{'fraction_non_alphanumeric': 0.021341463414634148, 'fraction_numerical': 0.0, 'mean_word_length': 5.282296650717703, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Adaptive scale model reconstruction for radio synthesis imaging

A sky model from CLEAN deconvolution is a particularly effective high dynamic range reconstruction in radio astronomy, which can effectively model the sky and remove the sidelobes of the point spread function (PSF) caused by incomplete sampling in the spatial frequency domain. Compared to scale-free and multi-scale sky models, adaptive-scale sky modeling, which can model both compact and diffuse features, has been proven to have better sky modeling capabilities in narrowband simulated data, especially for large-scale features in high-sensitivity observations which are exactly one of the challenges of data processing for the Square Kilometre Array (SKA). However, adaptive scale CLEAN algorithms have not been verified by real observation data and allow negative components in the model. In this paper, we propose an adaptive scale model algorithm with non-negative constraint and wideband imaging capacities, and it is applied to simulated SKA data and real observation data from the Karl G. Jansky Very Large Array (JVLA), an SKA precursor. Experiments show that the new algorithm can reconstruct more physical models with rich details. This work is a step forward for future SKA image reconstruction and developing SKA imaging pipelines.