YOLO-CIANNA: Galaxy detection with deep learning in radio data. I. A new YOLO-inspired source detection method applied to the SKAO SDC1

Abstract

The upcoming Square Kilometer Array (SKA) will set a new standard regarding data volume generated by an astronomical instrument, which is likely to challenge widely adopted data-analysis tools that scale inadequately with the data size. The aim of this study is to develop a new source detection and characterization method for massive radio astronomical datasets based on modern deep-learning object detection techniques. For this, we seek to identify the specific strengths and weaknesses of this type of approach when applied to astronomical data. We introduce YOLO-CIANNA, a highly customized deep-learning object detector designed specifically for astronomical datasets. In this paper, we present the method and describe all the elements introduced to address the specific challenges of radio astronomical images. We then demonstrate the capabilities of this method by applying it to simulated 2D continuum images from the SKA observatory Science Data Challenge 1 (SDC1) dataset. Using the SDC1 metric, we improve the challenge-winning score by +139% and the score of the only other post-challenge participation by +61%. Our catalog has a detection purity of 94% while detecting 40 to 60% more sources than previous top-score results, and exhibits strong characterization accuracy. The trained model can also be forced to reach 99% purity in post-process and still detect 10 to 30% more sources than the other top-score methods. It is also computationally efficient, with a peak prediction speed of 500 images of 512x512 pixels per second on a single GPU. YOLO-CIANNA achieves state-of-the-art detection and characterization results on the simulated SDC1 dataset and is expected to transfer well to observational data from SKA precursors.

arXiv comment42 pages. Custom layout edit of the corresponding in press A&A paper

References in corpus (30)

• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• The LOFAR Two-metre Sky Survey -- V. Second data release
• ProFound: Source Extraction and Application to Modern Survey Data
• Source finding in the era of the SKA (precursors): Aegean 2.0
• Source extraction and photometry for the far-infrared and sub-millimeter continuum in the presence of complex backgrounds
• Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection
• A new source detection algorithm using FDR
• The Tiered Radio Extragalactic Continuum Simulation (T-RECS)
• BLOBCAT: Software to Catalogue Flood-Filled Blobs in Radio Images of Total Intensity and Linear Polarization
• Duchamp: a 3D source finder for spectral-line data
• Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning
• Source-finding for the Australian Square Kilometre Array Pathfinder
• Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes
• Estimate of the carbon footprint of astronomical research infrastructures
• deep21: a Deep Learning Method for 21cm Foreground Removal
• Square Kilometre Array Science Data Challenge 1: analysis and results
• DeepSource: Point Source Detection using Deep Learning
• Deep learning approach for identification of HII regions during reionization in 21-cm observations
• MaxiMask and MaxiTrack: two new tools for identifying contaminants in astronomical images using convolutional neural networks
• PySE: Software for Extracting Sources from Radio Images
• SKA Science Data Challenge 2: analysis and results
• Artificial intelligence for celestial object census: the latest technology meets the oldest science
• YOLO-CL: Galaxy cluster detection in the SDSS with deep machine learning
• Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
• A Multimodal Dataset and Benchmark for Radio Galaxy and Infrared Host Detection
• CAESAR source finder: recent developments and testing
• Edge-on Low-surface-brightness Galaxy Candidates Detected from SDSS Images Using YOLO
• Lightweight HI source finding for next generation radio surveys

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• Transformer-Based Source Detection and Morphological Classification in LOFAR Deep-Field Continuum Images
• Galaxy clusters in the LoTSS-DR3: Catalogues and detection pipeline for diffuse radio emission
• Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models