11institutetext: Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan
281 S9, B-9000 Gent, Belgium
11email<EMAIL_ADDRESS>22institutetext: Department of Physics and
Astronomy, N283 ESC, Brigham Young University, Provo, UT 84602, USA
33institutetext: Center for Astrophysical Sciences, Department of Physics and
Astronomy, The Johns Hopkins University, 3400 N Charles St., Baltimore, MD
21218, USA 44institutetext: Space Telescope Science Institute, 3700 San Martin
Drive, Baltimore, MD 21218, USA 55institutetext: Physics Department, College
of Natural Sciences, Jimma University, PO Box 378, Jimma, Ethiopia
66institutetext: Instituto de Radioastronomía y Astrofísica, Universidad
Nacional Autónoma de México, Morelia, Michoacán 58089, Mexico 77institutetext:
SRON Netherlands Institute for Space Research, Landleven 12, 9747 AD,
Groningen, The Netherlands 88institutetext: Kapteyn Astronomical Institute,
University of Groningen, Postbus 800, 9700 AV, Groningen, The Netherlands
99institutetext: INAF – Osservatorio Astronomico di Capodimonte, Salita
Moiariello 16, I-80131 Napoli, Italy

# The TNG50-SKIRT Atlas:
post-processing methodology and first data release

Maarten Baes The TNG50-SKIRT Atlas:  post-processing methodology and first
data releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
release Andrea Gebek The TNG50-SKIRT Atlas:  post-processing methodology and
first data releaseThe TNG50-SKIRT Atlas:  post-processing methodology and
first data release Ana Trčka The TNG50-SKIRT Atlas:  post-processing
methodology and first data releaseThe TNG50-SKIRT Atlas:  post-processing
methodology and first data release Peter Camps The TNG50-SKIRT Atlas:  post-
processing methodology and first data releaseThe TNG50-SKIRT Atlas:  post-
processing methodology and first data release Arjen van der Wel The
TNG50-SKIRT Atlas:  post-processing methodology and first data releaseThe
TNG50-SKIRT Atlas:  post-processing methodology and first data release
Abdurro’uf The TNG50-SKIRT Atlas:  post-processing methodology and first data
releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
release Nick Andreadis The TNG50-SKIRT Atlas:  post-processing methodology and
first data releaseThe TNG50-SKIRT Atlas:  post-processing methodology and
first data release
Sena Bokona Tulu The TNG50-SKIRT Atlas:  post-processing methodology and first
data releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
release Abdissa Tassama Emana The TNG50-SKIRT Atlas:  post-processing
methodology and first data releaseThe TNG50-SKIRT Atlas:  post-processing
methodology and first data releaseThe TNG50-SKIRT Atlas:  post-processing
methodology and first data releaseThe TNG50-SKIRT Atlas:  post-processing
methodology and first data release Jacopo Fritz The TNG50-SKIRT Atlas:  post-
processing methodology and first data releaseThe TNG50-SKIRT Atlas:  post-
processing methodology and first data release Raymond Kelly The TNG50-SKIRT
Atlas:  post-processing methodology and first data releaseThe TNG50-SKIRT
Atlas:  post-processing methodology and first data release Inja Kovačić The
TNG50-SKIRT Atlas:  post-processing methodology and first data releaseThe
TNG50-SKIRT Atlas:  post-processing methodology and first data release
Antonio La Marca The TNG50-SKIRT Atlas:  post-processing methodology and first
data releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
releaseThe TNG50-SKIRT Atlas:  post-processing methodology and first data
release Marco Martorano The TNG50-SKIRT Atlas:  post-processing methodology
and first data releaseThe TNG50-SKIRT Atlas:  post-processing methodology and
first data release Aleksandr Mosenkov The TNG50-SKIRT Atlas:  post-processing
methodology and first data releaseThe TNG50-SKIRT Atlas:  post-processing
methodology and first data release Angelos Nersesian The TNG50-SKIRT Atlas:
post-processing methodology and first data releaseThe TNG50-SKIRT Atlas:
post-processing methodology and first data release Vicente Rodriguez-Gomez The
TNG50-SKIRT Atlas:  post-processing methodology and first data releaseThe
TNG50-SKIRT Atlas:  post-processing methodology and first data release
Crescenzo Tortora The TNG50-SKIRT Atlas:  post-processing methodology and
first data releaseThe TNG50-SKIRT Atlas:  post-processing methodology and
first data release Bert Vander Meulen The TNG50-SKIRT Atlas:  post-processing
methodology and first data releaseThe TNG50-SKIRT Atlas:  post-processing
methodology and first data release Lingyu Wang The TNG50-SKIRT Atlas:  post-
processing methodology and first data releaseThe TNG50-SKIRT Atlas:  post-
processing methodology and first data releaseThe TNG50-SKIRT Atlas:  post-
processing methodology and first data releaseThe TNG50-SKIRT Atlas:  post-
processing methodology and first data release

Galaxy morphology is a powerful diagnostic to assess the realism of
cosmological hydrodynamical simulations. Determining the morphology of
simulated galaxies requires the generation of synthetic images through 3D
radiative transfer post-processing that properly accounts for different
stellar populations and interstellar dust attenuation. We use the SKIRT code
to generate the TNG50-SKIRT Atlas, a synthetic UV to near-infrared broadband
image atlas for a complete stellar-mass selected sample of 1154 galaxies
extracted from the TNG50 cosmological simulation at $z=0$. The images have a
high spatial resolution (100 pc) and a wide field of view (160 kpc). In
addition to the dust-obscured images, we also release dust-free images and
physical parameter property maps with matching characteristics. As a sanity
check and preview application we discuss the UVJ diagram of the galaxy sample.
We investigate the effect of dust attenuation on the UVJ diagram and find that
it affects both the star-forming and the quiescent galaxy populations. The
quiescent galaxy region is polluted by younger and star-forming highly
inclined galaxies, while dust attenuation induces a separation in inclination
of the star-forming galaxy population, with low-inclination galaxies remaining
at the blue side of the diagram and high-inclination galaxies systematically
moving towards the red side. This image atlas can be used for a variety of
other applications, including galaxy morphology studies and the investigation
of local scaling relations. We publicly release the images and parameter maps,
and we invite the community to use them.

###### Key Words.:

radiative transfer – dust: extinction – galaxies: fundamental parameters –
galaxies: ISM – galaxies: stellar content – galaxies: structure

##  1 Introduction

Cosmological hydrodynamical simulations are powerful tools to investigate the
origin and evolution of galaxies. These simulations use numerical methods to
emulate the behaviour of gas, stars, black holes, and dark matter in a virtual
universe. They take into account the effects of gravity, hydrodynamics, star
formation, feedback from supernovae and active galactic nuclei, and other
physical processes that are known or expected to influence the formation and
evolution of galaxies. For recent reviews, we refer to Somerville & Davé
(2015), Vogelsberger et al. (2020a), and Crain & van de Voort (2023).

In general, our confidence in the conclusions drawn from cosmological
hydrodynamical simulations increases with the level of agreement between
simulations and observations. Vice versa, detailed comparisons are required to
calibrate the subgrid physics recipes in the simulations and to guide the
improvements for the next-generation simulations. Only a decade ago, the
overall agreement between hydrodynamical simulations and observations of
galaxies was relatively poor (e.g. Navarro & Benz 1991; Navarro & Steinmetz
1997, 2000; Sommer-Larsen et al. 1999; Scannapieco et al. 2012). In recent
years the agreement has improved significantly, mainly due to an increase in
resolution and better implementations of subgrid physics recipes. Large-volume
cosmological hydrodynamical simulations such as Illustris and IllustrisTNG
(Vogelsberger et al. 2014; Pillepich et al. 2018a), EAGLE (Schaye et al. 2015;
Crain et al. 2015), Magneticum (Dolag et al. 2016), Horizon-AGN and NewHorizon
(Dubois et al. 2016, 2021), or SIMBA (Davé et al. 2019) succeed in reproducing
many observed global galaxy properties to a fair degree, including stellar and
gas mass functions, the colour bimodality, the star-forming main sequence, the
mass–metallicity relation, and many other scaling relations (e.g. De Rossi et
al. 2017; Bottrell et al. 2017; Torrey et al. 2019; Davé et al. 2020; Akins et
al. 2022; de Graaff et al. 2022; Zenocratti et al. 2022, to name just a few
examples)

Galaxies are no structureless point sources but complex ecosystems. The
comparison of simulated galaxies to observations on local rather than global
scales provides a powerful and meaningful test for cosmological hydrodynamical
simulations. With many large imaging and integral-field spectroscopic surveys
just finished, ongoing, or about to start, such as the SAMI Galaxy Survey
(Bryant et al. 2015; Croom et al. 2021), MaNGa (Bundy et al. 2015; Abdurro’uf
et al. 2022a), the DESI Legacy Imaging Surveys (Dey et al. 2019), WEAVE-StePS
(Iovino et al. 2023), or the Euclid Wide Survey (Euclid Collaboration:
Scaramella et al. 2022), the required observational data are nowadays
available. This offers the prospect of testing the simulation’s fidelity in
much more detail, and to identify limitations due to both particle resolution
or incorrect physics implemented in the models.

Galaxy morphology is a particularly interesting characteristic to assess the
realism of cosmological hydrodynamical simulations. Morphology has served as
the main basis for galaxy classification since the early days of extragalactic
astronomy (Sandage 2005, and references therein). However, galaxy morphology
is important beyond classification alone. It is a powerful diagnostic of the
physical processes driving galaxy evolution (e.g. bars, mergers, spiral
structure, tidal features). Morphological parameters are found to correlate
with several fundamental galaxy properties such as stellar mass, colour, star
formation history, merger history, and local galactic environment (Dressler
1980; Gómez et al. 2003; Kauffmann et al. 2003; Conselice 2003, 2014; Lotz et
al. 2008; van der Wel 2008; Blanton & Moustakas 2009; Bluck et al. 2014).

In order to determine the morphology of simulated galaxies, we have to apply a
forward modelling or post-processing approach on them. Since we know the 3D
position and characteristics of all stellar particles in a cosmological
hydrodynamical simulation, this seems a fairly straightforward projection
operation. However, the generation of realistic synthetic images can be quite
involved, especially due to the effects of absorption and scattering by dust
grains in the interstellar medium. Indeed, dust attenuates a third to half of
all the starlight in typical spiral galaxies in the local Universe (Popescu &
Tuffs 2002; Viaene et al. 2016; Bianchi et al. 2018), and detailed radiative
calculations have shown that the effects of dust attenuation are often complex
(Witt et al. 1992; Byun et al. 1994; Pierini et al. 2004; Möllenhoff et al.
2006; Gadotti et al. 2010). The only way to generate synthetic images that
properly account for dust absorption and scattering in a realistic geometry is
by 3D radiative transfer modelling, a demanding and challenging numerical
problem (Steinacker et al. 2013).

In the past few years, there have been several efforts to generate synthetic
images for large sets of galaxies extracted from cosmological hydrodynamical
simulations. Torrey et al. (2015) used the SUNRISE radiative transfer code
(Jonsson 2006) to generate a broadband image atlas for about 7000 $z=0$
galaxies extracted from the Illustris simulation (without including dust in
their models). Trayford et al. (2015) used the SKIRT code (Camps & Baes 2015,
2020) to produce an image atlas for more than 30,000 galaxies from the EAGLE
simulation at $z=0.1$, fully accounting for dust attenuation. Similarly,
Rodriguez-Gomez et al. (2019) generated realistic synthetic Pan-STARRS-like
images for more than 12,000 galaxies from the TNG100 simulation using SKIRT.
The power of these large-volume-simulation-based image databases is that they
cover the entire galaxy population, from massive ellipticals to less massive
star-forming galaxies. The drawback is that the physical resolution of the
underlying simulations, and hence also of the mock images, is limited to about
1 kpc, a limiting factor in both realism and range of applications.

This disadvantage can be addressed by shifting from large-volume to higher-
resolution zoom-in simulations, such as FIRE (Hopkins et al. 2014), NIHAO
(Wang et al. 2015), APOSTLE (Sawala et al. 2016), Latte (Wetzel et al. 2016),
Auriga (Grand et al. 2017), RomulusC (Tremmel et al. 2019), or ARTEMIS (Font
et al. 2020). Kapoor et al. (2021) and Camps et al. (2022) generated a suite
of synthetic high-resolution images for the present-day galaxies from the
Auriga and ARTEMIS simulations, respectively, with a combined image database
that covers the entire UV–submm wavelength range. Faucher et al. (2023) also
used SKIRT to generate UV–submm images for a set of present-day galaxies from
the NIHAO simulation. The main limitation of these studies is that they only
cover a relatively small number of galaxies (30 from Auriga, 45 from ARTEMIS,
65 from NIHAO) and the limited dynamical range in galaxy properties.

The TNG50 simulation (Pillepich et al. 2019; Nelson et al. 2019b) offers the
prospect of combining the advantages of both approaches as it combines a high
spatial and mass resolution typical for zoom-in simulations with a large
number and diversity of galaxies typical for large-volume simulations. Very
recently, Guzmán-Ortega et al. (2023) presented a database of simulated KiDS-
like gri images for more than 5000 galaxies from the TNG50 simulation at
$z=0.034$ to investigate the connection between galaxy mergers and optical
morphology in the local Universe. Their approach is similar to Rodriguez-Gomez
et al. (2019) and fully accounts for dust attenuation using SKIRT. At the same
time, several teams have generated synthetic optical data cubes for samples of
galaxies extracted from the TNG50 simulation, with different levels of
refinement in the way dust effects are approximated (Bottrell & Hani 2022;
Nanni et al. 2022, 2023; Sarmiento et al. 2023).

In this paper we present and release the TNG50-SKIRT Atlas (hereafter TSA), a
synthetic image database for 1154 galaxies at $z=0$ extracted from the TNG50
simulation. We cover a wide range of galaxy properties, we generate mock
images in 18 broadband filters in the UV to near-infrared (NIR) wavelength
range with high spatial resolution and a wide field of view, while fully
accounting for dust absorption and scattering. Moreover, we also generate
images that are free of dust attenuation, as well as synthetic maps of
intrinsic physical parameters such that connections between the observed and
the intrinsic properties can easily be made.

This paper is organised as follows. In Sect. 2 we briefly present the TNG50
cosmological hydrodynamical simulation and the SKIRT radiative transfer code,
and we discuss our methodology to generate synthetic images for the TNG50
galaxies. In Sect. 3 we present the multi-wavelength image database. In Sect.
4 we discuss the UVJ diagram of the galaxy sample as an illustration of the
range of applications that is possible with these data. We present a summary
and our conclusions in Sect. 5.

##  2 Synthetic images for the TNG50 simulation

###  2.1 The TNG50 simulation

The TNG50 simulation (Pillepich et al. 2019; Nelson et al. 2019b) is the
highest-resolution version of the IllustrisTNG cosmological magneto-
hydrodynamical simulations suite (Marinacci et al. 2018; Naiman et al. 2018;
Nelson et al. 2018; Pillepich et al. 2018b; Springel et al. 2018). It follows
the evolution of a cubic volume of 51.7 comoving Mpc on the side, with
cosmological parameters based on the Planck 2015 results, namely
$\Omega_{\text{m}}=0.3089$, $\Omega_{\text{b}}=0.0486$,
$\Omega_{\Lambda}=0.6911$, and $H_{0}=67.74\leavevmode\nobreak\
{\text{km}}\,{\text{s}}^{-1}\,{\text{Mpc}}^{-1}$ (Planck Collaboration et al.
2016). TNG50 uses the moving-mesh code AREPO (Springel 2010) as its
hydrodynamics solver. The galaxy formation model (Weinberger et al. 2017;
Pillepich et al. 2018a) in the TNG50 simulation is identical for all three TNG
simulations. The physical processes accounted for in the simulation include
gas cooling and heating, stochastic star formation, stellar evolution,
chemical enrichment of the ISM, feedback from supernovae, seeding and growth
of supermassive black holes, AGN feedback, and magnetic fields. TNG50 reaches
a baryonic mass resolution of $8.5\times 10^{4}\leavevmode\nobreak\
{\text{M}}_{\odot}$; the average cell size in the star-forming regions of
galaxies is 70–140 pc. For a full description of the TNG50 simulation we refer
to Pillepich et al. (2019) and Nelson et al. (2019b).

###  2.2 Sample selection

Figure 1: Relation between stellar mass, half-mass radius, and SFR for the
TNG50 galaxies in our sample. In the bottom panel, the dashed line indicates
the separation between quiescent and star-forming galaxies
(${\text{sSFR}}=10^{-11}\leavevmode\nobreak\ {\text{yr}}^{-1}$). Galaxies with
no ongoing star-formation are plotted at ${\text{SFR}}=2\times
10^{-4}\leavevmode\nobreak\ {\text{M}}_{\odot}\leavevmode\nobreak\
{\text{yr}}^{-1}$.

To build the TSA, we selected all TNG50 galaxies at $z=0$ with total stellar
mass between $10^{9.8}$ and $10^{12}\leavevmode\nobreak\ {\text{M}}_{\odot}$
from the TNG50 public database (Nelson et al. 2019a), resulting in a sample of
1154 individual galaxies. The characteristics of the sample are illustrated in
Fig. 1, which shows the correlations between stellar mass, star formation rate
(SFR), and half-mass radius. As evident from the bottom panel, most galaxies
in the sample are star-forming galaxies lying in the main sequence, whereas
there is also a population of more quiescent galaxies. Adopting a specific
star formation rate (sSFR) of ${\text{sSFR}}=10^{-11}\leavevmode\nobreak\
{\text{yr}}^{-1}$ as the boundary between star-forming and quiescent galaxies
(e.g. Brinchmann et al. 2004; Fontanot et al. 2009; Donnari et al. 2019;
Paspaliaris et al. 2023), our sample contains 869 star-forming and 285
quiescent galaxies.

###  2.3 The SKIRT radiative transfer code

SKIRT (Camps & Baes 2015, 2020) is a three-dimensional Monte Carlo radiative
transfer code. Originally set up as a dust radiative transfer code and
designed to model the effects of dust in galaxies (Baes et al. 2003, 2011), it
has transformed into a more generic Monte Carlo radiative transfer tool. The
physical ingredients within SKIRT include absorption, multiple scattering, and
stochastic emission (Camps et al. 2015); polarisation caused by scattering or
by emission from aligned nonspherical dust grains (Peest et al. 2017;
Vandenbroucke et al. 2021); Ly$\alpha$ resonant line scattering (Camps et al.
2021); absorption and emission at rotational or electronic transitions for
selected ions, atoms and molecules (Gebek et al. 2023; Matsumoto et al. 2023);
photo-absorption, fluorescence, and scattering at X-ray wavelengths (Vander
Meulen et al. 2023). SKIRT is equipped with a library of flexible input
models, routines to import the output from various kinds of hydrodynamical
simulations, and a selection of advanced spatial grids for discretising the
medium (Baes & Camps 2015; Saftly et al. 2013, 2014; Camps et al. 2013). Many
Monte Carlo radiative transfer optimisation mechanisms and a hybrid
parallelisation strategy are implemented to maximise the efficiency of the
code (Baes 2008; Steinacker et al. 2013; Baes et al. 2016, 2022; Verstocken et
al. 2017).

While there are several applications in other fields (e.g. Deschamps et al.
2015; Hendrix et al. 2016; Mosenkov et al. 2021; Ebenbichler et al. 2022;
Jáquez-Domínguez et al. 2023), SKIRT is mainly used in an extragalactic
context. It is used to generate images, spectra, spectral energy
distributions, and polarisation maps for idealised galaxy models (Gadotti et
al. 2010; De Geyter et al. 2014; Lee et al. 2016; Lin et al. 2021; Qiu & Kang
2022), for high-resolution models fitted to observed spiral galaxies (De Looze
et al. 2014; Viaene et al. 2017, 2020; Mosenkov et al. 2018; Williams et al.
2019; Verstocken et al. 2020; Nersesian et al. 2020a, b), and for simulated
galaxies extracted from cosmological simulations (e.g. Saftly et al. 2015;
Trayford et al. 2017; Rodriguez-Gomez et al. 2019; Vogelsberger et al. 2020b;
Liang et al. 2021; Hsu et al. 2023; Cochrane et al. 2023).

###  2.4 The SKIRT setup to generate high-resolution images

Each SKIRT simulation is completely determined by specifying the
characteristics of the primary radiation sources, the transfer medium, a suite
of instruments to capture the emerging radiation field, probes to measure
other aspects of the simulation, and a number of technical or numerical
simulation parameters. The recipe for the SKIRT simulations we adopted in this
work largely follows the prescriptions outlined by Trčka et al. (2022), which
were inspired by earlier works by Camps et al. (2016, 2018, 2022) and Kapoor
et al. (2021). We only give a brief overview, focusing on the particular
aspects of the current work, and refer to these papers for more details.

####  2.4.1 Primary radiation sources

For each galaxy, the primary radiation sources are the stellar particles that
belong to the galaxy, which were extracted from the TNG50 database. To each
stellar particle older than 10 Myr, we assigned a simple stellar population
(SSP) SED from the Bruzual & Charlot (2003) SSP family with a Chabrier (2003)
initial mass function. The mass, metallicity, and age of the SSP are directly
inherited from the TNG50 particle data. Each particle was given a smoothing
length corresponding to the distance to the 32nd nearest neighbour, with a
maximum smoothing length of 800 pc.

Stellar particles younger than 10 Myr are assumed to be still partly embedded
in their birth cloud. They were assigned an SED from a library of Hii region
templates based on the MAPPINGS III library (Groves et al. 2008). Each
template in this library is characterised by five free parameters, two of
which (metallicity and SFR) can be directly obtained from the particle data.
Since the specific choice of the ISM pressure value hardly affects the
broadband SED shape (Groves et al. 2008, Fig. 4), we used a fixed value,
$P=1.38\times 10^{-12}\leavevmode\nobreak\ {\text{Pa}}$. For the compactness,
which essentially determines the dust temperature and thus the shape of the
far-infrared spectrum, we sampled a value from a lognormal distribution
(Kapoor et al. 2021; Trčka et al. 2022) with parameters calibrated on the dust
temperature distribution in observed and simulated star forming regions (Utomo
et al. 2019; Kannan et al. 2020). Finally, the PDR covering factor was set to
$f_{\text{PDF}}={\text{e}}^{-t/\tau}$ with $t$ the particle age and $\tau$ the
PDR clearing timescale, a free parameter in the post-processing recipe. The
value $\tau=3\leavevmode\nobreak\ {\text{Myr}}$ was determined by Trčka et al.
(2022) from a calibration of the integrated TNG50 fluxes against observational
data from the DustPedia nearby galaxy sample (Davies et al. 2017; Clark et al.
2018).

####  2.4.2 The dusty medium

The properties of the transfer medium, in our case the dusty interstellar
medium, are based on the characteristics of the Voronoi gas cells in the
hydrodynamical simulation, which were again downloaded from the TNG50
database. The density of the dust at every location in the SKIRT simulation
volume is based on the assumption that a fixed fraction $f_{\text{dust}}$ of
the metals in the ISM gas is locked up in dust grains, that is

$\rho_{\text{dust}}=\begin{cases}\;f_{\text{dust}}\,Z_{\text{gas}}\,\rho_{\text{gas}}&\quad{\text{if
ISM}},\\\ \;0&\quad{\text{else}},\end{cases}$ (1)

with $Z_{\text{gas}}$ the metallicity and $\rho_{\text{gas}}$ the density of
the gas. To determine which gas cells qualify as ISM gas cells, we applied the
prescription by Torrey et al. (2012, 2019) that separates the hot
circumgalactic medium from the cooler ISM gas. In this framework, gas is
considered as belonging to the ISM if its temperature $T_{\text{gas}}$
satisfies the condition

$\log\left(\frac{T_{\text{gas}}}{\text{K}}\right)<6+0.25\log\left(\frac{\rho_{\text{gas}}}{10^{10}\leavevmode\nobreak\
h^{2}\leavevmode\nobreak\ {\text{kpc}}^{-3}}\right).$ (2)

The density, metallicity and temperature of each gas cell were directly taken
from the cell data. The only free parameter left was the dust-to-metal
fraction $f_{\text{dust}}$, for which Trčka et al. (2022) determined
$f_{\text{dust}}=0.2$ as the best value in combination with the PDR clearing
timescale of $\tau=3\leavevmode\nobreak\ {\text{Myr}}$ (see previous section).
This value is comparable to observational estimates based on different nearby
galaxy samples (e.g. De Vis et al. 2019; Galliano et al. 2021; Zabel et al.
2021).

We assumed a fixed dust grain model at every location in the galaxy. As our
dust grain model, we used the diffuse ISM THEMIS model (Jones et al. 2017). It
consists of a distribution of carbonaceous and silicate grains, with optical
properties based on laboratory data where possible, and can reproduce many
observed properties of the dust in the Milky Way, including the UV to infrared
extinction curve, extinction correlations, the thermal dust emission spectrum,
and red and blue luminescence. For the present paper we focused on the UV to
near-infrared wavelength range, and we did not include diffuse dust emission
in the radiative transfer procedure (ISM dust emission is only important in
the mid-infrared to mm spectral range). Such extinction-only SKIRT simulations
are much faster than simulations that take dust emission into account, such as
the work by Kapoor et al. (2021) and Camps et al. (2022).

The SKIRT code requires a grid structure on which the medium is discretised.
Since the TNG50 simulation uses the AREPO moving mesh technique to solve the
hydrodynamics, we could have opted to use the native Voronoi grid for the
SKIRT post-processing as well. SKIRT is equipped with a mechanism to
efficiently traverse photon packets through a Voronoi grid (Camps et al.
2013), and this approach has been adopted to post-process moving-mesh
hydrodynamical simulations (e.g. Rodriguez-Gomez et al. 2019; Schulz et al.
2020; Camps et al. 2021; Popping et al. 2022; Guzmán-Ortega et al. 2023).
However, like in many other works (e.g. Vogelsberger et al. 2020b; Kapoor et
al. 2021; Trčka et al. 2022; Hsu et al. 2023; Barrientos Acevedo et al. 2023)
we chose to re-grid the dust density distribution onto a hierarchical octree
grid to boost the speed of the radiative transfer process. We used up to 12
levels of refinement to ensure that we fully resolve the dust density
distribution.

####  2.4.3 Instruments and probes

Table 1: Polar angle ($\theta$) and azimuth ($\phi$) of the observer positions with respect to the simulation volume. observer |  $\theta$ [deg] |  $\phi$ [deg]
---|---|---
O1 | $60.279459$ | $99.800435$
O2 | $98.997076$ | $-19.706009$
O3 | $109.839470$ | $-146.239796$
O4 | $30.457794$ | $-94.086488$
O5 | $149.542206$ | $85.913512$
Table 2: Main characteristics of the TNG50-SKIRT Atlas. parent simulation | TNG50-1 (snapshot 99, $z=0$)
---|---
number of galaxies | 1154
stellar mass range | $10^{9.8}-10^{12}\leavevmode\nobreak\ {\text{M}}_{\odot}$
observer positions | 5 random positions (see Table 1)
broadband filters | 18 filters (GALEX, Johnson UBVRI, LSST, 2MASS, WISE W1 and W2)
dust attenuation treatment | dust-obscured and dust-free images
physical parameter maps |  $\Sigma_{\star}$, $\Sigma_{\text{dust}}$, $\langle Z_{\star}\rangle$, $\langle t_{\star}\rangle$
spatial resolution of images/maps | 100 pc
number of pixels | $1600\times 1600$
field-of-view | 160 kpc
images/maps per galaxy | $5\times 40=200$
total number of images/maps | $1154\times 200=230\,800$
total data volume | 1.97 Tb
data access location | https://skirt.ugent.be/data/tng50-skirt-atlas

We defined a set of broadband imagers as instruments in our SKIRT simulation.
Contrary to Kapoor et al. (2021) and Camps et al. (2022), who positioned the
observers at specific sight lines relative to orientation of each galaxy
(face-on, edge-on, and at specific inclination angles), we aimed at arbitrary
viewing points. We used fixed viewing points relative to the simulation box,
which has no connection to the orientation of each individual galaxy. In order
to limit the computation time and the data volume, we settled on
$N_{\text{obs}}=5$ viewing positions per galaxy. These positions were spread
on the unit sphere in optimal arrangement, that is, so as to maximise the
angular distance between them. This problem is generally known in geometry as
the Tammes problem (Tammes 1930). Exact solutions for low values of
$N_{\text{obs}}$ are available in the literature (e.g. Fejes Tóth 1943;
Schütte & van der Waerden 1953; Erber & Hockney 1991), as well as approximate
numerical solutions for larger values of $N_{\text{obs}}$ (e.g. Kottwitz 1991;
Hardin et al. 1994; Steinacker et al. 1996). For the case $N_{\text{obs}}=5$,
two of the observer positions are antipodal, and this semi-redundancy provides
an opportunity to test the accuracy of any analysis method on the images. The
details of the observer positions are provided in Table 1.

For the instruments, a pixel scale of 100 pc provides a nice match to the
spatial resolution of the TNG50 simulation (Pillepich et al. 2019; Nelson et
al. 2019b). The actual size of the detectors, or equivalently, the field-of-
view, does not significantly affect the simulation run time, but it does
impact the data volume. We chose detectors with $1600\times 1600$ pixels,
corresponding to a field of view of 160 kpc on the side, in order to cover the
outer regions of the most extended galaxies.

SKIRT offers the opportunity to define broadband data cubes, that is,
synthetic instruments that contain multiple broadband images automatically
convolved with the correct transmission curves (Camps & Baes 2020, Sect. 4.5).
We generated images in the GALEX FUV- and NUV-bands, the Johnson UBVRI-bands,
the LSST ugrizy-bands, the 2MASS JHK${}_{\text{s}}$-bands, and the WISE W1-
and W2-bands. Together, these bands cover the wavelength range between 0.1 to
5 $\mu$m, the domain of dominance of stellar emission.

Apart from instruments that record the radiation escaping from the simulation
volume, SKIRT offers the opportunity to install probes, which sample numerical
or physical quantities internal to the simulated model. Probes thus provide
relevant diagnostics on the simulation setup and offer an opportunity to
investigate properties of the simulated model that could never be directly
observed from the outside, such as for example the radiation field (Camps &
Baes 2020, Sect. 3.5). Specifically for this project, a set of probes was
implemented in SKIRT to generate intrinsic physical parameter maps for an
arbitrary observer’s position. These maps are generated by projecting the 3D
physical fields such as the stellar mass density on the observer’s plane of
the sky. Concretely, we generated stellar mass surface density maps, stellar-
mass-weighted metallicity and age maps, and dust mass surface density maps for
each of the five observers, with the same pixel scale and field-of-view as the
instruments described above.

In a similar way, the emissivity of the stellar particles within a given
broadband can be projected on an observer’s plane of the sky, resulting in
dust-free broadband images. The main advantage of this approach is that it
does not involve any Monte Carlo radiative transfer calculation, and hence
generates no Monte Carlo noise. We generated a corresponding dust-free image
for each observer and each broadband filter.

####  2.4.4 Numerical parameter settings

Apart from the definition of the sources, the dust medium, the instruments,
and the probes, each SKIRT simulation requires a number of numerical
parameters related to the different wavelength grids used internally or to the
different Monte Carlo optimisation techniques. There was no reason to deviate
from the default values, which are optimised to assure the best performance
(see also Trčka et al. 2022).

A crucial parameter that directly affects the quality of the output data and
the simulation run time is the number of launched photon packets. The optimal
value was determined empirically using a limited number of galaxies of
different stellar masses and sizes. We re-simulated the same galaxies with
increasing number of photon packets until we found convergence on a pixel by
pixel basis. Convergence was defined using the reliability statistics
introduced in SKIRT by Camps & Baes (2018, 2020), based on work in the field
of nuclear particle transport simulations (X-5 Monte Carlo Team 2003). In
order to obtain convergence in the optical r-band out to at least twice the
half-mass radius of the galaxies, we found that at least $10^{9}$ photon
packets are required. For the FUV- and NUV-bands, the corresponding
convergence area is typically smaller due to the lower intrinsic emissivity
and the larger dust attenuation. We fixed the number of photon packets to
$10^{9}$ for all galaxies. For a typical TNG50 galaxy the corresponding SKIRT
run time on a dedicated 64-core machine was 3 to 4 hours, though there was a
large variety depending on the number of stellar particles and the number of
grid cells in the hierarchical octree grid.

##  3 The TNG50-SKIRT Atlas

###  3.1 Atlas characteristics and availability

For each of the 1154 TNG50 galaxies in our selection and for each of the 5
observing positions, the data set consists of dust-obscured and dust-free
images in 18 broadbands, and 4 physical parameter maps. In total, this adds up
to exactly 200 images per galaxy, or 230 800 images in total. Each image or
map is stored as an individual 1600 $\times$ 1600 pixel FITS file. The data
and user guidelines on how to easily access and use them are available on the
SKIRT website.111https://skirt.ugent.be More details on the TSA
characteristics are listed in Table 2.

The broadband images have units of MJy sr-1 and the parameter maps have
natural units (${\text{M}}_{\odot}\leavevmode\nobreak\ {\text{pc}}^{-2}$ for
the stellar and dust surface density maps, dimensionless units for the mean
stellar metallicity maps, and Gyr for the mean stellar age maps). We note that
the images are not convolved with a PSF and are noise-free, except for the
presence of the Monte Carlo noise in the dust-obscured images. Users who wish
to generate synthetic images matching a particular instrument or survey can
convolve the images with the appropriate PSF and add background noise (see
e.g. Rodriguez-Gomez et al. 2019; de Graaff et al. 2022; Guzmán-Ortega et al.
2023).

###  3.2 Example galaxies

Figure 2: Three-colour images for 36 randomly selected galaxies from our
sample, all shown from observer position O1. The images are cutouts with a
field-of-view of 30 kpc, and combine the LSST u-, g-, and z-band images
according to the methodology presented by Lupton et al. (2004). The galaxies
are sorted according to increasing stellar mass.

Fig. 2 shows RGB images for 36 randomly selected galaxies, using the LSST u-,
g-, and z-band images for observer position O1. The galaxies are sorted by
increasing stellar mass. This gallery illustrates the wide variety in optical
morphology in the galaxy population: some galaxies are regular and almost
featureless, others have a clear bulge-disc structure, and some have a
disturbed morphology. In a number of galaxies, the effects of dust attenuation
are clearly visible. Also the optical colours and SFRs of the galaxies vary
widely, both globally and on spatially resolved scales.

Figure 3: Representative images and physical parameter maps for the spiral
galaxy TNG000008 from observer position O2 ($i=47.6^{\circ}$). Top row:
stellar mass surface density, mean stellar metallicity, mean stellar age, and
dust mass surface density. Bottom row: u-, g-, i-, and
${\textit{K}}_{\text{s}}$-band images. All images and parameter maps zoom into
the central $30\times 30$ kpc region.

Figure 4: Same as Fig. 3, but for the massive early-type galaxy TNG096764
observed from observer position O1 ($i=35.9^{\circ}$).

Figs. 3 and 4 show a more detailed view on two handpicked galaxies from the
data set. Fig. 3 shows TNG000008, a spiral galaxy with stellar mass
$M_{\star}=3.73\times 10^{10}\leavevmode\nobreak\ {\text{M}}_{\odot}$ and
${\text{SFR}}=3.98\leavevmode\nobreak\ {\text{M}}_{\odot}\leavevmode\nobreak\
{\text{yr}}^{-1}$, here seen at an intermediate inclination. The stellar mass
distribution is characterised by a small but dense central component of metal-
rich stars, whereas the disc is on average less metal-rich. The mass-averaged
age of the stellar population is around 7 Gyr for the central bulge component
and only about 3 Gyr for the populations in the spiral arms. The dust
distribution is strongly concentrated in the central regions and the spiral
arms and has a filamentary morphology. The broadband images show the signature
as expected based on the physical parameter maps. The u-band image is
dominated by the young stars and has a very clumpy appearance. It also shows
clear signs of dust attenuation at the locations of the most prominent dust
mass surface density enhancements. Moving to longer wavelengths, the images
become less clumpy, the apparent signatures of attenuation gradually
disappear, and the bulge gradually becomes more prominent.

Fig. 4 shows a very different galaxy, TNG096764, a massive early-type galaxy
with $M_{\star}=1.05\times 10^{11}\leavevmode\nobreak\ {\text{M}}_{\odot}$ and
no ongoing star formation. The stellar mass surface density map shows a smooth
distribution without obvious substructure, except a small bar in the central
regions (viewed from this single observer’s position, the central feature
could be either a bar or an edge-on disc, but the combination of the different
viewing angles shows that it is a bar rather than a disc). The stellar
population shows a strong metallicity gradient, and the bar seen in the
stellar mass surface density map stands out by its high metallicity. The same
structure is also seen in the age map, where the feature stands out due to a
slightly younger mean stellar age. On average, the stellar populations are
relatively old and the age gradients modest. Furthermore, the galaxy displays
a ring-like structure with a radius of about 5 kpc that is slightly younger
($\sim$6 Gyr) than the overall stellar population ($\sim$9 Gyr). Also
interesting is the dust mass surface density map, which shows a conspicuous,
albeit very low surface density, spiral-arm-like feature. Since there is no
ongoing star formation and hardly any dust attenuation, the morphology of all
broadband images is similar. It is very smooth without much substructure,
except the inner bar and a faint shell-like structure in the bottom right
quadrant. The u-band image of this galaxy is smoother than the u-band image of
the spiral galaxy shown in Fig. 3.

##  4 The UVJ diagram

###  4.1 Comparison of TNG50, TNG100, and EAGLE

Figure 5: The UVJ plane of the TNG50, EAGLE, and TNG100 cosmological
hydrodynamical simulations. Each panel contains only galaxies at $z=0$ within
the same stellar mass range ($10^{9.8}\leavevmode\nobreak\
{\text{M}}_{\odot}<M_{\star}<10^{12}\leavevmode\nobreak\ {\text{M}}_{\odot}$).
All colours are calculated from SKIRT-generated fluxes or images that take
dust attenuation into account. The colour scale represents the median sSFR of
all galaxies with UVJ colours within each pixel. The dotted line in each panel
indicates the separation between the quiescent and star-forming galaxy
populations and is taken from Donnari et al. (2019). Figure 6: Physical
properties in the UVJ plane, with (top row) and without (bottom row) dust
attenuation. The different columns correspond to different physical
parameters, indicated at the top of the column. The colour scale represents
the median value of the physical parameter within each bin in UVJ colours.

The UVJ diagram was first presented by Wuyts et al. (2007) and Williams et al.
(2009) as a way to distinguish between quiescent and star-forming galaxies. A
single optical colour is not enough to separate these two classes as different
combinations of dust attenuation and SFR can give rise to similar optical
colours. However, the combination of an optical and an optical–NIR colour
proves successful at making that distinction. The UVJ method has been widely
applied to select quiescent galaxies from observed galaxy samples (Whitaker et
al. 2013; Straatman et al. 2014; Barro et al. 2014; Papovich et al. 2015,
2018; Fang et al. 2018; Tan et al. 2022; Valentino et al. 2023) and from
cosmological hydrodynamical simulations (Davé et al. 2017; Trayford et al.
2017; Donnari et al. 2019; Akins et al. 2022; Kurinchi-Vendhan et al. 2023).

The first panel of Fig. 5 shows the UVJ diagram for the galaxies in our TSA.
We calculated the rest-frame U-, V-, and J-magnitudes by integrating the
surface brightness of our images over the entire field-of-view. The colour
scale in the UVJ diagram represents the sSFR, which is taken directly from the
TNG50 database. The other panels show equivalent UVJ diagrams based on other
simulated galaxy data sets, all at $z=0$ and within the same stellar mass
range we used. The second panel contains the same set of galaxies, but used
the ‘random’ viewing position flux values generated by Trčka et al. (2022).
The third panel is based on SKIRT-generated fluxes presented by Camps et al.
(2018) for the flagship EAGLE simulation. The final panel corresponds to the
TNG100 simulation, for which Gebek et al. (2024) generated broadband fluxes
using the same methodology as applied by Trčka et al. (2022) for the TNG50
simulation.

In all panels, the data show a clear correlation between the UVJ colours and
the sSFR. The dotted line, taken from Donnari et al. (2019), separates the
quiescent galaxy population from the star-forming galaxies. For all four
cases, the separation line corresponds roughly to a fixed ${\text{sSFR}}\sim
10^{-10.7}\leavevmode\nobreak\ {\text{yr}}^{-1}$. The general agreement
between the different simulations is a good sanity check for the SKIRT
calculations executed in this work.

The differences between the different panels are at least as interesting. The
main difference is the coverage of the UVJ diagram, which can primarily be
attributed to the sample size. The TNG100 fluxes generated by Gebek et al.
(2024) reach lower ${\textit{U}}-{\textit{V}}$ colours for a fixed
${\textit{V}}-{\textit{J}}$ colour than the other diagrams. The galaxies
populating these blue regions at the bottom of the UVJ diagram are among the
most actively star-forming galaxies. A remarkable difference is that the
distribution of star-forming galaxies in our new TNG50 UVJ diagram extends
towards very red colours in the diagonal direction, beyond the vertical
separation line at ${\textit{V}}-{\textit{J}}=1.6$, whereas it ends rather
abruptly before this line in the other three panels (apart from some scattered
galaxies in the TNG100 diagram). The galaxies populating this part of the UVJ
diagram are very actively star-forming with
${\text{sSFR}}>10^{-10}\leavevmode\nobreak\ {\text{yr}}^{-1}$. At the same
time they have very red colours, which implies that they must be heavily
attenuated. It is, at first sight, surprising that these galaxies are lacking
in the UVJ diagram based on the fluxes calculated by Trčka et al. (2022) since
the galaxy samples used for the two TNG50 diagrams are exactly the same. The
reason is that we consider four different random viewing positions for each
galaxy, compared to a single random flux for the Trčka et al. (2022)
catalogue. As one can visually infer from Fig. 2, the level of dust
attenuation in the TNG50 galaxies can differ substantially depending on the
viewing angle, which can move galaxies across the UVJ plane for different
observer’s positions. These red, star-forming galaxies are also missing from
the EAGLE and TNG100 UVJ diagrams, and this difference cannot be due to the
different sample size. In these cases, the reason is probably the lower
spatial resolution of the EAGLE and TNG100 simulations compared to the TNG50
simulation, which causes a puffier and more diffuse dust distribution, and
therefore a shallower relation between attenuation and inclination (e.g.
Trayford et al. 2017).

A final note we observe that in our new TNG50 UVJ diagram, and to a lesser
degree also in the TNG100 diagram, we still note a clear anti-correlation
between the ${\textit{U}}-{\textit{V}}$ colour and the sSFR for the galaxies
beyond the vertical separation line at ${\textit{V}}-{\textit{J}}=1.6$. More
specifically, the galaxies with the reddest ${\textit{U}}-{\textit{V}}$
colours are quiescent with ${\text{sSFR}}<10^{-11}\leavevmode\nobreak\
{\text{yr}}^{-1}$, in spite of lying in the star-forming region of the UVJ
diagram. One could therefore question the shape of the quiescent galaxies
region, as also raised by Belli et al. (2019).

###  4.2 Dust attenuation and physical properties in the UVJ diagram

While the UVJ diagram is widely used to discriminate between star-forming and
quiescent galaxies, its origin and reliability is being investigated in more
detail (Belli et al. 2019; Leja et al. 2019; Díaz-García et al. 2019; Wu et
al. 2020; Nagaraj et al. 2022; Antwi-Danso et al. 2023). More specifically, it
has been investigated to which degree the UVJ colours can be used to constrain
other physical properties of galaxies, and how dust attenuation affects the
UVJ colours of galaxies. The general effect of dust attenuation is that
galaxies move upwards in the UVJ diagram, roughly parallel to the diagonal
separation line. The exact direction depends on the shape of the attenuation
curve, which can vary significantly among galaxies (e.g. Salmon et al. 2016;
Leja et al. 2017; Salim et al. 2018; Narayanan et al. 2018; Qin et al. 2022;
Zhang et al. 2023). The attenuation in our models is calculated in full 3D and
takes into account absorption and scattering, which can sometimes lead to
counterintuitive effects (Witt et al. 1992; Byun et al. 1994). Moreover, our
models have more complex, and hopefully more realistic, star formation
histories than the parametric models often assumed.

In the top row panels of Fig. 6, we show the UVJ diagram based on the dust-
obscured images for the galaxies in our sample colour-coded by six different
physical parameters. The bottom row contains the same information, but now for
UVJ colours based on the dust-free images. The correlation between UVJ colours
and sSFR (see also Fig. 5) is obvious for both the dust-obscured and dust-free
diagrams, but it is not the only systematic trend. Also mean stellar age, mean
metallicity, V-band mass-to-light ratio, and dust-to-stellar mass ratio show a
clear trend with the UVJ colours. These results are well in agreement with the
results obtained by Leja et al. (2019). By means of a Bayesian inference
method applied to synthetic SED models, they demonstrated that the mass-to-
light ratio is well constrained by UVJ colours alone, whereas the trends with
age and metallicity are induced by galaxy scaling relations.

Figure 7: Age–colour relation for quiescent galaxies in the TSA. The quiescent
galaxy population is defined by means of the Donnari et al. (2019) separation
line in the UVJ diagram.

Looking at the quiescent galaxies region of the second panel on the top row,
one can see that the mean stellar ages of the galaxies systematically increase
when moving upward in the diagonal direction. To investigate this in more
detail, we used the rotated coordinate system on the UVJ diagram introduced by
Fang et al. (2018). In this system, the rotated axes $S_{\\!\text{Q}}$ and
$C_{\text{Q}}$, defined as

$\displaystyle S_{\\!\text{Q}}=0.75\,(V-J)+0.66\,(U-V),$ (3) $\displaystyle
C_{\text{Q}}=-0.66\,(V-J)+0.75\,(U-V),$ (4)

run parallel and perpendicular to the boundary of the quiescent box,
respectively. In Fig. 7 we show the relation between the colour index parallel
to the separation line and the logarithm of the mean stellar age of each
galaxy in the quiescent region. We recovered a reasonably strong linear
correlation ($R^{2}=0.44$), which means that the ages of quiescent galaxies
can in principle be estimated from UVJ colours alone. This supports the
conclusions by Belli et al. (2019), who derived a similar linear relation
between the logarithmic median stellar age and the $S_{\\!\text{Q}}$ index.

Comparing the panels on the top row with the corresponding ones on the bottom
row, a number of interesting effects of dust attenuation can be discerned. One
can immediately note the difference in coverage of the UVJ plane, which is
most obvious for the star-forming galaxy population. The dust-free star-
forming galaxies predominantly occupy a region with
$0.5\lesssim{\textit{V}}-{\textit{J}}\lesssim 1.1$ which moves into the
quiescent galaxies region as soon as ${\textit{U}}-{\textit{V}}\gtrsim 1.7$.
The dust-obscured star-forming galaxies, on the other hand, occupy a much more
extended region that runs parallel to the separation line up to
${\textit{V}}-{\textit{J}}>1.5$. The galaxies in this new territory have high
sSFR values, and so they moved from the bottom left corner in the dust-free
UVJ diagram. Interestingly, a large number of these galaxies are oriented
almost edge-on, in line with the findings by Patel et al. (2012). This
segregation by inclination is clearly visible when comparing the panels in the
fifth column. While the galaxies in the bottom panel have a uniform
distribution in inclination with a median value of about 60∘ in every pixel of
the parameter space, the distribution is clearly separated in the top panel,
in particular for the star-forming galaxies: low-inclination galaxies remain
at the blue side of the diagram, whereas high-inclination galaxies move
towards the red side. At the extreme end of the star-forming distribution we
find edge-on galaxies with high sSFR values and very high dust-to-stellar-mass
ratios.

There is also a difference in the dust-free and the dust-obscured UVJ diagram
for the quiescent galaxy population. In the dust-free diagram it forms a well-
defined sequence out to ${\textit{V}}-{\textit{J}}\approx 1.5$, populated
exclusively by galaxies with very low sSFR values, old ages, high
metallicities, large mass-to-light ratios, and very small dust-to-stellar-mass
ratios. When dust attenuation is turned on, however, the population extends to
redder colours, but most importantly, the region in the UVJ space below the
original tight sequence is populated, across the boundary line into the star-
forming galaxies regimes. The galaxies in this new region are, again, oriented
almost edge-on and heavily attenuated. We can quantify the nature of these
additional galaxies by calculating some global statistics of the two different
populations (as defined by the dotted line region in the plots) with and
without dust attenuation. In the dust-free case, the quiescent galaxy region
contains just 36% of the total stellar mass budget, and the mean sSFR and
stellar age are $\langle{\text{sSFR}}\rangle=10^{-11.52}\leavevmode\nobreak\
{\text{yr}}^{-1}$ and $\langle t\rangle=8.84$ Gyr. In the dust-obscured case,
the pollution by star-forming galaxies increases the fraction of the stellar
mass budget of the quiescent galaxy region to 48%. The mean sSFR increases by
0.4 dex to $\langle{\text{sSFR}}\rangle=10^{-11.12}\leavevmode\nobreak\
{\text{yr}}^{-1}$, and the mean stellar age decreases to $\langle
t\rangle=8.55$ Gyr. In summary: dust attenuation pollutes the quiescent galaxy
region with younger and more actively star-forming highly inclined galaxies.

##  5 Discussion and conclusion

###  5.1 Summary

The ambition of this work was to generate, present, and publicly release a
synthetic UV–NIR broadband image atlas for a complete stellar-mass selected
sample of 1154 galaxies extracted from the $z=0$ snapshot of the TNG50
cosmological simulation (Pillepich et al. 2019; Nelson et al. 2019b). The
images were generated with the SKIRT radiative transfer code (Camps & Baes
2015, 2020) and account for different stellar populations and absorption and
scattering by interstellar dust in a realistic 3D setting.

For each galaxy, we generated a suite of 100 pc resolution images in 18
broadband filters, for five different observer positions. In addition to the
dust-obscured images, we also released synthetic images without dust
attenuation, and stellar mass surface density, mean stellar age, mean stellar
metallicity, and dust mass surface density maps. While other teams have
already released synthetic image datasets for cosmological hydrodynamical
simulations (e.g. Torrey et al. 2015; Trayford et al. 2015; Rodriguez-Gomez et
al. 2019; Kapoor et al. 2021; Camps et al. 2022; Guzmán-Ortega et al. 2023),
we argue that the present atlas is unique in its kind due the realism of both
the underlying simulation and the radiative transfer treatment, the large
sample size, the high spatial resolution, the number of filters, and the
combination of matching images and physical parameter maps.

As a sanity check and a first application of our image atlas, we investigated
the UVJ diagram. Comparing our UVJ diagram with the one based on the fluxes
generated by Trčka et al. (2022) for the same TNG50 galaxy sample, and with
the EAGLE and TNG100 UVJ diagrams we found excellent agreement in terms of the
relation between sSFR and UVJ colours. The diagrams also show some interesting
differences, mainly in the coverage of the UVJ diagram. In particular, the
distribution of star-forming galaxies in our new TNG50 UVJ diagram extends
towards very red colours in the diagonal direction, whereas the other diagrams
lack these very actively star-forming and heavily obscured galaxies. These
differences can be interpreted as a result of different sample size and the
difference in spatial resolution.

We also investigated the trends of galaxy physical parameters in the UVJ
diagram. We found that, apart from the strong correlation with sSFR, the UVJ
colours also show systematic trends with mean stellar age, mean stellar
metallicity, V-band mass-to-light ratio, and dust-to-stellar-mass ratio. We
found a reasonably strong positive correlation between the mean stellar age
and the UVJ colours for the quiescent galaxy population, which suggests that
the ages of quiescent galaxies can be well constrained by UVJ colours alone
(Belli et al. 2019). Finally, we investigated the effect of dust attenuation
on the distribution of the galaxy population in the UVJ diagram. As expected,
dust attenuation spreads the galaxy population towards redder colours parallel
to the separation line between the quiescent and star-forming galaxy
populations. In the dust-free UVJ diagram the quiescent galaxy population
forms a well-defined sequence populated exclusively by galaxies with very low
sSFR values and old ages, but dust attenuation pollutes this quiescent galaxy
region with younger and more actively star-forming galaxies. Dust attenuation
generates a clear separation in inclination of the star-forming galaxies: low-
inclination galaxies remain at the blue side of the diagram, whereas high-
inclination galaxies move towards the red side. The reddest star-forming
galaxies are edge-on, dusty galaxies with high sSFR values.

###  5.2 Possible applications

The prime goal of this paper was to present and publicly release the TSA. We
demonstrated its usefulness by investigating the UVJ diagram. We hope that
this image atlas can be used for many more applications.

A prime application is the connection between the morphology of galaxies,
their fundamental physical properties, and the environment in which they
reside (e.g. Conselice 2003, 2014; Blanton & Moustakas 2009; Holwerda 2021).
The availability of multi-band dust-obscured and dust-free images allows for a
systematic investigation of the wavelength dependence of galaxy morphology
(Muñoz-Mateos et al. 2009; Kelvin et al. 2012; Vulcani et al. 2014; Baes et
al. 2020; Nersesian et al. 2023; Martorano et al. 2023) and the effects of
dust attenuation on photometric and morphological parameters (Gadotti et al.
2010; Pastrav et al. 2013a, b; Savchenko et al. 2023).

In a companion paper to this release paper (Baes et al. 2024) we used the TSA
to investigate the wavelength dependence of the effective radii of galaxies.
In the near future we plan to employ single or multiple-component Sérsic
fitting and non-parametric morphological indices to quantify morphology. All
global physical properties (also including dark matter properties), the
intrinsic particle and cell data, and the entire history for all galaxies can
be readily accessed through the TNG public database (Nelson et al. 2019a),
which allows for a thorough investigation on what drives galaxy morphology.

In the past few years, several well-known global galaxy scaling relations have
been investigated on local, sub-kpc scales as well. Examples include the local
dust scaling relations (Viaene et al. 2014; Casasola et al. 2022), the
spatially resolved star-forming main sequence (Cano-Díaz et al. 2016; Hsieh et
al. 2017; Morselli et al. 2020; Pessa et al. 2022; Abdurro’uf et al. 2022b;
Baker et al. 2022), the resolved stellar mass gas metallicity relation
(Rosales-Ortega et al. 2012; Barrera-Ballesteros et al. 2016; Gao et al. 2018;
Boardman et al. 2023; Baker et al. 2023), or the resolved stellar mass stellar
metallicity relation (González Delgado et al. 2014; Zibetti et al. 2020;
Neumann et al. 2021; Zibetti & Gallazzi 2022; Pessa et al. 2023). Our image
and physical parameter map database can be used to investigate the
universality of these local scaling relations and to determine the physical
scales at which they potentially break down.

We plan to address several of the questions raised above. However, we also
publicly release these data and warmly invite the community to use them in any
way they see fit.

###  5.3 Caveats and future work

While we believe that the current image atlas is sufficiently rich and
realistic to allow for a range of applications, we are aware that it also has
its caveats and limitations. A first important aspect is that our data are
built on simulated galaxies extracted from the TNG50 cosmological
hydrodynamical simulation. While this simulation is generally considered as
one of the most powerful large-volume simulations, it comes with its own
caveats and limitations. One of them is that the TNG model was designed and
calibrated at the resolution of the original Illustris simulation, while the
TNG50 simulation has a roughly 16 times better mass resolution. This improved
resolution results in somewhat larger galaxy masses and SFRs (Pillepich et al.
2018a, b, 2019; Donnari et al. 2019; Trčka et al. 2022).

A second limitation of our atlas is that it is limited to the UV–NIR range,
that is, the range where stellar emission dominates the SED. This limitation
is not inherent to SKIRT code: the code has been used to generate synthetic
SEDs and images for galaxies that cover the entire UV-mm wavelength range. The
main reason is computational: to simulate the UV–NIR range only dust
attenuation and no dust emission is required. SKIRT simulations with dust
emission are computationally much more demanding (up to an order of magnitude,
depending on the details of the simulation) compared to attenuation-only
simulations.

In previous post-processing work of cosmological hydrodynamical simulations,
we encountered difficulties to reproduce the UV and MIR fluxes and colours of
observed galaxies: we typically found insufficient UV attenuation and too much
emission in the MIR (Baes et al. 2019; Trčka et al. 2020, 2022; Kapoor et al.
2021; Camps et al. 2022). We argued that the MAPPINGS III templates we use for
the young stellar particles are at least partly responsible for these
discrepancies. Kapoor et al. (2023) recently generated a new template library,
called TODDLERS, to be used in SKIRT with the aim of addressing this problem.
The first tests of this new library are very promising (Kapoor et al., in
prep.). Our ambition is to rerun our image library with this new emission
library in the near future, extending the range to mm wavelengths. This new
emission library is expected to not affect the optical images, but will
probably improve the UV images. We advise users to take this caveat into
account when they use our data.

Looking forward, we see this UV–NIR broadband image atlas as an intermediate
step in an effort to generate increasingly more realistic synthetic data
products for simulated galaxies. As discussed above, our next ambition is to
extend this image database to the UV–mm wavelength range, incorporating the
new TODDLERS library. The step beyond that could be a transition from
broadband imaging to full spectral resolution. Such an effort could be similar
to the works of Bottrell & Hani (2022), Nanni et al. (2022, 2023), and
Sarmiento et al. (2023), but with a completely self-consistent treatment of
dust attenuation and emission, and ideally covering the entire UV–mm
wavelength range. Several intermediate steps towards realising this ambition
have already been taken or are under development: the TODDLERS templates have
full spectral resolution and contain a detailed treatment of nebular emission
lines, SKIRT has full support for gas and stellar kinematics (Camps & Baes
2020; Barrientos Acevedo et al. 2023), and we are currently working on a sub-
grid model for the multi-phase ISM, inspired by Olsen et al. (2021) and Ramos
Padilla et al. (2021, 2023).

Finally, applying the SKIRT radiative transfer post-processing recipe for
different redshift snapshots would open up the possibility to directly
investigate and test the cosmic evolution of galaxy morphology and many of the
scaling relations mentioned above.

###### Acknowledgements.

MB, NA, IK, and MM acknowledge financial support by the Flemish Fund for
Scientific Research (FWO-Vlaanderen) through the research projects G030319N,
G0G0420N, and G037822N. AG and BVM acknowledge the support by FWO-Vlaanderen
through the PhD Fellowship Grants 11H2121N and 1193222N, respectively. CT
acknowledges the INAF grant 2022 LEMON. This project has received funding from
the European Research Council (ERC) under the European Union’s Horizon 2020
research and innovation program (grant agreement No. 683184).

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