Python Framework Improves Radio Interferometry Imaging with uGMRT

This paper introduces a Python-based framework designed to improve radio interferometry imaging by addressing direction-dependent calibration challenges. Modern radio interferometric arrays, while offering high sensitivity and wide fields of view, are susceptible to artifacts caused by ionospheric phase distortions and primary beam variations. The presented framework, built on standard CASA tasks, aims to mitigate these effects.

The framework employs a peeling technique, efficiently subtracting bright-source models and suppressing associated artifacts. This results in flattened backgrounds, improved image fidelity, and enhanced faint-source detectability. An optimized "model-restoration" strategy is also introduced to preserve the flux densities and morphologies of bright sources. The framework's sequential application reduces background noise, increasing sensitivity and faint-source detection capabilities.

The framework's Python-based, CASA-compatible nature makes it readily applicable to other mid- and low-frequency interferometric arrays. The code is publicly released, fostering wider adoption and further development within the radio astronomy community. This advancement has the potential to significantly enhance the quality of radio astronomy data and facilitate more accurate observations of faint sources.

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