J/A+A/699/A232 GOBLIN. dwarf galaxy candidates in UNIONS (Heesters+, 2025)
Galaxies OBserved as Low-luminosity Identified Nebulae (GOBLIN): Catalog of
43000 high-probability dwarf galaxy candidates in the UNIONS survey.
Heesters N., Chemaly D., Mueller O., Sola E., Fabbro S., Ferreira A.,
McConnachie A.W., Magnier E., Hudson M.J., Chambers K., Hammer F.,
Sanchez-Janssen R.
<Astron. Astrophys. 699, A232 (2025)>
=2025A&A...699A.232H 2025A&A...699A.232H (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies, nearby ; Galaxies, optical ; Galaxy catalogs ; Surveys
Keywords: methods: observational - techniques: image processing - catalogs -
surveys - galaxies: abundances - galaxies: dwarf
Abstract:
The detection of low surface brightness galaxies beyond the Local
Group poses significant observational challenges, yet these faint
systems are fundamental to our understanding of dark matter,
hierarchical galaxy formation, and cosmic structure. Their abundance
and distribution provide crucial tests for cosmological models,
particularly regarding the small-scale predictions of LambdaCDM. We
present a systematic detection and classification framework for
unresolved dwarf galaxy candidates in the large-scale Ultraviolet Near
Infrared Optical Northern Survey (UNIONS) imaging data. The main
survey region covers 4861deg2. Our pipeline preprocesses UNIONS data
in three (gri) of the five bands (ugriz), including binning, artifact
removal, and stellar masking before employing the software MTObjects
(MTO) to detect low surface brightness objects. After parameter cuts
using known dwarf galaxies from the literature and cross-matching
between the three bands, we are left with an average of ∼360
candidates per deg^2. With ∼4000deg2 in g, r and i, this amounts to
∼1.5 million candidates that form our GOBLIN (Galaxies OBserved as
Low-luminosity Identified Nebulae) catalog. For the final
classification of these candidates, we fine-tuned the deep learning
model Zoobot, which was pre- trained based on labels from the Galaxy
Zoo project. We created our training dataset by visually inspecting
dwarf galaxy candidates from existing literature catalogs within our
survey area and assigning probability labels based on averaged expert
assessments. This approach captures both consensus and uncertainty
among experts. Applied to all detected MTO objects, our method
identifies 42965 dwarf galaxy candidates with probability scores >0.8,
of which 23072 have probabilities exceeding 0.9. The spatial
distribution of high-probability candidates reveals a correlation with
the locations of massive galaxies (log(M*/M☉)≥10) within
120Mpc. While some of these objects may have been previously
identified in other surveys, we present this extensive catalog of
candidates, including their positions, structural parameter estimates,
and classification probabilities, as a resource for the community to
enable studies of galaxy formation, evolution, and the distribution of
dwarf galaxies in different environments.
Description:
This work is based on data in the g, r, and i bands from the
Ultraviolet Near Infrared Optical Northern Survey (UNIONS, Gwyn+,
2025). The data includes observations obtained by the MegaCam camera
on the CFHT, the Hyper Suprime-Cam on the Subaru Telescope and the
Pan-STARRS telescopes. This catalog contains 1478733 objects detected
by the MTObjects (MTO; Teeninga et al., 2015, ISMM, Springer, 157-168;
2016, MMTA, 1) software with a probability of being a dwarf galaxy
assigned by the fine-tuned deep learning model Zoobot (pmodel column).
The catalog also includes structural parameter estimates derived from
MTO that were measured on the 4x4 binned images. The flux was scaled
by a factor of 16 to account for this binning effect and magnitude and
mean effective suface brightness were derived from this scaled value.
As shown in Appendix E in the paper, these parameters should only be
considered as rough estimates, as the software uses a simplified
approach to estimate the effective radius and therefore the mean
effective surface brightness. The magnitude estimates are more
reliable, but show a bias, which was corrected in the g-band (gmagcor
column) and r-band (rmagcor column) via comparison with literature
measurements of known objects.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table3.dat 496 1478733 GOBLIN catalog: dwarf galaxy candidates in UNIONS
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See also:
J/ApJ/933/47 : The ELVES survey photometric results (Carlsten+, 2022)
J/MNRAS/506/5494 : MATLAS dwarfs structure and morphology (Poulain+, 2021)
J/ApJS/267/27 : SMUDGes V. The complete catalog of UDGs from DR9
(Zaritsky+, 2023)
J/ApJ/907/85 : The SAGA Survey. II. Satellite systems around galaxies
(Mao+, 2021)
J/ApJS/265/57 : Early-type dwarf galaxies in the local universe
(Paudel+, 2023)
https://www.skysurvey.cc : UNIONS survey website
Byte-by-byte Description of file: table3.dat
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Bytes Format Units Label Explanations
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1- 22 A22 --- Name GOBLIN source designation
(GOBLIN JHHMMSS.s+DDMMSS; J2000)
24- 31 F8.4 deg RAdeg Right ascension (J2000)
33- 39 F7.4 deg DEdeg Declination (J2000)
41- 59 A19 --- IDlit Identifier from literature catalog if
previously known
61- 78 A18 --- Source Source catalog of the previously known
dwarf galaxy (1)
80- 84 F5.3 --- pmodel Probability of being a dwarf galaxy
from ML model
86- 91 F6.3 --- plabel [0/1]? Visual classification label
from training set (2)
93- 99 F7.2 deg PAr ? Position angle in r-band (3)
101-112 F12.2 --- Fluxr ? Total flux in r-band (in ADU unit) (4)
114-121 F8.2 --- mumaxr ? Maximum pixel value in r-band
(in ADU unit)
123-128 F6.2 --- mumedr ? Median pixel value in r-band
(in ADU unit)
130-135 F6.2 --- mumeanr ? Mean pixel value in r-band (in ADU unit)
137-142 F6.2 arcsec Rer ? Effective radius in r-band (in ADU unit)
144-149 F6.2 arcsec RFWHMr ? FWHM radius in r-band
151-156 F6.2 arcsec R10r ? Radius enclosing 10% of light in r-band
158-163 F6.2 arcsec R25r ? Radius enclosing 25% of light in r-band
165-170 F6.2 arcsec R75r ? Radius enclosing 75% of light in r-band
172-178 F7.2 arcsec R90r ? Radius enclosing 90% of light in r-band
180-186 F7.2 arcsec R100r ? Radius enclosing 100% of light in
r-band (6)
188-194 F7.2 arcsec ar ? Major axis in r-band
196-201 F6.2 arcsec br ? Minor axis in r-band
203-207 F5.2 --- b/ar ? Axis ratio (B/A) in r-band
209-214 F6.2 mag rmag ? Apparent magnitude in r-band
216-221 F6.2 mag rmagcor ? Corrected apparent magnitude
in r-band (5)
223-228 F6.2 mag/arcsec2 SuBrer ? Mean effective surface brightness in
r-band
230-236 F7.2 deg PAg ? Position angle in g-band (3)
238-247 F10.2 --- Fluxg ? Total flux in g-band (in ADU unit) (4)
249-255 F7.2 --- mumaxg ? Maximum pixel value in g-band
(in ADU unit)
257-261 F5.2 --- mumedg ? Median pixel value in g-band
(in ADU unit)
263-267 F5.2 --- mumeang ? Mean pixel value in g-band (in ADU unit)
269-274 F6.2 arcsec Reg ? Effective radius in g-band
276-281 F6.2 arcsec RFWHMg ? FWHM radius in g-band
283-287 F5.2 arcsec R10g ? Radius enclosing 10% of light in g-band
289-294 F6.2 arcsec R25g ? Radius enclosing 25% of light in g-band
296-301 F6.2 arcsec R75g ? Radius enclosing 75% of light in g-band
303-308 F6.2 arcsec R90g ? Radius enclosing 90% of light in g-band
310-315 F6.2 arcsec R100g ? Radius enclosing 100% of light in
g-band (6)
317-323 F7.2 arcsec ag ? Major axis in g-band
325-330 F6.2 arcsec bg ? Minor axis in g-band
332-336 F5.2 --- b/ag ? Axis ratio (B/A) in g-band
338-343 F6.2 mag gmag ? Apparent magnitude in g-band
345-350 F6.2 mag gmagcor ? Corrected apparent magnitude in
g-band (5)
352-357 F6.2 mag/arcsec2 SuBreg ? Mean effective surface brightness in
g-band
359-365 F7.2 deg PAi ? Position angle in i-band (3)
367-378 F12.2 --- Fluxi ? Total flux in i-band (in ADU unit) (4)
380-387 F8.2 --- mumaxi ? Maximum pixel value in i-band
(in ADU unit)
389-394 F6.2 --- mumedi ? Median pixel value in i-band
(in ADU unit)
396-401 F6.2 --- mumeani ? Mean pixel value in i-band (in ADU unit)
403-408 F6.2 arcsec Rei ? Effective radius in i-band
410-415 F6.2 arcsec RFWHMi ? FWHM radius in i-band
417-422 F6.2 arcsec R10i ? Radius enclosing 10% of light in i-band
424-429 F6.2 arcsec R25i ? Radius enclosing 25% of light in i-band
431-436 F6.2 arcsec R75i ? Radius enclosing 75% of light in i-band
438-443 F6.2 arcsec R90i ? Radius enclosing 90% of light in i-band
445-451 F7.2 arcsec R100i ? Radius enclosing 100% of light in
i-band (6)
453-459 F7.2 arcsec ai ? Major axis in i-band
461-466 F6.2 arcsec bi ? Minor axis in i-band
468-472 F5.2 --- b/ai ? Axis ratio (B/A) in i-band
474-479 F6.2 mag imag ? Apparent magnitude in i-band
481-486 F6.2 mag/arcsec2 SuBrei ? Mean effective surface brightness in
i-band
488-494 A7 --- Tile UNIONS tile identifier
496 I1 --- Train [0/1] Flag indicating if object was used to
train the model (7)
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Note (1): References for the source catalogs are as follows:
dEs Local Universe = Paudel et al., 2023ApJS..265...57P 2023ApJS..265...57P, J/ApJS/265/57
ELVES = Carlsten et al., 2022ApJ...933...47C 2022ApJ...933...47C, J/ApJ/933/47
MATLAS = Poulain et al., 2021MNRAS.506.5494P 2021MNRAS.506.5494P, J/MNRAS/506/5494
NGC5485 UDGs = Merritt et al., 2014ApJ...787L..37M 2014ApJ...787L..37M
SAGA = Mao et al., 2021ApJ...907...85M 2021ApJ...907...85M, J/ApJ/907/85
SMUDGES = Zaritsky et al., 2023ApJS..267...27Z 2023ApJS..267...27Z, J/ApJS/267/27
Note (2): We obtained this label by averaging the visual classifications of 4
experts who classified all the objects in the training set 3 times.
The possible individual labels were 0 (non-dwarf), 0.5 (unsure), and 1 (dwarf).
Note (3): Major axis position angle in degrees, measured from North to East
[0, 180].
Note (4): The flux measurements were derived from 4x4 binned images. Here we
report the total flux in ADU, which has been scaled by a factor of 16 to
account for this binning.
Note (5): The corrected apparent magnitude was derived by comparing the measured
flux with literature values of known dwarf galaxies in the survey
area. The correction was applied to the g-band and r-band magnitudes.
Note (6): Radius of a circle with an area equivalent to the total area of all
pixels assigned to an object by MTO.
Note (7): Objects with Train=1 were used to train the model.
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Acknowledgements:
Nick Heesters, nick.heesters(at)epfl.ch
References:
Walmsley et al., 2023JOSS....8.5312W 2023JOSS....8.5312W, Zoobot paper
https://github.com/mwalmsley/zoobot : Zoobot GitHub repository
https://github.com/CarolineHaigh/mtobjects : Python implementation of
MTObjects
https://gitlab.com/nick-main-group/dwarforge : Code for the detection and
classification pipeline that produced this catalog
https://github.com/heesters-nick/DwarfClass : Visual classification tool used
to generate the training dataset
(End) Patricia Vannier [CDS] 04-Jun-2025