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We have developed and applied a new procedure which includes both direction-independent (DI and direction-dependent (DD) self-calibration to process Apertif data. The DD processing is based on the LOFAR Default Preprocessing Pipeline (DPPP/DP3; van Diepen et al. 2018) and the wsclean imaging package (Offringa et al. 2014). The code and documentation of the new pipeline are available at GitLab. The image of each compound beam of an observation is calibrated independently. The new pipeline starts with the pre-flagged cross-calibrated continuum visibilities produced with the initial steps of Apercal. For many of these data sets, additional flagging of antenna-beam combinations is made for which the DD errors are very large. Following this, a DI calibration is performed. This step includes three cycles of self-calibration and imaging. First, a phase-only self-calibration while the final step in the DI calibration is an amplitude and phase calibration. The second and third CLEAN step in the DI self-calibration are done using masks based on the local signal-to-noise ratio estimated from the residual images of the previous step. After the DI calibration, a clustering procedure is performed. The final CLEAN model obtained after the DI calibration is segmented using Voronoi tessellation, for which the cluster centers located at the 10 brightest sources. With this segmented model, DD calibration is performed by calibrating each segment independently. This step is performed subtracting the model visibilities from the DI calibrated data with the DD calibration solutions applied. The residual visibilities, free from DDEs, are then imaged, and the final DI model is restored on this image. This final compound-beam image is produced with a size of 3072 × 3072 pixels and a pixel scale of 3′′.

With this pipeline we aim to produce These pages describe the Apertif 1.4 GHz mosaic images of obtained with this pipeline for several deep fields observed by LOFAR: Bootes, Elais-North and Lockman Hole. A detailed description of the data processing can be found in Kutkin et al. (2023)