Bull, PhilipPagano, MichaelLiu, JingLiu, Adrian2023-06-052023-06-052023Pagano, M., Liu, J., Liu, A., Kern, N.S., Ewall-Wice, A., Bull, P., Pascua, R., Ravanbakhsh, S., Abdurashidova, Z., Adams, T. and Aguirre, J.E., 2023. Characterization of inpaint residuals in interferometric measurements of the epoch of reionization. Monthly Notices of the Royal Astronomical Society, 520(4), pp.5552-5572.https://doi.org/10.1093/mnras/stad441http://hdl.handle.net/10566/9006To mitigate the effects of Radio Frequency Interference (RFI) on the data analysis pipelines of 21 cm interferometric instruments, numerous inpaint techniques have been developed. In this paper, we examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that is capable of inpainting RFI corrupted data. We train our network on simulated data and show that our network is capable of inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modelling are best suited for inpainting over narrowband RFI. We show that with our fiducial parameters discrete prolate spheroidal sequences (DPSS) and CLEAN provide the best performance for intermittent RFI while Gaussian progress regression (GPR) and least squares spectral analysis (LSSA) provide the best performance for larger RFI gaps.enDark agesFirst starsLarge-scale structure of UniverseObservationalStatisticalArticle