An Overview of the SKA Science Analysis Pipeline

TL;DR

The paper discusses the development of an automated SKA science analysis pipeline designed to handle massive data volumes by producing key data products and discarding uninteresting data in real time, enhancing efficiency and scientific output.

Contribution

It introduces a novel SKA data analysis pipeline that automates data product generation and filtering, addressing data volume challenges and enabling real-time scientific analysis.

Findings

Pipeline produces continuum images, spectral cubes, and Faraday spectra.

Automated data filtering reduces storage needs and improves efficiency.

Expected to increase scientific productivity and reduce archive size.

Abstract

When completed the Square Kilometre Array (SKA) will feature an unprecedented rate of image generation. While previous generations of telescopes have relied on human expertise to extract scientifically interesting information from the images, the sheer data volume of the data will now make this impractical. Additionally, the rate at which data are accrued will not allow traditional imaging products to be stored indefinitely for later inspection meaning there is a strong imperative to discard uninteresting data in pseudo-real time. Here we outline components of the SKA science analysis pipeline being developed to produce a series of data products including continuum images, spectral cubes and Faraday depth spectra. We discuss a scheme to automatically extract value from these products and discard scientifically uninteresting data. This pipeline is thus expected to give both an increase in scientific productivity, and offers the possibility of reduced data archive size producing a considerable saving.