Advances on the morphological classification of radio galaxiesreview: A review

Stephen Machetho*, Trienko L. Grobler, Stefan J. Wijnholds, Dimka Karastoyanova, George Azzopardi

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)
103 Downloads (Pure)

Abstract

Modern radio telescopes will generate, on a daily basis, data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of machine learning to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of machine/deep learning in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study provides a comprehensive and concise overview of the use of machine learning techniques for the morphological classification of radio galaxies. It summarizes the recent literature on this topic, highlighting the main challenges, achievements, state-of-the-art methods, and the future research directions in the field. The application of machine learning in radio astronomy has led to a new paradigm shift and a revolution in the automation of complex data processes. However, the optimal exploitation of machine/deep learning in radio astronomy, calls for continued collaborative efforts in the creation of high-resolution annotated data sets. This is especially true in the case of modern telescopes like MeerKAT and the LOw-Frequency ARray (LOFAR). Additionally, it is important to consider the potential benefits of utilizing multi-channel data cubes and algorithms that can leverage massive datasets without relying solely on annotated datasets for radio galaxy classification.

Original languageEnglish
Article number101685
Number of pages17
JournalNew Astronomy Reviews
Volume97
Early online date17-Oct-2023
DOIs
Publication statusPublished - Dec-2023

Cite this