Radio source-component association for the LOFAR Two-metre Sky Survey with region-based convolutional neural networks

Abstract

Radio loud active galactic nuclei (RLAGNs) are often morphologically complex objects that can consist of multiple, spatially separated, components. Astronomers often rely on visual inspection to resolve radio component association. However, applying visual inspection to all the hundreds of thousands of well-resolved RLAGNs that appear in the images from the Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) at $144$ MHz, is a daunting, time-consuming process, even with extensive manpower. Using a machine learning approach, we aim to automate the radio component association of large ($>15$ arcsec) radio components. We turned the association problem into a classification problem and trained an adapted Fast region-based convolutional neural network to mimic the expert annotations from the first LoTSS data release. We implemented a rotation data augmentation to reduce overfitting and simplify the component association by removing unresolved radio sources that are likely unrelated to the large and bright radio components that we consider using predictions from an existing gradient boosting classifier. For large ($>15$ arcsec) and bright ($>10$ mJy) radio components in the LoTSS first data release, our model provides the same associations for $85.3\%\pm 0.6$ of the cases as those derived when astronomers perform the association manually. When the association is done through public crowd-sourced efforts, a result similar to that of our model is attained. Our method is able to efficiently carry out manual radio-component association for huge radio surveys and can serve as a basis for either automated radio morphology classification or automated optical host identification. This opens up an avenue to study the completeness and reliability of samples of radio sources with extended, complex morphologies.

arXiv comment22 pages; accepted for publication in A&A

References in corpus (33)

• The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance
• Binary companions of evolved stars in APOGEE DR14: Search method and catalog of ~5,000 companions
• LOFAR: The LOw-Frequency ARray
• Overview of the DESI Legacy Imaging Surveys
• Reconciling modern machine learning practice and the bias-variance trade-off
• A guide to convolution arithmetic for deep learning
• 2MASS Extended Source Catalog: Overview and Algorithms
• SWIRE: The SIRTF Wide-area InfraRed Extragalactic Survey
• Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey
• The LOFAR Two-metre Sky Survey - I. Survey Description and Preliminary Data Release
• The LOFAR Two-metre Sky Survey -- V. Second data release
• Radio galaxies and feedback from AGN jets
• Compact continuum source-finding for next generation radio surveys
• Deep ATLAS radio observations of the CDFS-SWIRE field
• ProFound: Source Extraction and Application to Modern Survey Data
• Source finding in the era of the SKA (precursors): Aegean 2.0
• The LOFAR Two-metre Sky Survey (LoTSS) III. First Data Release: optical/IR identifications and value-added catalogue
• Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
• Spectral ageing in the lobes of FR-II radio galaxies: New methods of analysis for broadband radio data
• Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection
• The LOFAR Two Metre Sky Survey: Deep Fields Data Release 1 -- III. Host-galaxy identifications and value added catalogues
• Space Warps: I. Crowd-sourcing the Discovery of Gravitational Lenses
• Are All Layers Created Equal?
• Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies
• Calibration artefacts in radio interferometry. I. Ghost sources in WSRT data
• Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era
• Square Kilometre Array Science Data Challenge 1: analysis and results
• Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
• Nature and evolution of powerful radio galaxies and their link with the quasar luminosity function
• Unveiling the rarest morphologies of the LOFAR Two-metre Sky Survey radio source population with self-organised maps
• Spurious source generation in mapping from noisy phase-self-calibrated data
• Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps
• A machine learning classifier for LOFAR radio galaxy cross-matching techniques

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