J/A+A/707/A354 KiDS UDG HQ catalog (Su+, 2026)
Ultra-diffuse galaxies in the Kilo-Degree Survey with deep learning.
Su H., Li R., Napolitano N.R., Yi Z., Tortora C., Su Y., Kuijken K.,
Chen L., Li R., Ragusa R., Li S., Dong Y., Radovich M., Wright A.H.,
Covone G., Zhong F.
<Astron. Astrophys. 707, A354 (2026)>
=2026A&A...707A.354S 2026A&A...707A.354S (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies ; Photometry ; Optical ; Morphology
Keywords: galaxies: dwarf - galaxies: structure
Abstract:
Ultra-diffuse Galaxies (UDGs) are a subset of Low Surface Brightness
Galaxies (LSBGs), showing mean effective surface brightness fainter
than 24mag/arcsec2 and a diffuse morphology, with effective radii
larger than 1.5kpc. Due to their elusiveness, traditional methods are
challenging to be used over large sky areas. Here we present a catalog
of ultra-diffuse galaxy (UDG) candidates identified in the full
1350deg2 area of the Kilo-Degree Survey (KiDS) using deep learning.
In particular, we use a previously developed network for the detection
of low surface brightness systems in the Sloan Digital Sky Survey
(LSBGnet, Su et al. 2024) and optimised for UDG detection. We train
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, among which, after visual inspection, we have selected 545
high-quality ones. The catalog contains independent re-discovery 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 like Rubin Observatory, Euclid and the China Space
Station Telescope.
Description:
We present a catalog of high-quality ultra-diffuse galaxy (UDG)
candidates identified in the Kilo-Degree Survey (KiDS Data Release 5)
using a dedicated deep-learning framework (UDGnet-K). From an initial
set of detections, we provide a visually inspected Grade-A subsample
of 545 objects. Structural parameters, including circular effective
radius and r-band mean effective surface brightness, are derived using
growth-curve photometry. Photometric redshifts are not included due to
their large uncertainties at low redshift. This catalog is intended
for statistical studies and follow-up observations of the
ultra-diffuse galaxy population.
This catalog provides the high-quality (Grade A) ultra-diffuse galaxy
sample detected in KiDS DR5 using deep learning. All objects satisfy:
Re between 3 arcsec and 20 arcsec
Mean effective surface brightness <mue>r_>23.8mag/arcsec2
Each object has been visually inspected by eight independent
classifiers and assigned a final grade based on the mean score.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table1.dat 88 545 High-quality UDG catalog (Grade A)
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Byte-by-byte Description of file: table1.dat
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Bytes Format Units Label Explanations
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1- 43 A43 --- Name Object identifier
45- 54 F10.6 deg RAdeg Right Ascension (J2000) (1)
56- 63 F8.4 deg DEdeg Declination (J2000) (1)
65- 69 F5.2 mag/arcsec2 muer Mean effective surface brightness (r) (2)
71- 75 F5.2 arcsec Re Circular effective radius (2)
77- 81 F5.2 --- MeanScore Mean visual score (0-10)
83- 86 F4.2 --- s_MeanScore Standard deviation of visual scores
88 A1 --- Grade [A] Final grade (A only)
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Note (1): Coordinates are given in ICRS J2000 system.
Note (2): All measurements are derived from r-band growth-curve photometry.
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Acknowledgements:
Hao Su, hao.su(at)unina.it,
Nicola R. Napolitano, nicolarosario.napolitano(at)unina.it
This work is based on observations obtained as part of the Kilo-Degree
Survey (KiDS) using the VLT Survey Telescope (VST) at ESO Paranal
Observatory.
(End) Patricia Vannier [CDS] 28-Nov-2025