YOLO-CIANNA: Galaxy detection with deep learning in radio data

I. A new YOLO-inspired source detection method applied to the SKAO SDC1

¹ LERMA, Observatoire de Paris, Université PSL, Sorbonne Université, CNRS, 75014 Paris, France
² Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON M5S 3H8, Canada
³ Research School of Astronomy & Astrophysics, Australian National University, Canberra ACT 2610, Australia
⁴ Université de Strasbourg, CNRS UMR 7550, Observatoire astronomique de Strasbourg, 67000 Strasbourg, France
⁵ DIO, Observatoire de Paris, CNRS, PSL, 75014 Paris, France
⁶ IDRIS, CNRS, 91403 Orsay, France
⁷ Collège de France, 11 Place Marcelin Berthelot, 75005 Paris, France
⁸ GEPI, Observatoire de Paris, CNRS, Université Paris Diderot, 5 Place Jules Janssen, 92190 Meudon, France
⁹ Department of Physics & Electronics, Rhodes University, PO Box 94, Grahamstown 6140, South Africa

^★ Corresponding author; david.cornu@observatoiredeparis.psl.eu

Received: 8 February 2024
Accepted: 19 August 2024

Abstract

Context. 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.

Aims. 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.

Methods. 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.

Results. 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 40–60% 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 10–30% more sources than the other top-score methods. It is also computationally efficient, with a peak prediction speed of 500 images of 512×512 pixels per second on a single GPU.

Conclusions. 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.