WAsp: The Wideband (W) Adaptive-Scale Pixel (Asp) Deconvolution Algorithm for Interferometric Imaging

TL;DR

WAsp is a new wide-band, scale-sensitive deconvolution algorithm for interferometric imaging that improves residuals and spectral index accuracy while maintaining computational efficiency.

Contribution

It introduces the WAsp algorithm, enhancing wide-band imaging by addressing limitations of existing scale-sensitive methods with improved performance and runtime.

Findings

Demonstrates superior imaging performance in simulations.

Effectively reduces residuals and spectral index errors.

Shows practical effectiveness on real-world data.

Abstract

This paper introduces the Wide-band Asp-Clean (\texttt{WAsp}) algorithm, a novel scale-sensitive image reconstruction method tailored for wide-band imaging applications. This algorithm is particularly beneficial for thermal noise-limited imaging with aperture synthesis telescopes, where joint spatio-frequency modeling of the sky brightness distribution is critical. The \texttt{WAsp} algorithm replaces the use of the MS-Clean algorithm in the MS-MFS algorithm with the {\tt Asp} algorithm \citep{Asp_Clean}, which itself has been improved for both imaging and runtime performance. With the high sensitivity of current and next-generation telescopes, spatio-frequency modeling in a scale-sensitive basis becomes crucial for ensuring that residuals align with the noise model across the frequency band. Although existing wide-band scale-sensitive algorithms have demonstrated superior performance over scale-insensitive counterparts, they often suffer from well-documented deficiencies, leading to significant wide-scale residuals in Stokes-I at low levels and consequently significant relative errors in spectral index maps. The \texttt{WAsp} algorithm addresses these limitations while maintaining computational efficiency. The implementation can be configured to support narrow-band and wide-band scale-sensitive imaging, spectral-cube imaging applications and joint single-dish and interferometer imaging. To demonstrate improved imaging performance, we show comparison with existing algorithms via carefully developed simulations for stress-testing the algorithms. We also present results from its application to real-world wide-band data, underscoring its effectiveness in practical imaging scenarios.