Radio astronomical images object detection and segmentation: a benchmark on deep learning methods

Renato, Sortino,; Magro, D.; Fiameni, Giuseppe; Sciacca, Eva; Riggi, S.; DeMarco, Andrea; Spampinato, Concetto; Hopkins, A. M.; Bufano, F.; Schillirò, F.; Bordiu, Cristobal; Pino, Carmelo

doi:10.1007/s10686-023-09893-w

Cited by 4 publications

(1 citation statement)

References 73 publications

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“…In supervised learning, the model undergoes training using image-label pairs, where these labels provide complete information required for the model to make specific predictions. Recently, machine learning (ML) techniques, as exemplified by studies, such as Lukic et al (2018), Alger et al (2018), Wu et al (2019), Bowles et al (2020), Maslej-Krešňáková et al (2021), Becker et al (2021), Brand et al (2023), Riggi et al (2023), Sortino et al (2023), Lao et al (2023), and Gupta et al (2024), have found application in the morphological classification and detection of radio galaxies.…”

Section: Introductionmentioning

confidence: 99%

RG-CAT: Detection pipeline and catalogue of radio galaxies in the EMU pilot survey

Gupta,

Norris,

Hayder

et al. 2024

Publ. Astron. Soc. Aust.

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We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 deg2 pilot survey of the Evolutionary Map of the Universe (EMU-PS) conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The detection pipeline uses Gal-DINO computer-vision networks (Gupta et al., 2024) to predict the categories of radio morphology and bounding boxes for radio sources, as well as their potential infrared host positions. The Gal-DINO network is trained and evaluated on approximately 5,000 visually inspected radio galaxies and their infrared hosts, encompassing both compact and extended radio morphologies.We find that the Intersection over Union (IoU) for the predicted and ground truth bounding boxes is larger than 0.5 for 99% of the radio sources, and 98% of predicted host positions are within 3″ of the ground truth infrared host in the evaluation set. The catalogue construction pipeline uses the predictions of the trained network on the radio and infrared image cutouts based on the catalogue of radio components identified using the Selavy source finder algorithm. Confidence scores of the predictions are then used to prioritize Selavy components with higher scores and incorporate them first into the catalogue. This results in identifications for a total of 211,625 radio sources, with 201,211 classified as compact and unresolved. The remaining 10,414 are categorized as extended radio morphologies, including 582 FR-I, 5,602 FR-II, 1,494 FR-x (uncertain whether FR-I or FR-II), 2,375 R (single-peak resolved) radio galaxies, and 361 with peculiar and other rare morphologies. Each source in the catalogue includes a confidence score.We cross-match the radio sources in the catalogue with the infrared and optical catalogues, finding infrared cross-matches for 73% and photometric redshifts for 36% of the radio galaxies. The EMU-PS catalogue and the detection pipelines presented here will be used towards constructing catalogues for the main EMU survey covering the full southern sky.

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Section: Introductionmentioning

confidence: 99%

RG-CAT: Detection pipeline and catalogue of radio galaxies in the EMU pilot survey

Gupta,

Norris,

Hayder

et al. 2024

Publ. Astron. Soc. Aust.

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Integration and Deployment of Model Serving Framework at Production Scale

Caronte,

Sciacca

2023

Astrophysics and Space Science Proceedings

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ContinUNet: fast deep radio image segmentation in the Square Kilometre Array era with U-Net

Stewart,

Birkinshaw,

Yeung

et al. 2024

RAS Techniques and Instruments

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We present a new machine learning driven (ML) source-finding tool for next generation radio surveys that performs fast source extraction on a range of source morphologies at large dynamic ranges with minimal parameter tuning and post-processing. The construction of the Square Kilometre Array (SKA) radio telescope will revolutionise the field of radio astronomy. However, accurate and automated source-finding techniques are required to reach SKA science goals. We have developed a novel source-finding method, ContinUNet, powered by an ML segmentation algorithm, U-Net, that has proven highly effective and efficient when tested on SKA precursor data sets. Our model was trained and tested on simulated radio continuum data from SKA Science Data Challenge 1 and proved comparable to the state-of-the-art source-finding methods, PyBDSF and ProFound. ContinUNet was then tested on the MIGHTEE Early Science data without retraining and was able to extract point-like and extended sources with equal ease; processing a 1.6 deg2 field in <13 s on a supercomputer and ≈2 min on a personal laptop. We were able to associate components of extended sources without manual intervention with the powerful inference capabilities learnt within the network, making ContinUNet a promising tool for enabling science in the upcoming SKA era.

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Radio astronomical images object detection and segmentation: a benchmark on deep learning methods

Cited by 4 publications

References 73 publications

RG-CAT: Detection pipeline and catalogue of radio galaxies in the EMU pilot survey

RG-CAT: Detection pipeline and catalogue of radio galaxies in the EMU pilot survey

Integration and Deployment of Model Serving Framework at Production Scale

ContinUNet: fast deep radio image segmentation in the Square Kilometre Array era with U-Net

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