Quantifying radio source morphology

Abstract The advent of next-generation telescope facilities brings with it an unprecedented amount of data, and the demand for effective tools to process and classify this information has become increasingly important. This work proposes a novel approach to quantify the radio galaxy morphology, through the development of a series of algorithmic metrics that can quantitatively describe the structure of radio source, and can be applied to radio images in an automatic way. These metrics are intuitive in nature and are inspired by the intrinsic structural differences observed between the existing Fanaroff-Riley (FR) morphology types. The metrics are defined in categories of asymmetry, blurriness, concentration, disorder, and elongation ( ABCDE /single-lobe metrics), as well as the asymmetry and angle between lobes (source metrics). We apply these metrics to a sample of 480 sources from the Evolutionary Map of the Universe Pilot Survey (EMU-PS) and 72 well resolved extensively studied sources from An Atlas of DRAGNs, a subset of the revised Third Cambridge Catalogue of Radio Sources (3CRR). We find that these metrics are relatively robust to resolution changes, independent of each other, and measure fundamentally different structural components of radio galaxy lobes. These metrics work particularly well for sources with reasonable signal-to-noise and well separated lobes. We also find that we can recover the original FR classification using probabilistic combinations of our metrics, highlighting the usefulness of our approach for future large data sets from radio sky surveys.

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Publication Details

Journal
Publications of the Astronomical Society of Australia
Published
2025-01-01
DOI
https://doi.org/10.1017/pasa.2025.10067
Primary Topic
Speech and Audio Processing
Type
article
Field-Weighted Citation Impact
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article

Quantifying radio source morphology

Andrew Hopkins, R. P. Norris, H. Andernach, L. Rudnick et al.
Publications of the Astronomical Society of Australia
Speech and Audio Processing
article

Quantifying radio source morphology

Andrew Hopkins, R. P. Norris, H. Andernach, L. Rudnick, Tayyaba Zafar, N. Gupta, Michael J. Cowley, Stanislav S. Shabala, Lachlan J. Barnes
article en

Abstract

Abstract The advent of next-generation telescope facilities brings with it an unprecedented amount of data, and the demand for effective tools to process and classify this information has become increasingly important. This work proposes a novel approach to quantify the radio galaxy morphology, through the development of a series of algorithmic metrics that can quantitatively describe the structure of radio source, and can be applied to radio images in an automatic way. These metrics are intuitive in nature and are inspired by the intrinsic structural differences observed between the existing Fanaroff-Riley (FR) morphology types. The metrics are defined in categories of asymmetry, blurriness, concentration, disorder, and elongation ( ABCDE /single-lobe metrics), as well as the asymmetry and angle between lobes (source metrics). We apply these metrics to a sample of 480 sources from the Evolutionary Map of the Universe Pilot Survey (EMU-PS) and 72 well resolved extensively studied sources from An Atlas of DRAGNs, a subset of the revised Third Cambridge Catalogue of Radio Sources (3CRR). We find that these metrics are relatively robust to resolution changes, independent of each other, and measure fundamentally different structural components of radio galaxy lobes. These metrics work particularly well for sources with reasonable signal-to-noise and well separated lobes. We also find that we can recover the original FR classification using probabilistic combinations of our metrics, highlighting the usefulness of our approach for future large data sets from radio sky surveys.

Publications of the Astronomical Society of AustraliaVol. 42
Commonwealth Scientific and Industrial Research Organisation (AU), University of Tasmania (AU), Universidad de Guanajuato (MX), University of Minnesota (US), Queensland University of Technology (AU), University of Southern Queensland (AU), Astronomy and Space (AU), Western Sydney University (AU), Macquarie University (AU)
Openalex Percentile: Top 80%
Speech and Audio Processing
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