Source Finding and Characterisation for SKAO Science

Abstract

The advancements in highly sensitive and powerful radio telescopes, including the Square Kilometre Array Observatory (SKAO) and its precursors, MeerKAT, ASKAP, MWA, and HERA, will enable us to create the deepest radio images of the sky. However, due to the sheer scale of the datasets, manually classifying and analyzing this data is computationally expensive, time-consuming, and laborious. Therefore, the development of automated algorithms to detect and classify complex morphological radio sources from large astronomical surveys is the need of the time. In this chapter, we examine the recent advancements and challenges in source-finding techniques triggered by the analysis of SKAO precursor data and the SKA Data Challenges, both in spectral and continuum modes, along with the growing demand for computational resources and automated source detection methods using machine learning (ML) algorithms for the identification and characterisation of new populations of sources, addressing their complex and diffuse morphologies, as well as transient nature. Additionally, we discuss the critical factors affecting the quality and limitations of automated source-finding techniques, including artefacts from residual continuum emission, sidelobes, radio frequency interference (RFI), technical failures, calibration issues, flagging methods and false positives. With the availability of the full operational phase of the SKAO, continued advancements in source detection algorithms and computational infrastructure will be essential to fully exploit its scientific potential.