Source Finding and Characterisation for SKAO Science

arXiv:2607.03736 · astro-ph.IM, astro-ph.GA · Submitted 2026-07-04 · Read on arXiv

Sabyasachi Pal, Souvik Manik, M Carmen Toribio, Geferson Lucatelli, Omkar Bait, Simone Riggio, Antxon Alberdi, Philippa Hartley, Javier Moldon, Mamta Pandey-Pommier, Rob Beswick

astro-ph.IM, astro-ph.GA

Submitted: 2026-07-04

Comments: Published in Advancing Astrophysics with the SKA II (AASKAII), 2026 (arXiv:2606.20366). Report-no:AASKAII/SabyasachiPal01

Code: https://github.com/google/jax

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: 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

Terminology

AGN — 22× in this paper · explained in 1 episode
Calibration — 9× in this paper · explained in 7 episodes
Core — 9× in this paper · explained in 2 episodes
Interpretability — 7× in this paper · explained in 15 episodes
Transfer Learning — 6× in this paper · explained in 10 episodes
Accuracy — 5× in this paper · explained in 1 episode
Adaptation — 5× in this paper · explained in 1 episode
AGN Feedback — 4× in this paper · explained in 1 episode
Generalization — 4× in this paper · explained in 13 episodes
Self-Supervised Learning — 4× in this paper · explained in 8 episodes
APEX — 3× in this paper · explained in 1 episode
Clustering — 3× in this paper · explained in 2 episodes
Consistency — 3× in this paper · explained in 3 episodes
Domain Adaptation — 3× in this paper · explained in 5 episodes
Radio Galaxy Zoo — 3× in this paper
Regularization — 3× in this paper · explained in 3 episodes
Robustness — 3× in this paper · explained in 5 episodes
Semi-supervised Learning — 3× in this paper · explained in 4 episodes
Square Kilometre Array Observatory — 3× in this paper
Active Learning — 2× in this paper · explained in 6 episodes
Data Augmentation — 2× in this paper · explained in 7 episodes
Fidelity — 2× in this paper · explained in 2 episodes
Trace — 2× in this paper · explained in 2 episodes
U-net architecture — 2× in this paper · explained in 3 episodes
Abundance — 1× in this paper · explained in 1 episode
Accretion Physics — 1× in this paper · explained in 2 episodes
Aperture — 1× in this paper · explained in 1 episode
Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
Attention Mechanism — 1× in this paper · explained in 3 episodes
Australian Square Kilometre Array Pathfinder — 1× in this paper
Benchmark — 1× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 4 episodes
Contrastive Learning — 1× in this paper · explained in 5 episodes
Covariate Shift — 1× in this paper · explained in 2 episodes
Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
Fast Radio Bursts (FRBs) — 1× in this paper · explained in 4 episodes
Feature Engineering — 1× in this paper · explained in 3 episodes
Few-Shot Learning — 1× in this paper · explained in 4 episodes
Generalizability — 1× in this paper · explained in 6 episodes
Generative Model — 1× in this paper · explained in 2 episodes
Generative Models — 1× in this paper · explained in 3 episodes
Human-in-the-Loop — 1× in this paper · explained in 3 episodes
Hybrid Approach — 1× in this paper · explained in 4 episodes
Hybrid Architectures — 1× in this paper · explained in 3 episodes
Multimodal Learning — 1× in this paper · explained in 2 episodes
Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
Provenance — 1× in this paper · explained in 4 episodes
Pseudo-labeling — 1× in this paper · explained in 3 episodes
Semi-Supervised Learning (SSL) — 1× in this paper · explained in 2 episodes
Synthetic Data Generation — 1× in this paper · explained in 5 episodes
Transferability — 1× in this paper · explained in 4 episodes
Uncertainty Quantification — 1× in this paper · explained in 8 episodes
Unified Framework — 1× in this paper · explained in 5 episodes
Very Large Array — 1× in this paper

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.

Source Finding and Characterisation for SKAO Science

Terminology

Abstract

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