Ultra-diffuse galaxies in the Kilo-Degree Survey with deep learning

¹ Department of Physics “E. Pancini”, University of Naples Federico II, Via Cintia, 21, 80126 Naples, Italy
² School of Mechanical, Electrical and Information Engineering, Shandong University, 180 Wenhua Xilu, Weihai 264209, Shandong, China
³ Institute for Astrophysics, School of Physics, Zhengzhou University, Zhengzhou 450001, PR China
⁴ INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Napoli, Italy
⁵ INFN, Sez. di Napoli, via Cintia, 80126 Napoli, Italy
⁶ INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Napoli, Italy
⁷ School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, 111 Renai Road, Suzhou 215123, PR China
⁸ INAF – Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5, I-35122 Padova, Italy
⁹ Ruhr University Bochum, Faculty of Physics and Astronomy, Astronomical Institute (AIRUB), German Centre for Cosmological Lensing, 44780 Bochum, Germany
¹⁰ School of Physics and Astronomy, Sun Yat-sen University, Zhuhai Campus, 2 Daxue Road, Xiangzhou District, Zhuhai 519082, China
¹¹ Leiden Observatory, Leiden University, Niels Bohrweg 2, 2333 CA, Leiden, The Netherlands
¹² School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
¹³ INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Napoli, Italy

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Received: 22 July 2025
Accepted: 24 December 2025

Abstract

Ultra-diffuse galaxies (UDGs) are a subset of low-surface-brightness galaxies (LSBGs), showing mean effective surface brightness fainter than 24 mag arcsec⁻² and a diffuse morphology, with effective radii larger than 1.5 kpc. Due to their elusiveness, using traditional methods over large sky areas is challenging. Here we present a catalog of UDG candidates identified in the full 1350 deg² area of the Kilo-Degree Survey (KiDS) using deep learning. In particular, we used a previously developed network for the detection of low-surface-brightness systems in the Sloan Digital Sky Survey (SBGnet) and optimized for UDG detection. We trained this new UDG detection network for KiDS (UDGnet-K), with an iterative approach, starting from a small-scale training sample. After training and validation, the UGDnet-K has been able to identify ∼3300 UDG candidates, of which, after visual inspection, we selected 545 high-quality ones. The catalog contains the independent rediscovery of previously confirmed UDGs in local groups and clusters (e.g., NGC 5846 and Fornax), and new discovered candidates in about 15 local systems, for a total of 67 bona fide associations. Besides the value of the catalog per se for future studies of UDG properties, this work shows the effectiveness of an iterative approach to training deep learning tools in presence of poor training samples, due to the paucity of confirmed UDG examples, which we expect to replicate for upcoming all-sky surveys such as those by the Rubin Observatory, Euclid, and the China Space Station Telescope.