estimate pulsar times-of-arrivals with an accuracy close to the one that would be obtained from RFI-free signals.