WS-Snapshot: An effective algorithm for wide-field and large-scale imaging

TL;DR

WS-Snapshot is a hybrid imaging algorithm that improves efficiency and reduces computational time for large-scale radio interferometry imaging, crucial for SKA's data processing needs.

Contribution

It introduces a novel hybrid imaging method combining improved W-Stacking and snapshots, enhancing performance and efficiency for SKA-scale radio imaging.

Findings

Reduces computational time significantly

Maintains acceptable imaging accuracy

Enables efficient distributed processing

Abstract

The Square Kilometre Array (SKA) is the largest radio interferometer under construction in the world. The high accuracy, wide-field and large size imaging significantly challenge the construction of the Science Data Processor (SDP) of SKA. We propose a hybrid imaging method based on improved W-Stacking and snapshots. The w range is reduced by fitting the snapshot $uv$ plane, thus effectively enhancing the performance of the improved W-Stacking algorithm. We present a detailed implementation of WS-Snapshot. With full-scale SKA1-LOW simulations, we present the imaging performance and imaging quality results for different parameter cases. The results show that the WS-Snapshot method enables more efficient distributed processing and significantly reduces the computational time overhead within an acceptable accuracy range, which would be crucial for subsequent SKA science studies.