Astronomy
&Astrophysics
A&A, 697, A38 (2025)
https://doi.org/10.1051/0004-6361/202451641
© The Authors 2025
The Complete Spitzer Survey of Stellar Structure
in Galaxies (CS4G)⋆
P. M. Sánchez-Alarcón1,2,⋆⋆ , H. Salo3 , J. H. Knapen1,2 , S. Comerón2,1 , J. Román1,2,4 , A. E. Watkins5 ,
R. J. Buta6, S. Laine7 , J. M. Falcón-Ramírez2,1, M. Anetjärvi3,8, E. Athanassoula9 , A. Bosma9 , D. A. Gadotti10 ,
J. L. Hinz11 , L. C. Ho12,13 , B. W. Holwerda14 , J. Janz15,3,16, T. Kim17 , J. Koda18 , J. Laine3,19 ,
E. Laurikainen3 , B. F. Madore20,21 , K. Menéndez-Delmestre22 , R. F. Peletier23 , M. Querejeta24 , A. Ruokanen3,
K. Sheth25 , and D. Zaritsky26
(Affiliations can be found after the references)
Received 24 July 2024 / Accepted 28 February 2025
ABSTRACT
Context. The Spitzer Survey of Stellar Structure in Galaxies (S4G), together with its Early Type Galaxy (ETG) extension, stands as
the most extensive dataset of deep uniform mid-infrared (mid-IR; 3.6 and 4.5µm) imaging for a sample of 2817 nearby (d < 40Mpc)
galaxies. However, the velocity criterion used to select the original sample results in an additional 422 galaxies without H I detection
that should have been included in the S4G on the basis of their optical recession velocities.
Aims. In order to create a complete magnitude-, size-, and volume-limited sample of nearby galaxies, we collected 3.6µm and i-band
images using archival data from different surveys and complemented it with new observations for the missing galaxies. Since most, but
not all, of these galaxies have a Hubble type in Hyperleda THL > 0, we denote the sample of these additional galaxies as disc galaxy
(DG) extension. We present the Complete Spitzer Survey of Stellar Structure in Galaxies (CS4G), encompassing a sample of 3239
galaxies (S4G+ETG+DG) with consistent imaging, surface brightness profiles, photometric parameters, and revised morphological
classification.
Methods. Following the original strategy of the S4G survey, we produced masks, surface brightness profiles, and curves of growth
using masked 3.6µm and i-band images. From these profiles, we derived the integrated quantities, including total magnitude, stellar
mass, concentration parameter, and galaxy size, converting between optical i-band and 3.6µm. We also re-measured these parameters
for the S4G and ETG to create a homogenous sample. We present new morphologically revised T -types, and we showcase mid-IR
scaling relations for the stellar mass, galaxy size, concentration index, and morphological type.
Results. Our new masking procedure increases the number of pixels masked out by a factor of five, improving the masking of fainter
regions over previous S4G data. Our photometric parameters from i-band imaging yield measurements consistent with the original
sample (S4G) and its ETG extension in the 3.6µm band. The new DG extension consists of galaxies with a wide morphological range
(−5 < THL < 10) and a mass range of 6 < log(M∗/M⊙) < 11. The galaxies in the DG sample have an average mass of log(M∗/M⊙) =
9.21, an average galaxy isophotal radius at 25.5mag arcsec−2 of R25.5 = 7.1 kpc, and an average concentration index of C82 = 2.92.
Conclusions. We completed the S4G sample by incorporating 422 galaxies into the original dataset. The new galaxies constitute 15%
of the total previous sample (S4G+ETG), but in the lower-mass range (M∗ < 109 M⊙), and the disc galaxy extension increases the
sample by 36%. The CS4G includes at least 99.94% of the complete sample of nearby galaxies, meeting the original selection criteria
based on a comparison with the NED database. We make the images and surface brightness profiles available to the community together
with the conjunct catalogue of the whole CS4G dataset with consistent photometric measurements for 3239 galaxies. The CS4G will
enable a wide set of investigations into galaxy structure and evolution, and it will complement the optical, near-IR, and mid-IR imaging
that will obtained in the coming years with Euclid, Rubin, Roman, and other research projects.
Key words. catalogs – surveys – galaxies: general – galaxies: photometry – galaxies: stellar content – galaxies: structure
1. Introduction
The local Universe offers an essential sample for studying galaxy
formation and evolution. Most local galaxies have a substantial
angular size (e.g. Jarrett et al. 2019; Moustakas et al. 2023),
which facilitates detailed decompositions and morphological
classifications. This also enables a comprehensive exploration
⋆ The authors of this paper seek to express deep gratitude for the
discussions and expertise from the late Dr. Tom Jarrett, a pioneer-
ing infrared astronomer who made invaluable contributions to 2MASS,
S4G, WISE and other infrared studies.
⋆⋆ Corresponding author: pmsa.astro@gmail.com
of the roles played in galaxy evolution by structural components
such as bulges, bars, rings, or lenses.
The Spitzer Survey of Stellar Structure in Galaxies (S4G;
Sheth et al. 2010) is the most extensive dataset of deep uni-
form mid-infrared (mid-IR) imaging for a sample restricted by
volume, magnitude, and size (d < 40Mpc, |b| > 30◦,mB, corr <
15.5, and D25 > 1′). The S4G is a survey of 2352 galaxies
observed in the mid-IR with the 3.6µm and 4.5µm channels of
the infrared array camera (IRAC; Fazio et al. 2004) of the Spitzer
Space Telescope (Werner et al. 2004). IRAC was an infrared
camera that simultaneously produced 5.′2 × 5.′2 images in two
bands, either in 3.6 and 4.5 µm or 5.8 and 8.0 µm, with a pixel
scale of 1.′′2. After the cryogenic phase, during the warm mission
A38, page 1 of 18
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Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
of Spitzer, only channels 1 (3.6µm) and 2 (4.5µm) continued
to be operational because they were less affected by the higher
operating temperature. The 3.6µm and 4.5µm wavelengths are
much less affected by dust than optical bands, and thus galaxies
imaged in those bands have a nearly constant mass-to-light ratio
(M/L) that only varies slightly with age and metallicity. As a
result, the 3.6 and 4.5µm bands are ideal for measuring stellar
masses of galaxies (see Meidt et al. 2014; Querejeta et al. 2015).
Studying these galaxies has contributed to our comprehension of
the impact of gas on bar formation (Comerón et al. 2018; Díaz-
García et al. 2019), the mechanisms behind disc breaks and trun-
cations (Laine et al. 2014a, 2016; Comerón et al. 2012), thin and
thick discs (Comerón et al. 2011, 2018), the nature of resonance
rings (Comerón et al. 2014), morphological features (Laine et al.
2014b; Buta et al. 2015), and the influence of the environment
on the gas content of disc galaxies (Laine et al. 2014a; Watkins
et al. 2019). The S4G plays a crucial role in studying the evo-
lution of galaxies, having led to the publication of numerous
research articles over the years (see Watkins et al. 2022, here-
after, W2022, for a review). Recent studies (e.g, Cuomo et al.
2023; Menéndez-Delmestre et al. 2024) continue to emphasise
the ongoing relevance of S4G and reinforce its enduring impact.
However, the original S4G sample suffered from a bias due to
the velocity-based selection criterion for distance. The selection
was based on H I measurements, which resulted in exclud-
ing gas-poor galaxies. Sheth et al. (2013) overcame this bias
using optical-band spectroscopic redshifts for early-type galaxies
(ETGs) with HyperLeda morphological types THL ≤ 0, incorpo-
rating 465 new Spitzer observations for these missing galaxies
into the survey (published by W2022). The original survey and
its extension (S4G+ETG) contain 2817 galaxies. Nonetheless,
the T -type criterion for the ETGs (THL ≤ 0) does not include
391 late-type, 20 lenticular, and 11 ETGs (based on HyperLeda
types) that lack radio-based velocities but meet the distance cri-
terion using optical velocities, as well as the other S4G selection
criteria. These 422 missed galaxies form the disc galaxy (DG)
extension described here.
To complete the survey with these 422 missing galaxies, we
collected archival IRAC images and optical i-band images from
several surveys (see Sect. 2). We complemented these data with
new observations for 18 galaxies lacking archival images. We
followed the original strategy of the S4G survey by conducting
the same photometric analysis applied to the previous exten-
sion of 465 ETGs by W2022. We applied the methods described
in the S4G Pipelines 2 and 3 (P2 and P3; see Muñoz-Mateos
et al. 2015, hereafter MM2015). This analysis includes mask-
ing images; producing intensity and surface brightness profiles;
obtaining position angle and ellipticity profiles; and computing
total magnitudes, stellar masses, measures of central concentra-
tion, and measures of galaxy size. MM2015 extensively outlined
the applications of this structural analysis, all of which are
still relevant to this extended sample of gas-poor galaxies that
includes mostly disc and dwarf galaxies.
In the current landscape of extensive deep surveys, which
are notably focused on exploring vast cosmic volumes (e.g. the
Hyper-Suprime Cam Subaru Strategic Program, the Vera C.
Rubin Observatory’s Legacy Survey of Space and Time, the
Roman Space Telescope, and the Euclid Mission; Aihara et al.
2018; Ivezic´ et al. 2019; Spergel et al. 2015; Laureijs et al. 2010),
a complete census of late-type galaxies at redshift z = 0 becomes
crucial for comparisons between young and present-day galaxies.
In this work, we present the Complete Spitzer Survey of
Stellar Structure in Galaxies (CS4G) by adding an extension of
422 galaxies that completes the final sample of 3239 galaxies.
The purpose of this complete dataset is to serve as the basis
for future analyses, including a detailed analysis of morphol-
ogy and multi-component decompositions. In Sect. 2, we give
an overview of the surveys and instruments used for the acquisi-
tion of the infrared and i-band images. In Sect. 3, we describe
the completeness of the CS4G sample. In Sect. 4, we review
the methods used for the photometric analysis, the creation
of masks, and the sky background estimation. In Sect. 5, we
present the empirical photometric conversion between i-band
and 3.6µm. In Sect. 6, we explain the radial and integrated
photometric parameters derived from the images. In Sect. 7,
we explore the scaling relationships among the photometric
parameters within this new sample. We list the conclusions of
our results in Sect. 8. Finally, in Sect. 8, we explain the data
products released with this work. Throughout, we use the AB
photometric system, and we assume the ΛCDM model with
a Hubble-Lemaître constant H0 = 751 km s−1 Mpc−1 and a
matter density parameter Ωm = 0.3. The Morphological types
used throughout the paper are either HyperLeda morpholog-
ical types, denoted as THL, or revised T -types presented in
Sect. 6, denoted as T , which are in the Comprehensive de
Vaucouleurs revised Hubble-Sandage (CVRHS) system (Buta
et al. 2015).
2. S4G disc galaxy extension sample, observations,
and origin of archival data
The S4G DG extension sample, as well as the ETG one (W2022),
adhere to the same selection criteria as the main S4G project.
These criteria include galaxies listed in the HyperLeda2 database
(Makarov et al. 2014) with radial velocities v < 3000 km/s,
total extinction-corrected blue magnitudes mB,corr < 15.5, blue
isophotal diameters 1′ < D25 < 30′, and Galactic latitude |b| >
30◦. Thus, the CS4G sample excludes nearby galaxies with
angular diameters larger than 30′ (LMC, SMC, M33, and UMi
Dwarf), and the largest galaxy included is NGC 0055 with a
diameter of 29.′8.
The original sample exclusively relied on H I-derived veloci-
ties (Sheth et al. 2010), which introduced a bias towards gas-rich
late type galaxies. To rectify this bias in the sample, the ETG
extension (Sheth et al. 2013; Watkins et al. 2022) incorporates
galaxies with morphological types THL ≤ 0 and optical-band
spectroscopic velocities. Nevertheless, this limitation to early
types still left out many galaxies (see Appendix A for more
details): a new search on the HyperLeda database in 2018 led to
a list of 422 galaxies with visual-band spectroscopic velocities
and meeting the original S4G criteria, forming our DG sample
(see Appendix B for comments on the exclusion of some galax-
ies). Of these, 391 galaxies have morphological types THL ≥ 0,
20 galaxies with −3 ≤ THL < 0, and 11 galaxies with THL < −3.
The DG sample consists of these 422 galaxies selected from the
HyperLeda database that lacked H I derived velocities.
To keep consistency with the original survey, we collect
3.6µm and 4.5µm band imaging from the Spitzer Heritage
Archive, maintaining the original photometric bands of the sur-
vey. When these bands were not available, we used i-band
imaging from different surveys. Despite its 3.4µm band being
similar to IRAC 3.6µm, we decided not to use WISE due to its
worse angular resolution. We find 55 mid-IR images from the
Spitzer Heritage Archive, 11 of which are from S4G and ETG
footprints, and 367 i-band images. For i-band imaging, we used
1 We chose this value to be consistent with Sheth et al. (2010).
2 http://leda.univ-lyon1.fr/
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Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
0
250
N
ga
l
0
250
N
ga
l
0
250
N
ga
l
−5 0 5 10
THL-type
0
250
N
ga
l
0 1000 2000 3000
vopt [km/s]
0 10 20 30 40
Distance [Mpc]CS4G S4G ETG DG
Fig. 1. HyperLeda morphological type (THL, left) and optical velocity distribution (right) for all the samples. These values are queried from
HyperLeda. From top to bottom, each row shows the distribution of the CS4G (grey), original S4G (blue), ETG (orange), and DG (purple) samples,
respectively.
Table 1. Survey and instrument properties.
Survey Galaxies Scale FWHM Depth
IRAC 44 0.′′600 2.′′1 26.8
IRAC (a) 11 0.′′750 2.′′1 26.8
DES 102 0.′′263 0.′′9 28.4
LS 169 0.′′263 (b) 1.′′0 27.9
SDSS 77 0.′′396 1.′′1 26.4
LT 15 0.′′150 1.′′4 28.3
NTT 3 0.′′241 1.′′2 27.6
HST 1 0.′′050 0.′′1 27.5
Notes. The depth column represents the surface brightness limit
[3σ, 10′′ × 10′′] in units of mag arcsec−2. (a)For this set of galaxies, the
IRAC images are from S4 G fields. (b)For 41 galaxies, the mosaics have
a pixel scale of 0.′′269.
the Dark Energy Survey DR2 (Abbott et al. 2021) for 102 galax-
ies, the DESI Legacy Imaging Surveys DR10 3 (hereafter Legacy
Surveys, Dey et al. 2019) for 169 galaxies, the SDSS DR12
(York et al. 2000; Alam et al. 2015) for 77 galaxies and archival
data from the Hubble Space Telescope for one galaxy. For the
remaining 18 galaxies we obtain i-band images with the Liver-
pool Telescope (LT) for 15 northern hemisphere galaxies, and
with the New Technology Telescope (NTT) for three galaxies in
the southern hemisphere.
We summarise the characteristics of the surveys and instru-
ments in Table 1. We show the number of images of each survey
used in this extension, the pixel scale, the average full width at
3 https://legacysurvey.org/dr10
half maximum (FWHM) spatial resolution, and the average sur-
face brightness depth [3σ, 10′′ × 10′′], measured as explained
by Román et al. (2020). In Fig. 1 we show the morphological
distribution (left column using Hyperleda types THL) and the
distance distribution (right column) for the whole CS4G (grey),
the original S4G (blue), the ETG (orange), and the DG (purple)
samples.
2.1. Spitzer Heritage Archive
We selected sample galaxies observed with the Spitzer IRAC
camera from the Spitzer Heritage Archive, either from the cryo-
genic or the warm phase of the mission. We found 55 galaxies
with 3.6µm and 4.5µm IRAC observations. Of these 55 galax-
ies, 11 were observed in fields of the original S4G survey.
These images have the same properties as those in the S4G
survey (Sheth et al. 2010), with 0.′′75/pixel and similar depth.
The remaining 44 had observations from different proposals,
or mosaics from the Spitzer Enhance Image Products with a
pixel scale of 0.′′60/pixel. The proposal IDs and the Region ID
of the frames and mosaics can be found in the headers of the
images. Two galaxies, NGC 4516 and NGC 4431, have only
4.5µm observations available.
2.2. Dark Energy Survey
The Dark Energy Survey (The Dark Energy Survey
Collaboration 2005) is a southern hemisphere imaging sur-
vey that aims to probe the origin of the accelerating Universe
and help uncover the nature of dark energy. The survey is based
on optical and mid-infrared imaging with the Dark Energy
Camera (DECam; Flaugher et al. 2015) mounted on the 4 m
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Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
Blanco telescope at Cerro Tololo Inter-American Observatory
in Chile, covering 5000 deg2. We identified 102 galaxies of the
DG sample with i-band images in the second data release of
DES (DR2; Abbott et al. 2021). The DECam has a pixel scale of
0.′′263, and the survey depth in the i-band is 23.8 mag (for a 1.′′95
diameter aperture at a S/N of ten) with a mean FWHM of 0.′′88.
We accessed the images via the DESaccess4 web application.
We used the image cutout service to download images for
102 galaxies, centred on the galaxy, with size 12 × 12 arcmin2.
2.3. DESI Legacy Imaging Surveys
The DESI Legacy Imaging Surveys (Dey et al. 2019) are a
combination of three public projects: the Dark Energy Camera
Legacy Survey, the Beijing–Arizona Sky Survey, and the May-
all z-band Legacy Survey. In its latest data release (DR10), the
combined surveys jointly cover ∼20 000 deg2. This release incor-
porates additional DECam data from NOIRLab mostly from
DES (Abbott et al. 2021), the DELVE Survey5 (Drlica-Wagner
et al. 2021), and the DECam eROSITA Survey (DeROSITA;
Zenteno et al. 2025). There are 169 S4G-DG galaxies with i-band
imaging in the DESI Legacy Imaging Surveys DR10 that are not
in the DES DR2 survey. We collected images for these 169 galax-
ies using the Sky Viewer 6 URL cutout service, imposing a size
of ∼11 × 11 arcmin2 and at the original pixel scale 0.′′263.
2.4. Sloan Digital Sky Survey
The Sloan Digital Sky Survey (York et al. 2000) is probably
the most widely used northern hemisphere imaging and spec-
troscopic survey. We used the Data Release 12 (DR12; Alam
et al. 2015) Science Archive Server (SAS) to download indi-
vidual frames and create mosaics using Swarp (Bertin 2010).
Since DR12, no additional imaging has been incorporated into
SDSS. We created mosaics centred at the galaxy with a size of
6 × 6 arcmin2 for 75 galaxies, and for the galaxies NGC 4964
and UGC 8736, which have a larger angular size, the mosaics
are 18 × 18 arcmin2 in size.
2.5. Liverpool telescope
Of our S4G-DG sample, some galaxies lack mid-IR and i-band
imaging in public surveys. We observed 15 of these galaxies dur-
ing the nights of July 16, 2018, December 9, 12, and 14, 2018,
January 5 and 12, 2019, March 3 and 16, 2019, and April 11,
2019, with the Optical-Infrared IO:O camera (Barnsley et al.
2016) at the 2.0m Liverpool Telescope in the Observatorio del
Roque de los Muchachos (Steele et al. 2004). The IO:O camera
has a 4096 × 4112 pixel2 CCD, with a pixel scale of 0.′′15 and a
10 × 10 arcmin2 field-of-view.
2.6. New technology telescope
We observed three further missing galaxies in the southern
hemisphere lacking archival mid-IR and i-band imaging using
the EFOSC2 instrument (Buzzoni et al. 1984) at ESO’s 3.58m
New Technology Telescope (NTT) in Chile during the night of
August 11th, 2019. The EFOSC2 instrument is equipped with a
2048 × 2048 pixel CCD, and we used the 2 × 2 binning mode
4 https://des.ncsa.illinois.edu/desaccess/
5 https://datalab.noirlab.edu/delve
6 https://www.legacysurvey.org/viewer
resulting in a pixel scale of 0.′′2408 and a 4.13 × 4.13 arcmin2
field-of-view.
2.7. Hubble space telescope
There is existing archival data for one remaining galaxy in
the Hubble Legacy Archive (Proposal ID: 9395, PI: Marcella
Carollo; Carollo et al. 2007). NGC 2082 was observed with the
Advanced Camera for Surveys (ACS, Ford et al. 1998; Sirianni
et al. 2005) using the F814W (I-band) filter with the Wide
Field Channel (WFC) camera of the Hubble Space Telescope.
The pixel scale of the ACS/WFC camera is 0.′′050 providing a
3.4 × 3.4 arcmin2 field of view, sufficient to capture the entire
galaxy.
3. Complete Spitzer survey of stellar structure
in galaxies
We present CS4G, which is formed by the DG sample studied
in this work added to the original S4G (Sheth et al. 2010) and
the ETG extension (W2022). The CS4G includes 3239 galaxies
selected using the original S4G criteria (see Sect. 2).
To assess the completeness of the CS4G sample, we selected
galaxies mimicking the criterion used in HyperLeda from the
NASA/IPAC Extragalactic Database (NED; Chen et al. 2022).
We used the Objects with Parameter Constraints tool
from the NED website7 to select a NED comparison sample.
However, this tool does not allow for constraints on the galaxy
diameter. From the resulting selection queried by magnitude (B-
band magnitude brighter than 15.5), volume (redshift less than
0.01), type of object (galaxies) and sky area, we query the diam-
eters. We used an isophotal diameter at 25mag arcsec−2 when
available, or the median of all diameters available. We find 3005
galaxies that meet the criteria of the CS4G according to NED. Of
these, 295 are not in the CS4G. This list of galaxies includes the
LMC, SMC, M33 and UMi Dwarf galaxies that were excluded
from the original S4G due to their large angular size. We also
find four objects misclassified as galaxies in NED. SIMBAD
and visual inspection of the images show clearly that they are
globular clusters of the Milky Way (UGC 09792, ESO 118-
031, and Sextans C) and an emission region from the LMC
(ESO 056-019).
We cross-matched the remaining 287 galaxies with the
HyperLeda database updated to October 1, 2024. We find that
285 galaxies fail to meet one or more of the S4G criteria (B-
band magnitude, diameter, or velocity) according to HyperLeda.
Two galaxies (IC 3418 and ESO 084-12) do not have any velocity
measurement in HyperLeda, but the other parameters, magni-
tude, diameter and galactic latitude, meet the criteria. According
to NED, these two galaxies have redshifts well below the limit.
They could potentially be included in the CS4G. However, to be
consistent with the original S4G sample selection criteria, these
galaxies are not included since they do not have any velocity
measurements in HyperLeda.
Figure 2 shows the HyperLeda isophotal diameter at
25.5 mag arcsec−2 with respect to B-band magnitude for the
CS4G sample (blue points) and for a larger sample of galaxies
extracted from HyperLeda (grey dots). Grey points inside the
region of the CS4G are galaxies that fail other criteria while blue
points outside the region are galaxies from the original S4G that
the newer measurements in HyperLeda made them outside the
7 https://ned.ipac.caltech.edu/byparams
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Fig. 2. HyperLeda isophotal diameter at 25.5 mag arcsec−2 (from
logd25 parameter) in arcminutes with respect to the B-band magnitude
(btc parameter). Blue points represent the CS4G sample, and grey dots
represent a larger sample from HyperLeda. The vertical and horizon-
tal dashed lines represent the thresholds used to build the CS4G. There
are some cases where some galaxies in the CS4G quadrant are not real
galaxies meeting the criteria (as explained in Appendix B).
region. For the sake of completeness, we included them in the
CS4G. The CS4G describes the largest and brightest galaxies
of the local Universe followed by a smooth transition to fainter
galaxies as seen with the representation of a larger sample in
grey dots.
In conclusion, a comparison to NED identifications indicates
that there are no new galaxies that meet the original sample
selection criteria of the S4G. Thus, the completeness of the CS4G
is 100%. However, there are two potential galaxies (0.06% of the
sample) that could meet the criteria in the future if the veloc-
ity from NED is included and validated in HyperLeda. In this
sense, the CS4G contains at least 99.94% of galaxies of the local
Universe falling within the CS4G selection criteria.
4. Data preparation
The original S4G sample and the ETG extension were observed
with the Spitzer IRAC camera and thus have similar prop-
erties, such as the pixel scale (0.′′75), units of MJy sr−1 and
depth (µAB,lim ∼ 27mag arcsec−2), as described in MM2015 and
W2022. Since the DG extension was observed with a variety
of instruments, it does not share these technical details. We
adapted Pipeline 1 and 2 (P1 and P2, see MM2015) to produce
image mosaics for new observations and mask images for the
422 galaxy images in the disc galaxy sample. We follow the pro-
cedures used for the original S4G (MM2015, Salo et al. 2015) and
the ETG extension (W2022) to have consistent measurements
throughout the surveys. We explain in detail the differences in
the methods used in the following sections while summarising
the similarities to the previous analyses.
4.1. Pipeline 1. Mosaics
For the new LT and NTT observations we follow the procedures
in Pipeline 1 as described in MM2015 to achieve consistency
with the whole survey. The pipeline initially reduces all the
frames, subtracting the dark and bias and correcting the flat field.
Then, it aligns the background levels of individual exposures
by utilising overlapping regions. Subsequently, it combines all
frames following standard dither/drizzle procedures (Fruchter &
Hook 2002). The final mid-IR images are delivered in units of
MJy sr−1 and for the optical images in nanomaggies (Abazajian
et al. 2003) per pixel (the pixel size can be found in Table 1) with
a zero point of 22.5 mag arcsec−2.
Figure 3 shows examples of images of different galaxies from
the DG sample, the original S4G and the ETG extension. We
used images of the different surveys and instruments in the DG
sample to show their differences in angular resolution and depth
(see Table 1). We sort the galaxies according to their stellar mass
in the x-axis and to their morphological type in the y-axis (see
Sect. 6). The SDSS examples are the images with the noisiest
backgrounds, while the DES and LS imaging surveys have more
depth and a better angular resolution. When selecting galaxies
for each panel, we first look for galaxies within the DG sample,
and when they are not available, we select them from the origi-
nal S4G. For panels with multiple options, the galaxy shown is
chosen randomly. Blank spaces are regions where there are no
galaxies in the CS4G. Despite their differences, the set of images
used in the DG samples traces the stellar content of their galaxies
similarly to those used in the original S4G.
4.2. Pipeline 2. Masking
The Pipeline 2 of the S4G and the ETG extension pro-
duced masks of contaminating sources, including foreground
stars, background galaxies, and scattered light artefacts, using
SExtractor (Bertin & Arnouts 1996) with three different
thresholds. Originally, three different masks on the 3.6µm
mosaic images, with high, medium, and low detection thresh-
olds, were produced to identify sources both far from the tar-
get galaxy and overlapping in projection with the galaxy. We
adopt a similar strategy to produce masks for the DG sam-
ple using a combination of SExtractor(v.2.25.0) masks with
different thresholds and configurations to improve the detec-
tion of the lower S/N regions (i.e. low surface brightness
features).
We first smooth the image using the Bayesian noise reduction
technique FABADA (Sánchez-Alarcón & Ascasibar 2023) with
a standard deviation of the noise measure on the image with
hard sigma clipping (rejecting pixels above 2.5σ). We then com-
bine three different runs of SExtractor in the smoothed image.
For the first two, we vary the background size (BACK_SIZE),
and detection threshold (DETECT_THRESH). In the first iteration,
we run it with an intermediate threshold (DETECT_THRESH =
1.2) and large background size (BACK_SIZE > 50 × FWHM)
which produces good masking of extended regions in the
image at intermediate S/N regions. We then run SExtractor
again, now optimised for point source detection, using a lower
threshold (DETECT_THRESH = 0.9) and smaller background
size (BACK_SIZE < 10 × FWHM). We vary the background
size according to the image resolution (measured as FWHM)
to increase the detection of smaller point sources in higher-
resolution images.
In the last step, we aim to increase the detection of faint
point sources and deblend the sources inside the galaxy. We run
A38, page 5 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
-3
-6 PGC 041598 - LS
8.32 [-5.0]
PGC 974148 - LS
9.03 [-5.0]
NGC 1320 - IRAC
10.76 [-5.0]
NGC 1316 - IRAC
11.65 [-4.0]
IC 3459 - LS
7.80 [-3.0]
ESO 119-059 - DES
8.60 [-2.0]
NGC 4431 - IRAC
9.37 [-2.0]
NGC 5866 - IRAC
10.65 [-3.0]
NGC 7410 - IRAC
11.37 [-1.0]
4
0 ESO 241-006 - DES
8.96 [3.0]
UGC 10974 - SDSS
9.14 [2.0]
PGC 009485 - DES
10.18 [1.0]
NGC 3169 - S4G
11.00 [1.5]
18
20
22
24
26
28
µ
3
.6
µ
m
[m
ag
/a
rc
se
c2
]
UGC 06102 - LS
7.81 [8.0]
ESO 345-050 - DES
8.87 [7.0]
ESO 340-019 - LS
9.02 [7.0]
NGC 7400 - DES
10.31 [5.0]
NGC 3521 - S4G
11.01 [4.0]
PGC 1034827 - LS
7.89 [10.0]
11
9
7 8
PGC 046630 - SDSS
8.96 [10.0]
ESO 508-003 - LS
9.75 [9.0]
9 10
NGC 5713 - S4G
10.54 [9.0]
11 12
log(M∗/M¯)
C
V
R
H
S
ty
p
es
Fig. 3. Examples of images of galaxies from the CS4G sample. Galaxies are sorted according to their stellar mass (x-axis) and morphological type
(y-axis) measured as explained in Sect. 6. The galaxy name and the survey or instrument of the image are shown in the upper part of each image.
The stellar mass and the revised morphological type (in brackets) are shown in the bottom part of each image. Each image is centred on the galaxy
and the field of view is set to be 1.4 times the isophotal radius at 25.5 mag arcsec−2 (R25.5). The radius, R25.5, is measured in the 3.6µm band as
described in Sect. 6.
SExtractor on the residual of the image enhanced with FABADA
minus a Gaussian smoothed version of the image (with a kernel
width of the FWHM). This residual highlights the regions that
are brighter than the average of the surrounding area, i.e., the
most likely peaks of sources. Then we masked regions compat-
ible with being stars, masking rounder objects weighted by the
inverse of the distance to the centre of the galaxy (the closer to
the centre of the galaxy, the rounder the region must be to be
masked). As a final step, to improve the detection of the fainter
regions of the sources, which are more affected by the wings of
the point spread function (PSF), we dilate the final mask images
using a Gaussian kernel of size 3 × 3 pixel2 and σ = 1 pixel. We
visually inspect all the masks produced to confirm that all the
external sources are masked. When needed, we edit masks man-
ually to overcome some problematic results, and we make use of
colour images when available to disentangle bright regions of the
galaxy from other sources, usually higher-redshift sources with
redder colours.
Using the same method described here we compute a new
mask image for NGC 0936 from the original S4G. We show its
impact on the surface brightness profiles and background charac-
terisation in Fig. 4 where we compare the mask produced by the
original technique of the S4G and our new method on the galaxy
NGC 0936. The left panel shows the original 3.6µm band image
A38, page 6 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
200 0 −200 −400
∆α [arcsec]
−200
−100
0
100
200
300
400
∆
δ
[a
rc
se
c]
16 18 20 22 24 26 28
µ3.6µm [mag arcsec
−2]
15 20 25 30 35
µ3.6µm [mag/arcsec
2]
101
103
N
um
b
er
of
pi
xe
ls
NGC 0936
S4G Mask
New Mask
Sky
Galaxy
0 50 100 150 200 250
semi-major axis [arcsec]
20
30
µ
3
.6
µ
m
[m
ag
/a
rc
se
c2
]
S4G Mask
New Mask
LS
Fig. 4. Difference between the masking of the original S4G and our new method. The left panel shows the surface brightness map in the 3.6µm
band of the galaxy NGC 0936 from the original S4G image. The shaded region represents the original mask of the S4G, while the black contour
lines show the regions masked using the method explained in Sect. 4.2. The top right panel shows the histogram of the image (thin grey line) in
surface brightness and the different contributions of the pixels in the original S4G mask (orange), the pixels added to the mask with the new method
(purple), the pixels within the galaxy (green dotted line) and the sky pixels (black line), which are the pixels that are neither masked nor used to
create the profile of the galaxy. The bottom right panel shows the surface brightness profiles in mag arcsec−2 using the original mask (orange line)
and the new mask (purple dashed line). We also show the converted 3.6µm profile (using the recipe from Sect. 5) using the Legacy Surveys (LS)
imaging. The filled region represents the uncertainties due to the sky background subtraction.
of the galaxy. On top of the image, we show the original S4G
mask with darker shaded regions and the new mask computed
with our method using contours (black line). The top right panel
shows the histogram (thin grey line) of the image but with the
pixels converted to surface brightness and with the sky back-
ground subtracted. We show the contribution to the histogram of
the pixels masked by the original mask image (orange), the pix-
els added by our new mask (purple), and the pixels of the galaxy
(green dashed line). We determined the pixels of the galaxy as
those used to derive the radial surface brightness profile of the
galaxy (see Sect. 6) up to a distance of 250 arcsec where the pro-
file reaches the noise level. The remaining pixels that were not
masked and not used to construct the profile of the galaxy con-
stitute the background sky (thick black line). In the bottom right
panel, we show the radial surface brightness profile measured
using the original S4G mask and the new mask. We also show
the radial profile measured using the deepest and widest imag-
ing available, the i-band image of the Legacy Survey. We convert
the i-band profile to the 3.6µm band using our recipe described
in Sect. 5.
Compared with the original S4G, we increase the number of
pixels masked by almost a factor of five, from 5% on the orig-
inal mask to 24% in the new mask. The original S4G fails to
mask out the faint outskirts of the sources, with an increase of
unmasked pixels above ∼24mag arcsec−2, where both distribu-
tions cross (Fig. 4). This results in 89% of pixels with a surface
brightness fainter than 22 mag arcsec−2 which are not masked out
by the original S4G. When measuring the background value, we
obtain a difference of 0.4% between the old and new masks.
However, we decrease the error on the background value by
52% when using the new mask. This is also seen in the pro-
files panel (Fig. 4; bottom right), where the error on the profile
(the shaded region) is larger for the original S4G profile (orange)
than for the new one (purple). The integrated magnitude of the
galaxy we measure is 9.54 ± 0.04 and 9.55 ± 0.01mag using the
original S4G and new mask, respectively. Both values are com-
patible, with a small difference of 0.01mag but with an error
four times larger when using the original S4G mask. This effect
occurs because the boxes used to measure the background value
are contaminated with low surface brightness (LSB) pixels from
sources not properly masked when using the original S4G mask,
which increases the variance of the pixels, increasing the error
on the background value. However, not properly masking LSB
regions does not significantly affect the measured value of the
sky background. Our new method is more effective in masking
the outskirts of sources, i.e. detecting pixels down to a lower S/N
(LSB regions) than the original S4G method.
The three profiles shown in the bottom right panel match
up to a surface brightness of 26mag arcsec−2 when the profiles
start to diverge. The profiles from IRAC imaging (orange and
purple) reach the outskirts of the galaxy and extend down to
∼29–30 mag arcsec−2, which is expected according to its surface
brightness limit. However, they differ due to the mask used to
measure the profiles. Using the original S4G mask, we incor-
porate signal from contaminating sources which results in a
brighter tail on the profile. Despite its deeper surface bright-
ness limit (see Table 1), the Legacy Surveys fails to preserve
the outskirts of the galaxy. This is an effect of the Legacy
Surveys pipeline where the sky background is overestimated
and extended objects usually show signatures of destroyed LSB
regions. This oversubstraction problem has also been noticed in
other works (see Liu et al. 2023). However, this is limited to
LSB regions fainter than ∼27 mag arcsec−2 and therefore does
not affect our results.
A38, page 7 of 18
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6 8 10 12
log(M∗/M¯)
−2
−1
0
1
i
−
3.
6
µ
m
−5 0 5 10
T−type
−2
−1
0
1
i
−
3.
6
µ
m
Fig. 5. Optical to infrared conversion using galaxies in the original S4G sample. The left panel shows the i − 3.6µm colour versus the stellar mass
relationship. The right panel shows the i − 3.6µm colour plotted against the CVRHS revised T−types. To have a more homogeneous distribution
in the right-hand panel, we added a random offset according to the uncertainties of the morphological revised T -types as reported by Buta et al.
(2015), from a normal distribution with a width of their uncertainties, N(0, 0.52).
4.3. Pipeline 3. Sky background
We follow a similar procedure as that in Salo et al. (2015), using
a semi-automatic IDL routine to manually select regions that are
used to estimate the sky background, typically 20–30 locations
outside the visible galaxy. We avoid placing the boxes on the
image edges and in crowded areas. The local sky values in these
chosen locations are determined by calculating medians of non-
masked pixels within 30 pixel by 30 pixel boxes. Subsequently,
the global sky background (sky) and its uncertainty (DSKY) are
derived from the mean and standard deviation of these local
values, respectively.
5. Optical to infrared correction
Since the goal of the DG extension is to provide images that can
be used alongside those of the original S4G and the ETG exten-
sion, we need to convert from i-band to 3.6µm AB magnitudes.
As a benchmark for the calibration we used the 1388 S4G galax-
ies that have both an SDSS i-band image compiled in Knapen
et al. (2014) and a well-defined µ3.6µm = 26.5mag arcsec−2
isophote in MM2015. For each galaxy we derive i-band and
3.6µm asymptotic magnitudes with profiles obtained out to the
µ3.6 µm = 26.5mag arcsec−2 isophote while keeping the orienta-
tion parameters fixed to the values presented in MM2015. For
the 3.6µm images, we used the aperture correction described in
Eq. (1) of W2022. Pipeline 2 masks were accounted for while
producing the profiles.
Figure 5 shows the derived i − 3.6µm colours as a function
of stellar mass (left panel) and morphological T−type from Buta
et al. (2015) (right panel). We fitted the data with order-two poly-
nomials. The resulting relation between the colour and the stellar
mass is
i − 3.6µm = (0.052 ± 0.007)(log(M∗/M⊙))2
− (0.67 ± 0.13) log(M∗/M⊙) + (1.20 ± 0.61), (1)
and between the colour and the T−type
i − 3.6µm = − (0.0070 ± 0.0004)T 2
+ (0.009 ± 0.004)T − (0.152 ± 0.011). (2)
The two expressions are represented as continuous black curves
in their respective figures. The first expression has a root-mean-
square deviation of 0.17mag and the second one of 0.19mag.
We convert our i-band apparent magnitudes to 3.6 µm appar-
ent magnitudes using the mass relationship (Eq. (1)), which has
the lowest sum of the squares residual. Since we need the stel-
lar mass of a galaxy to estimate the i − 3.6µm colour and the
stellar mass is one of the parameters we want to derive from our
images, we apply an iterative procedure to estimate the colours.
We first assume that the colour i − 3.6µm = 0 and then esti-
mate the stellar mass of the galaxy (see Sect. 6.3). With this first
mass estimate, we calculate the i − 3.6µm colour using Eq. (1),
we transform the i-band magnitudes to 3.6µm and calculate the
mass with the 3.6µm magnitude again. We repeat this process
until we reach convergence in the i − 3.6µm colour. We reach
the convergence after 5 – 9 iterations to the fifth decimal value.
This uncertainty is well below our uncertainty in the integrated
magnitudes.
6. Derived quantities
We follow the original S4G procedures (MM2015, Salo et al.
2015) along with those in the ETG extension (W2022) to derive
photometric quantities. We measure radial profiles of flux and
surface brightness, position angle and ellipticity profiles for the
DG sample. For the whole set of galaxies in the CS4G, we mea-
sure asymptotic magnitudes, concentration indices, and sizes.
The parameters from the three samples are homogenised using
the same methods.
All the parameters derived from this analysis can be found
in an electronic table accessible from the CDS and IRSA (see
Sect. 8).
6.1. Radial profiles
To derive radial profiles, we used the implementation of the
iterative ellipse-fitting method described by Jedrzejewski (1987)
in the Photutils package (Bradley et al. 2022, v1.5.0) of
Astropy (Astropy Collaboration 2013, 2018, 2022, v5.1). This
method is efficient in tracing the structure of a galaxy, following
A38, page 8 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
50 0 −50
∆α [arcsec]
−60
−40
−20
0
20
40
60
∆
δ
[a
rc
se
c]
PGC 034407
50 0 −50
∆α [arcsec]
20.0 20.5 21.0 21.5 22.0 22.5 23.0 23.5 24.0
µ3.6µm [mag arcsec
−2]
20
25
30
35 µ
3
.6
µ
m
[m
ag
ar
cs
ec
−2
]
log(M∗/M¯) = 8.50 , m3.6µm = 15.28 , i− 3.6µm= 0.74
IRAC
LS
SDSS
0 20 40 60
semi-major axis [arcsec]
−2.5
0.0
2.5
∆
µ
3
.6
µ
m
Fig. 6. Surface brightness maps and profiles. The left panel shows the surface brightness map of PGC 34407 in the 3.6µm band. The middle panel
shows the surface brightness map overlaid with the mask image with shaded regions, and the elliptical apertures used to measure the radial profile.
The top right panel shows the radial surface brightness profile derived using the 3.6µm band (black curve), the i-band from the Legacy Imaging
Surveys (purple circles), and i-band from the SDSS (orange diamonds). The radial profiles of both the i-band images have been converted to 3.6µm
using the recipe described in Sect. 5. The lower right panel shows the difference between the mid-IR profile and the converted optical profiles.
bars and other features found in the isophotes (e.g. Salo et al.
2015, W2022, Sánchez-Alarcón et al. 2023). This is the same
method used in the original sample (MM2015), but implemented
in the Python programming language.
We first estimate the galaxy orientation using the image
moments on the masked image to improve the success of the
fitting procedure, which requires a first ellipse to initialize. Pre-
viously, the implementation in Salo et al. (2015) of the same
method iterated until the fitting was done through the entire
galaxy but sometimes it stopped prematurely, missing the out-
skirts or failing to fit the inner regions, and forcing a restart of
the fitting procedure. To accelerate the process and make it more
efficient, we estimate the maximum radius of the profile as the
radius where we reach the value that was on the order of the
sky level. This way, the fitting is more robust and successful for
almost the whole sample (≳90%). After a visual inspection of the
profile, we decide the maximum radius of the profile. We keep
the centre position fixed through the whole fitting procedure and
measure the position angle (PA) and the ellipticity (ϵ) for each
radial bin. We determine the centre using the same method as in
Salo et al. (2015), in which a first guess is done manually and
the centre is refined as the location where the brightness gra-
dient is zero. We increase the radius logarithmically by 2% for
each radial bin. This adapted step size allows us to have an opti-
mal radial resolution in the inner regions, with wider ellipses in
the outskirts reaching higher S/N. We estimate the uncertainties
as the quadratic sum of the error on the fit procedure, and the
uncertainty of the sky value measured in P3.
We show an example of the surface brightness profile mea-
sured of PGC 34407 in the 3.6µm band in Fig. 6. The left panel
shows the surface brightness map of the 3.6µm image and the
middle panel shows the same map with the mask regions shaded
and the elliptical apertures used to measure the profiles, along
with the boxes used to measure the sky background value and its
uncertainty. The top right panel shows the 3.6µm surface bright-
ness radial profiles of the IRAC imaging, and the profiles from
the optical LS and SDSS surveys, converted to 3.6µm using our
recipe described in Sect. 5. In the lower right panel, we show
the difference between the converted optical profiles and the
IRAC profile. We used the same elliptical apertures to measure
the three profiles. The elliptical isophotes successfully follow the
orientation of the inner and the outer regions of the galaxy and
the profiles reach the faint outskirts. We observe that the three
profiles exhibit consistent behaviour. We see the expected differ-
ences, in the inner region caused by the PSF and in the outer part
due to the depth of the different images. We reach surface bright-
nesses of ≳25.1 and 26.2 mag arcsec−2 [3σ, 10′′ × 10′′] in the
mid-IR and i-band, respectively, for 95% of the galaxies. Some
galaxies have contaminating light from brighter neighbouring
sources (i.e. bright stars or galaxies) that would require a model
subtraction to describe the faintest regions.
We do not measure radial profiles for the S4G and ETG
galaxies, instead, we used the ones derived from MM2015 and
W2022. However, when converting profiles to physical units, we
used updated distances.
6.2. Asymptotic magnitudes
We derive asymptotic magnitudes from the curve of growth
(hereafter, c.o.g.) of the 3.6µm AB magnitude following the
same procedure as in MM2015 and W2022. We measure the
cumulative sum over the flux within elliptical apertures with
constant ellipticity (ϵ), and position angle (PA), and logarithmi-
cally grown by 2% in semi-major axis. We set the orientation
values, PA and ϵ, to match the outskirts of the galaxy which
were measured by P3. Given the depth of the DG extension, the
c.o.g. flattens in the outskirts of the galaxy and the relationship
between the local gradient and the magnitude enclosed within
each elliptical aperture becomes roughly linear. We can mea-
sure the asymptotic magnitudes as the y-intercept of the linear
fit of the last points on the magnitude-local gradient plane. For
galaxies with i-band images, we convert the i-band magnitudes
to 3.6µm magnitudes.
W2022 show in their Fig. 5 a representation of the same
method as what was used in this work to measure the asymp-
totic magnitudes for two galaxies in the sample with different
A38, page 9 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
inclinations, luminosity, and radial extent. They also discuss the
different effects on the uncertainty of this method introduced in
the magnitude derived. This method is accurate enough that the
uncertainty introduced is smaller than the systematic uncertainty
induced by the sky subtraction.
6.3. Absolute magnitudes and stellar masses
To transform apparent magnitudes to absolute magnitudes, we
query distances from the NED database (Chen et al. 2022).
We used redshift-independent measurements when available (for
97 galaxies, ∼23%) and redshifts when not (for 325 galaxies,
∼77%). There are four galaxies with redshift-independent mea-
surements with very high and unexpected values, of the order of
∼100Mpc. For these galaxies, we used another distance indi-
cator such as the redshift when available or optical velocities
from HyperLeda (see Appendix D for a discussion). We investi-
gated the use of Cosmic-Flow corrected distances (Carrick et al.
2015) and found that, for nearby galaxies, redshift-independent
distances or simple redshift-derived estimates provide a more
reliable determination of distance.
We also updated the distances of the galaxies in the S4G
and ETG samples. We find 1920 (82%) and 247 (53%) galax-
ies with redshift-independent measurements for the S4G and
ETG, respectively. This results in 2264 (70%) galaxies with
redshift-independent measurements for the entire CS4G.
Then, we transform the 3.6µm absolute magnitudes to stel-
lar masses. As reported by Querejeta et al. (2015), in the 3.6µm
band we can assume a constant mass-to-light ratio Υ3.6µm which
varies by 10%–30% on integrated galaxy scales. This uncer-
tainty does not affect our general results in terms of statistical
behaviour in trends. We assume a Chabrier initial mass func-
tion (Chabrier 2003) and use a constant mass-to-light ratio of
Υ3.6 µm ∼ 0.6 (M⊙/L⊙)3.6 µm (see Meidt et al. 2014; Querejeta
et al. 2015; Comerón et al. 2018) to estimate the stellar mass for
all the galaxies.
6.4. Concentration indices
The spatial distribution of light contains valuable information
about the morphological classification of galaxies and their evo-
lution. Concentration indices can be directly measured from
the light distribution of a galaxy. Again following MM2015
and W2022, we measure the concentration parameters C31
(de Vaucouleurs 1977) and C82 (Kent 1985), defined as
C31 = R75/R25, and (3)
C82 = 5 log (R80/R20) , (4)
where Rx is the radius containing x% of the total luminosity of
the galaxy. As in MM2015 and W2022, to avoid assumptions
about the shapes of the light profiles, we measure Rx from the
c.o.g. We extrapolate the total luminosities of galaxies out to
infinity rather than measuring them within set apertures.
We interpolate the c.o.g. to improve the resolution of the pro-
file and increase the accuracy of the measurement. We estimate
the uncertainty of the concentration indices for each galaxy by
performing a Monte Carlo sampling on the value of the sky,
modelling 1000 random sky values from a normal distribution
N(SKY;DSKY), measuring the concentration indices and esti-
mating the error on the value as the standard deviation from each
value.
6.5. Galaxy sizes
We measure the effective (or half-light) radius, Re, along with
the two isophotal radii, R25.5 and R26.5, denoting the radii at
which the surface brightness profile of the galaxy reaches 25.5
and 26.5 mag arcsec−2, respectively, in the 3.6µm and i-bands.
We also apply an inclination correction to measure the isopho-
tal radius at 25.5 and 26.5 mag arcsec−2 (R25.5,corr and R26.5,corr)
in both bands. We follow the correction used in W2022 and
multiply the intensity profiles by the axial ratio. These radii
are measured from the major axis of the elliptical apertures.
We measure Re using the c.o.g. and employing the asymptotic
magnitudes to set the total flux of the galaxy. We estimate the
uncertainties of the values using the same Monte Carlo strategy
as in the concentration indices. We measure the galaxy sizes in
the 3.6µm and 4.5µm channels when available, and for those
galaxies with only i-band we measure it from the i-band profile
and the transformed 3.6µm profile.
6.6. Average surface brightness levels
Using the fitted radial surface brightness profile, we measure the
mean surface brightness within the effective radii (µe) and within
1 kpc (µ1 kpc). We interpolate the profile to have a higher res-
olution and extract more precise values. The uncertainties are
again estimated using the Monte Carlo strategy. We measure
these values for the DG but also for the entire S4G and the ETG
extension.
6.7. Morphological revision
The morphological classifications of the sample galaxies have
been made in a comprehensive version of the de Vaucouleurs
(1959) revised Hubble-Sandage (CVRHS) system (Buta et al.
2015). This includes the recognition of many features of inter-
est to extragalactic observers that were considered mere details
in the past. The hallmark of the system is the prominence of
galactic rings and lenses and the ease with which these can
be added to the system. Rings and lenses are considered pri-
mary tracers of galactic secular evolution (Knapen 2012). The
CVRHS classification also includes barlenses (Laurikainen et al.
2011; Laurikainen & Salo 2017) denoting the inner lens-like
components, actually forming part of the bar, embedded in thin
bars in massive galaxies. The galaxies were classified using the
3.6µm and i-band images presented in this work, together with
g-band images from the SDSS and LS when available. The g-
band is closest in wavelength to the B-band, the historical band
originally used for galaxy morphological study.
A noteworthy finding of our study is that the DG galaxy
sample contains a significant number of extreme late-type spi-
rals (i.e. types Scd and later). Typically, such galaxies tend to be
fairly rich in H I (Buta et al. 1994). Similarly, there appears to be
an unusual number of spindles (edge-ons) in the sample.
Images in the g-band were not available for all of the
galaxies, and also some images had poor seeing. Since the clas-
sifications are based on only a single examination (i.e. without a
second classification of the same objects using the same images),
we assume that the uncertainty is σ(T ) = 0.7 stage intervals
(Buta et al. 2015, 2019).
7. Discussion
We present a collection of scaling relations using the derived
photometric parameters for the DG extension, in comparison
A38, page 10 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
6 8 10
log(M∗/M¯)
0.1
1
10
R
25
.5
[k
p
c]
No inclination corrected
S4G
ETG
DG
6 8 10
log(M∗/M¯)
Inclination corrected
Fig. 7. Stellar mass–size relation showing the correlation of size, traced by the isophotal radius (R25.5) with the stellar mass. The DG, ETG, and
original S4G samples are represented with purple circles, orange stars, and blue triangles respectively. The isophotal radius axis is on a logarithmic
scale. The left panel shows the size of galaxies with no correction, and the left panel shows the size of galaxies with inclination correction.
with the original sample (MM2015) and the ETG extension
(W2022). We begin with the mass–size relation, then we explore
the parameter space defined by the photometric parameters and
the regions where the different morphological types of galaxies
reside. We finish by exploring the difference in H I content for
the different samples.
7.1. Mass–size relation
In the left side panel of Fig. 7 we plot the isophotal radius
at 25.5 mag arcsec−2 (R25.5) against the stellar mass of the
galaxy for the DG extension (purple circles), the ETG exten-
sion (orange stars) and the original sample (blue triangles).
MM2015 and W2022 showed this relation (in their Figs. 14
and 11, respectively), demonstrating the expected monotonic
trend, where galaxies with a higher stellar mass are also larger.
Our photometric measurements, properly converted to 3.6µm,
reproduce this trend, with excellent agreement between our new
galaxies and the original S4G and the ETG samples. The DG
sample populates, in general, the intermediate-low mass region
(7 ≲ log(M∗/M⊙) ≲ 11) with a few massive galaxies with stellar
mass above 1011 M⊙.
The right panel of Fig. 7 shows the mass—size relation
using inclination-corrected isophotal sizes (see Sect. 6.5). This
correction reduces the root mean square deviation of the sizes
from the average trends in mass bins by 0.014 dex, leading to a
tighter relation with lower dispersion. This behaviour was also
discussed in W2022 (their Fig 11), but the corrected values were
not published. However, for a few edge-on galaxies, the correc-
tion introduces discrepancies, increasing their deviation from the
expected trend.
Intriguingly, the DG sample contains six large and mas-
sive galaxies: NGC 1316, NGC 1404 NGC 4125, NGC 4552,
NGC 7172, and NGC 7410. They can be identified in the top-
right corner of Fig. 7, with size R25.5 > 15 kpc and stellar
mass log(M∗/M⊙) > 10.9. The largest galaxy is NGC 7410 with
R25.5 = 51 kpc and log(M∗/M⊙) = 11.36 while the most massive
is NGC 1316 with R25.5 = 43 kpc and log(M∗/M⊙) = 11.65. The
galaxies NGC 1316 and NGC 1404 are part of the Fornax Clus-
ter (see e.g. Ferguson & Sandage 1988, and follow-up papers).
These galaxies follow the same trend as their counterparts in the
S4G and ETG samples.
We also find three dwarf galaxies with mass below
log(M∗/M⊙) < 7 and smaller than R25.5 < 0.6 kpc. These galax-
ies are PGC 3097691, UGC 04879, and UGC 08308.
These galaxies underscore the significance of incorporating
the DG extension to improve the completeness throughout the
mass range of the sample.
7.2. General trends
In Fig. 8, we present a corner plot of the different photometric
parameters derived in this work to explore the different relation-
ships between the parameters in a compact way. We defined two
sub-samples from the S4G, the late-type (S4G-LTG with T > 0)
and the early-type (S4G-ETG with T < 0) to compare with the
DG and ETG extensions. We represent the different parameter
spaces defined by the stellar mass, log(M∗/M⊙), the size of the
galaxy measured as the isophotal radius at 25.5 mag arcsec−2,
log(R25.5), the concentration index, C82, and the morphologi-
cal type, T , for the different samples: the original S4G-LTGs
(blue triangles) and S4G-ETGs (down-pointing red triangles),
the ETG extension (orange stars), and the DG extension (pur-
ple stars). The curves represent the average values per mass bin
in the middle two rows and per morphological-type bins in the
bottom row.
Along the diagonal of the figure, the panels (from top-left
to bottom-right) show the distribution of stellar mass, isophotal
radius, concentration index, and morphological type, respec-
tively, for the four different samples. The sample size difference
A38, page 11 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
0
2
lo
g(
R
25
.5
)
[k
p
c]
2
4
6
C
82
5 10
log(M∗/M¯)
−5
0
5
10
T
0 2
log(R25.5) [kpc]
2 4 6
C82
0 10
T
Sub-sample
S4G-LTG
S4G-ETG
ETG
DG
Fig. 8. Corner plot of the parameter space described by some of the photometric parameters derived for the DG sample together with the original
sample and ETG extension. From top-left to bottom-right, the diagonal of the figure shows the absolute distribution of the stellar mass, log(M∗/M⊙),
the galaxy size measured by the isophotal radii at 25.5mag arcsec−2, log(R25.5), the concentration index, C82, and the morphological types, T , for
the different samples, the original S4G-LTGs (blue triangles) and S4G-ETGs (down-pointing red triangles), the ETG extension (orange stars), and
the DG extension (purple circles). These distributions are heavily smoothed. Each column and row represents one of the parameters. The first
column (left) shows, from top to bottom, galaxy size, concentration index and morphological type versus stellar mass. The second column shows
the concentration index and morphological type versus galaxy size. The last column shows morphological types versus concentration index. Curves
represent the average values within mass bins for the middle two rows and within morphological types bins for the last row.
immediately stands out. While the ETG extension (465) is larger
than the number of ETG galaxies in the original S4G (282),
the DG sample (422) is much smaller than the LTG part of the
original S4G (2070), yet it is essential for the completeness of
the S4G.
The stellar masses show different trends in the histogram,
with distribution means and standard deviations of 9.23 ± 0.78,
9.77 ± 0.83, 10.11 ± 0.82, and 10.46 ± 0.64 log(M∗/M⊙) for
the DG, S4G-LTG, ETG, and S4G-ETG, respectively. While the
S4G-ETG and ETG have broader distributions, the DG and S4G-
LTG distributions are narrower and more skewed. The DG and
the ETG sub-samples are 0.54 dex and 0.35 dex, respectively,
less massive on average than the LTG and ETG sub-samples of
the original S4G.
The stellar mass panels (first column) indicate the stel-
lar mass–size relation (second row, same as Fig. 7), the
A38, page 12 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
concentration indices (third row), and the morphological types
(fourth row) with respect to the stellar mass. As discussed previ-
ously, the parameters derived in this analysis for the DG sample,
and properly corrected to the 3.6µm band, follow similar trends
as the S4G and ETG. Lower-mass galaxies show smaller radii,
lower concentration indices, and higher values of morphologi-
cal types, while massive galaxies have larger sizes and broader
distributions in concentration index and morphological types.
These trends are easily seen with the average curves.
The sizes of galaxies show narrow and aligned distributions
for all the sub-samples with some difference in the logarithmic
means and standard deviations, log(R25.5/kpc) = 0.74 ± 0.35,
0.97 ± 0.27, 0.98 ± 0.31, and 1.11 ± 0.27 for the DG, S4G-LTG,
ETG, and S4G-ETG sub-samples, respectively. In units of kilo-
parsecs, this is 7± 5, 11± 6, 11± 9, and 14± 8. kpc, respectively.
The galaxies in the DG and ETG extension are, on average, 35%
smaller than the LTG of the original S4G, while the galaxies in
the ETG extension are 20% larger.
The size-related panels (second column) show the concen-
tration indices (third row), and the morphological types (fourth
row) with respect to the isophotal radii. Again, as galaxy size is
well correlated with stellar mass, we find expected trends, as pre-
viously reported by MM2015 and W2022. Smaller galaxies (i.e.
less massive) have lower values of C82 and later types. However,
these relations are less strongly correlated than the ones with
respect to stellar mass.
The concentration indices C82 (third row) show different
distributions (in the third column) for the DG and ETG sub-
samples but very similar ones for the extensions and the original
S4G. The mean values for each sub-sample are 2.93 ± 0.74,
3.12 ± 0.74, 4.31 ± 0.99, and 4.19 ± 0.97 for the DG, S4G-LTG,
ETG, and S4G-ETG, respectively. These differences are less sig-
nificant than 10%. However, in general, these figures and average
lines show two clear populations that can be identified as LTGs
and ETGs, with lower concentration and higher concentration,
respectively. As previously reported in many works (see, e.g.
MM2015, W2022, and references therein), the light concentra-
tion of a galaxy correlates with its morphological type, and it is
one of the parameters typically used for galaxy classification for
higher-redshift and unresolved objects. However, among the disc
galaxies, there is a notable scatter due to S0 galaxies having both
high and intermediate values of concentration indices.
The morphological type distributions (fourth row) show the
variety of galaxies in each sub-sample. All samples combined
show a homogeneous and representative of the population in
the local Universe. The mean value and standard deviations
of T -types for each sub-sample are 4.44 ± 4.3, 6.54 ± 2.77,
−1.25 ± 5.17, and −1.8 ± 1.58 for the DG, S4G-LTG, ETG, and
S4G-ETG, respectively. These average values are expected and
a consequence of the definition of each sub-sample. We find
that for similar morphological types, the galaxies in the DG
and ETG extensions are less massive, smaller, and less concen-
trated than their counterparts in the original S4G, as shown in
the average values. The plot of the morphological types with
respect to the concentration index (third column) also shows the
expected clear transition between less-concentrated LTGs and
highly concentrated ETGs.
Our recipes for the conversion between optical i-band and
3.6µm band yield results consistent with the original S4G sub-
sample and the ETG extension observed with Spitzer, as shown
in all the panels of Fig 8. By adding this new extension, we
increase the sample by 422 new galaxies, which represents 18%
of the original S4G, and 36% of the sub-sample with mass
below log(M∗/M⊙) < 9. Together with the ETG extension, the
survey increased the number of galaxies by 38%, significantly
improving its completeness.
7.3. Gas content
Since the DG sub-sample originated from the lack of radio-
derived velocities, we may expect that galaxies in this sub-
sample exhibit a low H I content. This particularity raises another
interesting reason to study this sub-sample and include it in the
CS4G.
We show the distribution of the H I mass fraction,
log(MH I/M∗), with respect to the stellar mass, log(M∗/M⊙), for
all the sub-samples in the left panel of Fig. 9. We used the mag-
nitude of the 21 cm line from HyperLeda to estimate the mass of
H I. There are 188 (44%), 2011 (97%), 65 (14%), and 234 (83%)
available measurements for the DG, S4G-LTG, ETG, and S4G-
ETG sub-samples, respectively. We follow a similar procedure as
Zwaan et al. (1997); Namumba et al. (2023) to obtain H I masses.
We measure H I masses as follows:
MH I = 2.36 × 105 d2 F21 × 11 + z , (5)
where d is the distance to the galaxy in Mpc, F21 is the 21 cm
line integrated flux in Janskys reported by HyperLeda, and z is
the redshift of the galaxy. The DG, S4G-LTG, ETG, and S4G-
ETG sub-samples are shown in purple circles, blue triangles,
orange stars, and down-pointing red triangles, respectively. The
lines represent the average values within the same bins of stellar
mass. The right column shows a violin plot with the H I mass
log(MH I/M⊙) distribution for all the sub-samples.
Despite the lack of observational H I data in much of the
DG sub-sample, the average values of the DG galaxies are below
the averages of their counterparts in the original S4G-LTG. The
disc galaxies in our new DG sub-sample, with the same mass
as their LTG counterparts in the original S4G, have a lower H I
mass fraction. The ETG and S4G-ETG sub-samples show no
clear difference in their average values. However, only 14% of
the galaxies in the ETG sample have H I measurements. If these
are the ETG galaxies richest in gas, they might represent the
upper limit of H I mass, while the remaining ones might have
a lower content of gas. These ETG galaxies most probably are
borderline galaxies between ellipticals and spirals. In the right
panel, we see a similar trend where the distribution of the DG
sub-sample is centred at lower values of H I mass than the S4G-
LTG, with a longer tail towards the low-mass end while the ETGs
and S4G-ETG show similar distributions.
Both the ETG and DG sub-samples have lower stellar masses
and, as discussed in the previous subsection, lower fractions of
H I content than the S4G counterparts with a larger difference
for the DG sub-sample. The exclusion of many of these galax-
ies from the original S4G may be attributed to their lower mass,
lower luminosity, and reduced H I fraction. Their lack of radio-
derived velocities stems from either insufficiently deep data or a
lack of H I observations at the time the original S4G project was
defined.
8. Conclusions
We have presented CS4G, a survey of 3239 galaxies with con-
sistent homogenised photometric parameters. We joined the
original sample of S4G with both the ETG (W2022) and DG
(this paper) extensions to deliver a single catalogue to the com-
munity. Additionally, we homogenised all measurements in the
A38, page 13 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
6 7 8 9 10 11 12
log(M∗/M¯)
−3
−2
−1
0
1
2
lo
g(
M
H
I/
M
∗)
S4G-LTG [N=2011]
S4G-ETG [N=234]
DG [N=207]
ETG [N=65]
S4G-ETG / ETG S4G-LTG / DG
Galaxy Sample
6
7
8
9
10
11
lo
g(
M
H
I/
M
¯)
Fig. 9. H I mass content distribution. The left panel shows the logarithmic gas fraction, log(MH I/M∗), versus the stellar mass log(M∗/M⊙). The
right column shows a violin plot of the H I mass relative distributions for all the sub-samples. The x-axis shows the relative distribution of all
sub-samples. The lines show the median values (dashed) and the 25% and 75% percentiles (dotted). The S4G-LTG, S4G-ETG, ETG, and DG
sub-samples are shown with blue triangles, down-pointing red triangles, orange stars, and purple circles, respectively. The numbers shown in the
legend of the left panel indicate the number of galaxies with H I measurements in each sub-sample.
catalogue and included new measurements of effective radius
and mean surface brightness levels within the effective radius
and within 1 kpc for the whole CS4G.
We incorporated 401 DGs and 21 elliptical galaxies into
the sample. We release archival and new i-band imaging for
367 galaxies of the DG extension and 55 3.6µm and 4.5µm
band images from the Spitzer Heritage Archive. We also include
a number of images (respectively 102, 169, 77, and one) from
DES, LS, SDSS, and HST. We observed 18 galaxies in the i-band
using the LT and the NTT. We analysed all the images using
the original S4G methods (MM2015, Salo et al. 2015, W2022)
and derived radial surface brightness profiles, curves of growth,
asymptotic magnitudes, stellar masses, effective radii, isophotal
radii, and concentration parameters. We derived a recipe to trans-
form optical i-band to 3.6µm magnitudes using images from
the original S4G and optical i-band images. Using this recipe,
we converted i-band magnitudes to 3.6µm to obtain absolute
parameters.
The CS4G parameters allowed us to study different scaling
relations and find the following results:
– We improved the completeness of the survey by adding
422 galaxies to the original sample. This represents 15%
of the total previous sample (S4G+ETG). However, for low-
mass galaxies (M∗ < 109 M⊙), this sample represents an
increment of 36%;
– We measured all the parameters described for the three sam-
ples (S4G, ETG, and DG) using the same methods, creating
a consistent and homogenised CS4G sample;
– Our recipe for the conversion between the optical i-band
to the infrared 3.6µm band yields measurements consistent
with those from the original survey;
– We improved the mask images of the DG by increasing
the number of pixels masked by a factor of five in com-
parison with the original S4G masks. We masked regions
2 mag arcsec−2 fainter. This does not affect the galaxy inte-
grated magnitudes, but it results in a 52% lower error on the
background characterization;
– The DG extension consists of galaxies with masses 7 ≲
log(M∗/M⊙) ≲ 11. It contains six massive galaxies with
log(M∗) > 11 and three galaxies in a tail of lower-mass dwarf
galaxies log(M∗) < 7;
– The DG galaxies are, on average, 0.23 dex less massive and
34% smaller in size than the LTGs of the original S4G.
They have similar concentration indices (within 5%) and
later morphological types;
– The DG sample galaxies show a lower H I gas fraction
than the LTGs in the original S4G. However, we lack H I
measurements for 86% and 56% of the ETG and DG sam-
ples, respectively. Further radio measurements are needed to
confirm this finding;
– The CS4G encompasses at least 99.94% of the complete
sample of nearby galaxies meeting the selection criteria of
the S4G in the local Universe.
In summary, our measurements and our study of the scaling
relations show good agreement with previous studies yet also
highlight specific details that are worthy of further investigation.
All images, profiles, and derived parameters are made available
via the NASA/IPAC Infrared Science Archive and the CDS. The
CS4G will serve as a local benchmark for comparing higher
redshift studies from upcoming surveys such as Euclid, Rubin,
Roman, and others.
Data availability
The CS4G catalogue and datasets, including Table E.2 are
available at the CDS via anonymous ftp to cdsarc.cds.
unistra.fr (130.79.128.5) or via https://cdsarc.cds.
unistra.fr/viz-bin/cat/J/A+A/697/A38. The data will
soon be available at IRSA, https://irsa.ipac.caltech.
edu/data/SPITZER/S4G/overview.html. The catalogue
contains all the information presented in previous samples
homogenised with the methods described together with the
additional bands studied in this work, the i-band (columns
with suffix 3, e.g. mag3) and the converted 3.6µm (inserted in
A38, page 14 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
columns with suffix 1, e.g. mag1) magnitudes. We include
some extra parameters (Re, µe, µ1 kpc), and inclination-corrected
isophotal radii (R25.5,corr and R26.5,corr) derived for the original
S4G and ETG that were not previously published. They are
explained in Sect. 6. We include all the parameters queried from
the HyperLeda database. We include the CVRHS morphological
classification for the DG extension and for the S4G (Buta et al.
2015) and ETG samples (W2022).
Acknowledgements. This work is based in part on observations made with the
Spitzer Space Telescope, which was operated by the Jet Propulsion Laboratory,
California Institute of Technology under a contract with NASA. Based on obser-
vations collected at the European Organisation for Astronomical Research in the
southern hemisphere under ESO programme 0103.B-0586(A). Based on obser-
vations made with the Liverpool Telescope operated on the island of La Palma by
Liverpool John Moores University in the Spanish Observatorio del Roque de los
Muchachos of the Instituto de Astrofisica de Canarias with financial support from
the UK Science and Technology Facilities Council. We acknowledge support
from the Agencia Estatal de Investigación del Ministerio de Ciencia, Innovación
y Universidades (MCIU/AEI) under the grants “The structure and evolution of
galaxies and their outer regions” and the European Regional Development Fund
(ERDF) with references PID2019-105602GBI00/10.13039/501100011033 and
PID2022-136505NB-I00/10.13039/501100011033. Co-funded by the European
Union (MSCA Doctoral Network EDUCADO, GA 101119830 and Widening
Participation, ExGal-Twin, GA 101158446). SC acknowledges funding from the
State Research Agency (AEI) of the Spanish Ministry of Science, Innovation, and
Universities under the grant “The relic galaxy NGC 1277 as a key to understand-
ing massive galaxies at cosmic noon” with reference PID2023-149139NB-I00.
J.R. acknowledges financial support from the Spanish Ministry of Science and
Innovation through the project PID2022-138896NB-C55. AEW acknowledges
support from the STFC [grant number ST/X001318/1]. This work was authored
by an employee of Caltech/IPAC under contract No. 80GSFC21R0032 with
the National Aeronautics and Space Administration. LCH was supported by
the National Science Foundation of China (11991052, 12233001), the National
Key R&D Program of China (2022YFF0503401), and the China Manned Space
Project (CMS-CSST-2021-A04, CMS-CSST-2021-A06). TK acknowledges sup-
port from the Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education (No. RS-
2023-00240212) and the NRF grant funded by the Korean government (MSIT)
(No. 2022R1A4A3031306). EA and AB gratefully acknowledge financial sup-
port from the CNES (Centre National d’Études Spatiales, France). DAG was
supported by STFC grants ST/T000244/1 and ST/X001075/1. JK acknowledges
support from NSF through grant AST-2006600. We acknowledge the usage
of the HyperLeda database (http://leda.univ-lyon1.fr). This research
has made use of the NASA/IPAC Extragalactic Database (NED), which is
operated by the Jet Propulsion Laboratory, California Institute of Technology,
under contract with the National Aeronautics and Space Administration. This
research has made use of the SIMBAD database, operated at CDS, Strasbourg,
France. Funding for the Sloan Digital Sky Survey V has been provided by
the Alfred P. Sloan Foundation, the Heising–Simons Foundation, the National
Science Foundation, and the Participating Institutions. SDSS acknowledges sup-
port and resources from the Center for High-Performance Computing at the
University of Utah. SDSS telescopes are located at Apache Point Observa-
tory, funded by the Astrophysical Research Consortium and operated by New
Mexico State University, and at Las Campanas Observatory, operated by the
Carnegie Institution for Science. The SDSS web site is www.sdss.org. SDSS
is managed by the Astrophysical Research Consortium for the Participating
Institutions of the SDSS Collaboration, including Caltech, The Carnegie Insti-
tution for Science, Chilean National Time Allocation Committee (CNTAC)
ratified researchers, The Flatiron Institute, the Gotham Participation Group, Har-
vard University, Heidelberg University, The Johns Hopkins University, L’Ecole
polytechnique fédérale de Lausanne (EPFL), Leibniz-Institut für Astrophysik
Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-
Planck-Institut für Extraterrestrische Physik (MPE), Nanjing University, National
Astronomical Observatories of China (NAOC), New Mexico State University,
The Ohio State University, Pennsylvania State University, Smithsonian Astro-
physical Observatory, Space Telescope Science Institute (STScI), the Stellar
Astrophysics Participation Group, Universidad Nacional Autónoma de México,
University of Arizona, University of Colorado Boulder, University of Illinois
at Urbana-Champaign, University of Toronto, University of Utah, University
of Virginia, Yale University, and Yunnan University. The DESI Legacy Imag-
ing Surveys consist of three individual and complementary projects: the Dark
Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey
(BASS), and the Mayall z-band Legacy Survey (MzLS). DECaLS, BASS and
MzLS together include data obtained, respectively, at the Blanco telescope,
Cerro Tololo Inter-American Observatory, NSF’s NOIRLab; the Bok telescope,
Steward Observatory, University of Arizona; and the Mayall telescope, Kitt Peak
National Observatory, NOIRLab. NOIRLab is operated by the Association of
Universities for Research in Astronomy (AURA) under a cooperative agreement
with the National Science Foundation. Pipeline processing and analyses of the
data were supported by NOIRLab and the Lawrence Berkeley National Labo-
ratory (LBNL). Legacy Surveys also uses data products from the Near-Earth
Object Wide-field Infrared Survey Explorer (NEOWISE), a project of the Jet
Propulsion Laboratory/California Institute of Technology, funded by the National
Aeronautics and Space Administration. Legacy Surveys was supported by: the
Director, Office of Science, Office of High Energy Physics of the U.S. Depart-
ment of Energy; the National Energy Research Scientific Computing Center, a
DOE Office of Science User Facility; the U.S. National Science Foundation,
Division of Astronomical Sciences; the National Astronomical Observatories
of China, the Chinese Academy of Sciences and the Chinese National Natu-
ral Science Foundation. LBNL is managed by the Regents of the University
of California under contract to the U.S. Department of Energy. The com-
plete acknowledgements can be found at https://www.legacysurvey.org/
acknowledgment/. Software: This work made use of Astropy (http://www.
astropy.org) a community-developed core Python package and an ecosystem
of tools and resources for astronomy (Astropy Collaboration 2013, 2018, 2022);
Photutils, an Astropy package for detection and photometry of astronomical
sources (Bradley et al. 2022); Matplotlib (Hunter 2007); NumPy (Harris et al.
2020); SciPy (Virtanen et al. 2020); Pandas (The pandas development team
2020); TOPCAT (Taylor 2005); SExtractor (Bertin & Arnouts 1996); Swarp
(Bertin 2010); and SAO Image DS9 (Joye & Mandel 2003).
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1 Instituto de Astrofísica de Canarias, c/ Vía Láctea s/n, 38205 La
Laguna, Tenerife, Spain
2 Departamento de Astrofísica, Universidad de La Laguna, 38206 La
Laguna, Tenerife, Spain
3 Space Physics and Astronomy Research Unit, University of Oulu,
Pentti Kaiteran katu 1, 90014 Oulu, Finland
4 Departamento de Física de la Tierra y Astrofísica, Universidad
Complutense de Madrid, 28040 Madrid, Spain
5 Centre of Astrophysics Research, School of Physics, Astronomy and
Mathematics, University of Hertfordshire, Hatfield, UK
6 Department of Physics and Astronomy, University of Alabama,
Box 870324, Tuscaloosa, AL 35487, USA
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1, 85748 Garching, Germany
9 Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France
10 Centre for Extragalactic Astronomy, Department of Physics,
Durham University, South Road, Durham DH1 3LE, UK
11 MMT Observatory, University of Arizona, 933 N Cherry Ave,
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20014 University of Turku, Finland
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National University, Daegu 702-701, Republic of Korea
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19 Normet Oy, Elektroniikkatie 8, 90590 Oulu, Finland
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A38, page 16 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
Appendix A: Origin of the bias in the S4G sample
The S4G aimed to construct a volume-, magnitude-, and size-
limited survey of galaxies in the local Universe and study the
spatial distribution of light through mid-infrared imaging. Orig-
inally, when the sample of the S4G was selected (Sheth et al.
2010), the query of the galaxies in the HyperLeda database
returned 2331 galaxies. For this selection, radio H I velocities
as a query for distance were used to select galaxies closer
than 40Mpc. However, the use of radio-derived H I velocities
biased the original sample towards gas-rich late-type galaxies
and missed a high fraction (over 50%) of ETGs, which typically
have lower gas content.
Sheth et al. (2013) used HyperLeda values based on opti-
cal spectroscopy to determine the distance to galaxies to search
for ETGs (THL ≤ 0) without H I velocities. This introduced 465
new ETGs (W2022), lacking radio H I measurements in the
HyperLeda database, which made them miss the criteria of the
original S4G but meeting the volume-limited criteria of 40Mpc
using optical-derived distances. Nevertheless, the specific crite-
rion to search for ETGs (THL ≤ 0) introduced a new bias against
late-type galaxies.
Later on, when a study of the Fornax cluster with S4G
authors involved was being carried out (Venhola et al. 2017), they
found out that many disc galaxies were not in the S4G despite
meeting all requisites. This finding made the authors revise the
original sample and select disc galaxies using optical velocities,
giving rise to the DG extension. As explained in Sect. 3, the
DG extension mimics the criterion of the ETG extension but
searches for late-type galaxies (THL > 0). Intriguingly, several
galaxies with THL ≤ 0 were not included in the early-type exten-
sion but are now included in the disc extension. We are not sure
why these galaxies were missed in the original or ETG sample,
but one possible explanation is that new measurements could
have been added in HyperLeda for these galaxies between the
time each of the samples was done (2013 and 20188). Another
explanation, at least for galaxies with large apparent size (such
as NGC 1316), is that the original S4G excluded them on purpose
not to expend many pointings of Spitzer on these galaxies. Now,
with this publication, we fulfil the original aim of constructing a
8 Between 2018 and 2024, there have been no significant changes in
the parameters of CS4G galaxies in HyperLeda
Fig. A.1: Illustration of the CS4G sample. The CS4G contains the S4G,
ETG and DG sample. It mimics the criteria used to define the S4G, but
including also optical velocities to define the ETG and DG samples.
volume-, magnitude-, and size-limited survey by adding 422 disc
galaxies to the sample.
We present the CS4G, a combination of the S4G, ETG and
DG sub-samples, with a final total size of 3239 galaxies in the
local Universe. Figure A.1 shows an illustration of the CS4G and
the differences between the ETG and DG samples. The x-axis
represents the morphological T -types, while the y-axis repre-
sents the recessional velocity of the galaxies. The ETG and DG
samples differ from the S4G by the wavelength used to mea-
sure the recessional velocity, optical and radio H I velocities,
respectively. They also differ by the morphological types, while
ETG includes THL ≤ 0, the DG mostly includes THL > 0 with
33 THL ≤ 0 galaxies that missed the ETG (represented with the
purple dashed line).
Appendix B: Galaxies not forming part of the DG
sample
The exact query used in HyperLeda, mimicking the selection
criteria of the CS4G is the following:
SELECT pgc,objname,al2000,de2000,logd25,
b2,t,bt,btc,vmaxg,vmaxs,v,vrot,modz,mod0,
modbest,mabs,vrad,vopt,objtype
WHERE (objtype=’G’ OR objtype=’?’)
AND btc<15.5 AND logd25 >1.
AND logd25 <2.48 AND abs(b2)>30.
AND (vrad <3000 OR vopt <3000)
This query executed on October 20249 resulted in 430 new
galaxies not included in the S4G and ETG. However, of these
galaxies, eight were rejected because the galaxy appeared to
be misclassified, it was a stellar stream related to another
galaxy, a system of interacting galaxies, or it was a background
galaxy. The galaxies rejected were: PGC 015573, PGC 166170,
PGC 3097827, PGC 014117, PGC 014118, PGC 014121,
PGC 068481, and ESO 056-019.
PGC 015573 is misclassified as a galaxy. We deduce it
must be a star system embedded in a dust cloud that pro-
duces an extended emission which could be interpreted as a
galaxy in shallower data. PGC 166170, also known as KK 208,
is a stellar stream of M83 with an estimated stellar mass of
∼1× 108M⊙ (Barnes et al. 2014). The galaxy PGC 3097827, also
known as F8D1, is an Ultra Diffuse Galaxy (UDG) associated
with M81 (Karachentsev et al. 2000) with a surface bright-
ness peak of 25.81 mag arcsec−2 and an integrated magnitude
fainter than 15.5 mag. The galaxies PGC 014117, PGC 014118,
and PGC 014121 appear to be systems of interacting galax-
ies, with individual galaxies having diameters below the limit.
PGC 068481 is a bright knot in the galaxy ESO 602-003, which
is included in the S4G. Finally, ESO 056-019 is a region of the
LMC. Rejecting these 8 entries results in a total sample of 422
galaxies in the DG extension.
Appendix C: Notes on the analysis of UGC 07636
and PGC 3097691
The galaxy UGC 07636 is a companion of M49 embedded in
the galaxy’s outskirts. The galaxy M49 is in the original S4G,
and its morphological decomposition was done by Salo et al.
(2015). We have used the residual S4G image, after subtracting
9 This is the date of the last execution
A38, page 17 of 18
Sánchez-Alarcón, P. M., et al.: A&A, 697, A38 (2025)
the model fitted by Salo et al. (2015), to measure the radial profile
of UGC 07636 properly. The dwarf galaxy PGC 3097691 is also
included in the sample. At a distance of 0.8Mpc the Legacy Sur-
veys imaging can resolve the stars, and despite being analysed,
the surface brightness technique is not optimal for studying the
structure of the object.
Appendix D: Distance selection
When selecting the distance to the galaxies to transform appar-
ent to absolute magnitudes, we aim to be consistent with the
original S4G, just as we have been with the sample analysis. In
the S4G, while the sample selection was based on HyperLeda
velocities, the distances were selected from the NED database. A
redshift-independent measurement was used in the original S4G,
and if that was unavailable, a distance derived from the redshift
was used assuming H0 = 75 km s−1 Mpc−1. We used the same
methodology. However, there are a few cases where the redshift-
independent distance is incompatible with its redshift and others
where the NED distances are incompatible with the HyperLeda
optical velocities. Figure D.1 shows the distances obtained from
the HyperLeda radial velocities in comparison with the NED
distances.
The NED distances are selected as explained above and
are a combination of redshift-independent measurements and
redshift-derived distances. We also show the lines representing
the volume limit criterion of the S4G of 40Mpc. These lines
divide the figure into four quadrants. The first quadrant, in the
bottom left, shows galaxies with distances from the two differ-
ent sources (NED and HyperLeda) within the criteria of the
S4G (below 40Mpc). The third quadrant, in the upper right,
as expected, is empty since it represents galaxies not meeting
the criterion in either of the sources. The second and fourth
quadrants show galaxies meeting the criterion in one of the
0 10 20 30 40 50 60
Vopt/75 [Mpc]
0
10
20
30
40
50
60
D
N
E
D
[M
p
c]
S4G limit
S
4
G
lim
it
1
2
4
3
Fig. D.1: Comparison of distances. The x-axis shows the distances
obtained using the optical velocities from the HyperLeda database. The
y-axis shows the distances obtained from the NED database, a combina-
tion of redshift-independent measurements and redshift distances. The
red line shows the one-to-one relation. The arrow shows galaxies with
distances above the limits of the figure but located at the position of the
other axis values. The dashed grey lines show the volume limit criterion
of the S4G of 40Mpc.
sources but not in the other. Since the sample selection crite-
rion of the S4G was only based on the HyperLeda database, we
would expect not to see any galaxies in the fourth quadrant. In
spite of this, we find two galaxies (ESO 288-049 and ESO 603-
006) with optical velocities corresponding to distances larger
than 40 Mpc in the HyperLeda database. These two galaxies do
have H I radio velocity measurements below 3000 km s−1. More-
over, there are 25 galaxies with NED distances over the 40 Mpc
criterion, but with optical velocities meeting the selection cri-
terion (seen in the second quadrant). For the particular case of
the galaxy PGC 040869, we used the redshift-independent dis-
tance from Cantiello et al. (2024). NED and HyperLeda both
register a redshift estimated distance of 0.43 Mpc. However, the
galaxy appears to be one of the many dwarfs around the Virgo
Cluster. Cantiello et al. (2024) obtained a distance estimation of
13.86 Mpc using surface brightness fluctuations, which links the
galaxy to the Virgo Cluster as expected.
In summary, our criterion for assigning distances is to ensure
they meet the requirement of being less than 40Mpc, regard-
less of the source. We prioritised our selections in the following
order: first, we used NED redshift-independent distances. Sec-
ond were NED redshift distances, and third were optical veloc-
ities from HyperLeda. If a measurement from a higher-priority
source does not meet the d < 40Mpc criterion, we move to the
next priority source.
Appendix E: CVRHS classifications
The morphological classification and their numerical values
(described in Sect. 6.7) can be found in the electronic table
(see Sect. 8) in the columns CVRHS and CVRHS_t, respectively.
Table E.1 shows the transformation from the morphological clas-
sification to numerical values. The description of all symbols can
be found in Buta et al. (2015). The symbol K stands for galaxies
showing a knotted outer ring.
Table E.1: Numerical T -type transformation.
Stage Numerical index (T -type)
cE -6
E -5
E+ -4
S0− -3
S0 -2
S0+ -1
S0/a 0
Sa 1
Sab 2
Sb 3
Sbc 4
Sc 5
Scd 6
Sd 7
Sdm 8
Sm 9
Im 10
dE, dS0, dSph 11
References. de Vaucouleurs (1977); Buta et al. (2015); W2022
A38, page 18 of 18