# How Dust Models Shape High-z Galaxy Morphology: Insights from the NewCluster Simulation

Gyeong-Hwan Byun, J K Jang, Zachary Scofield, Eunmo Ahn, Maarten Baes, Yohan Dubois, San Han, Seyoung Jeon, Juhan Kim, Christophe Pichon, et al.

► To cite this version:

Gyeong-Hwan Byun, J K Jang, Zachary Scofield, Eunmo Ahn, Maarten Baes, et al. How Dust Models Shape High-z Galaxy Morphology: Insights from the NewCluster Simulation. The Astrophysical Journal, 2025, 992 (1), pp.92. 10.3847/1538-4357/adfed9. hal-05499633

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# How Dust Models Shape High-z Galaxy Morphology: Insights from the NewCluster Simulation

GYEONG-HWAN BYUN,¹ J. K. JANG,¹ ZACHARY P. SCOFIELD,¹ EUNMO AHN,¹ MAARTEN BAES,² YOHAN DUBOIS,³ SAN HAN,³ SEYOUNG JEON,¹ JUHAN KIM,⁴ CHRISTOPHE PICHON,³,⁵ JINSU RHEE,¹,³,⁶ FRANCISCO RODRÍGUEZ MONTERO,³ AND SUKYOUNG K. YI¹

¹Department of Astronomy and Yonsei University Observatory, Yonsei University, Seoul 03722, Republic of Korea

²Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281, B-9000 Gent, Belgium

³Institut d' Astrophysique de Paris, Sorbonne Universités, et CNRS, UMP 7095, 98 bis bd Arago, 75014 Paris, France

⁴Korea Institute of Advanced Studies, 85 Hoegiro, Dongdaemun-gu, Seoul 02455, Republic of Korea

⁵Kyung Hee University, Dept. of Astronomy & Space Science, Yongin-shi, Gyeonggi-do 17104, Republic of Korea

⁶Korea Astronomy and Space Science Institute, 776, Daedeokdae-ro, Yuseong-gu, Daejeon 34055, Republic of Korea

Submitted to ApJ

## ABSTRACT

Dust plays a pivotal role in shaping the observed morphology of galaxies. While traditional cosmological simulations often assume a fixed dust-to-gas (DTG) or dust-to-metal (DTM) mass ratio to model dust effects, recent advancements have enabled on-the-fly (OTF) dust modeling that captures the spatial and temporal evolution of dust. In this work, we investigate the impact of dust modeling on galaxy morphology using the NEWCLUSTER simulation, which implements a detailed OTF dust model. We generate mock images of NEWCLUSTER galaxies under both OTF and fixed DTM models using the radiative transfer code SKIRT, and compare their morphology to JWST observations. We measure morphology indices and use the G-M$_{20}$ test to classify galaxies. We find that the OTF galaxy models exhibit brighter centers and more pronounced bulges than those of the fixed DTM models, resulting in a lower late-type galaxy (LTG) fraction, particularly at high redshifts. This central brightening is linked to a phenomenon we refer to as the DTM cavity, a localized depression in the DTM ratio driven by intense bulge starbursts. Our results highlight the importance of modeling dust evolution in a physically motivated manner, as fixed DTM models fail to capture key morphological features.

## 1. INTRODUCTION

Morphology is one of the most fundamental properties of galaxies, providing a first impression of their nature. Since Hubble (1926) first proposed a morphological classification scheme, systematic studies of galaxy morphology have been conducted from various perspectives (e.g., de Vaucouleurs 1959; van den Bergh 1960; Sandage 1961; Dressler et al. 1994; Conselice 2003; Lotz et al. 2008). These studies have revealed that morphology not only characterizes intrinsic structural features but also reflects the complex interplay between galaxies and their surrounding environments, as well as the impact of internal feedback mechanisms.

In the framework of hierarchical structure formation (e.g., White & Rees 1978; Davis et al. 1985; Kauffmann et al. 1993), galaxies are understood to form and evolve within dark matter halos. Late-type galaxies (LTG), such as disk galaxies, are expected to preferentially form in halos with higher angular momentum (Fall & Efstathiou 1980; Mo et al. 1998; van den Bosch et al. 2002;

Agertz et al. 2011; Somerville & Davé 2015), and gradually stabilize through disk settling mechanisms (Kassin et al. 2012; McCluskey et al. 2024). However, over cosmic time, these disk galaxies may transform into spheroidal, dispersion-dominated galaxies. While mergers have often been invoked to explain these transformations, recent studies have emphasized that the evolutionary pathways are considerably more complex, involving multiple interacting physical processes (Negroponte & White 1983; Moore et al. 1996; Sales et al. 2012; Martin et al. 2018).

Therefore, accurately understanding the evolution of galaxy morphology is crucial for testing galaxy formation theories. Since morphological transformations occur early in the history of structure formation (Leste et al. 2024; Lee et al. 2024), high-redshift galaxy morphology provides key insights into the initial stages of galaxy evolution. Despite its significance, studying high-redshift galaxy morphology remains challenging due to observational limitations. The advent of JWST has ush-
ered in a new era, enabling high-redshift galaxy mor-
phology to be determined in unprecedented detail (e.g.,
Ferreira et al. 2022; Kuhn et al. 2024) and allowing
for direct comparisons with theoretical predictions from
cosmological simulations. However, accurately inter-
preting these observations requires careful consideration
of dust, which can significantly alter the appearance of
galaxies by absorbing and scattering light.

Given the difficulty of constraining dust properties
observationally at high redshifts, cosmological hydro-
dynamic simulations provide a valuable alternative for
investigating its impact on galaxy morphology. Con-
sequently, many studies have incorporated dust effects
into simulations when producing mock observations that
can be directly compared with JWST and other obser-
vational data. These efforts are crucial for bridging the
gap between theory and observation.

However, traditional simulations without explicit dust evolution models have approximated dust distribution using a fixed dust-to-gas (DTG) or dust-to-metal (DTM) ratio (e.g., Narayanan et al. 2010; Somerville et al. 2012; Hayward et al. 2014; Lacey et al. 2016; Camps et al. 2016; Rodriguez-Gomez et al. 2019). This approach assumes a uniform relationship between dust and metals, thereby simplifying the modeling process, yet it fails to capture the self-consistent nature of dust evolution (Inoue 2003; Hirashita & Kuo 2011; Aoyama et al. 2017).

Early studies suggested that adopting a fixed DTM
ratio across all galaxies provided a reasonable approxi-
mation (Dwek 1998; Zafar & Watson 2013). However,
recent research has demonstrated that DTG and DTM
values exhibit significant variations depending on galaxy
properties (Rémy-Ruyer et al. 2014; Popping et al. 2017;
De Vis et al. 2019) and even within individual galaxies
(Mattsson & Andersen 2012). These findings highlight
the need for a more realistic dust modeling approach.
Moreover, this issue becomes particularly problematic
at high redshifts. Observations from JWST and ALMA
suggest that the dust mass, DTG ratios, and DTM ra-
tios in the high-redshift universe differ from those in the
local universe (Algera et al. 2025). Consequently, the
use of a fixed DTM ratio in galaxy morphology studies
may introduce significant biases, limiting the ability of
simulations to produce realistic mock observations.

To overcome these limitations, a more advanced ap-
proach is needed that accurately captures the evolving
nature of dust in galaxies. In response to this need,
“on-the-fly” (OTF) dust models have been developed
(e.g., Bekki 2013; Hirashita 2015; McKinnon et al. 2017;
Li et al. 2019; Aoyama et al. 2020; Dubois et al. 2024;
Choban et al. 2024). Unlike fixed DTM models, OTF

models self-consistently track dust formation and de-
struction, providing a more physically motivated repre-
sentation of dust properties. This advancement facili-
tates a more accurate assessment of how dust influences
the observed morphology.

In this work, we use the NEWCLUSTER simulation, a
high-resolution cosmological zoom-in simulation that in-
corporates detailed dust physics. The NEWCLUSTER re-
gion traces a 4 σ overdensity, providing a representa-
tive environment for studying massive galaxies in the
early universe and enabling meaningful comparisons
with deep observations. With its high spatial and mass
resolution (see Section 2), NEWCLUSTER resolves internal
galactic structures and allows for a direct assessment
of how dust affects observed morphology. To obtain
realistic galaxy morphologies, we perform mock obser-
vations using SKIRT (Baes & Camps 2015; Camps &
Baes 2020). We investigate how the choice of dust mod-
eling—specifically, OTF versus fixed DTM ratio mod-
els—affects simulated galaxy morphology at high red-
shift. We then discuss how these morphological dif-
ferences arise from a physical mechanism. Finally, we
argue that a realistic treatment of dust is essential as
cosmological simulations increasingly aim to model dust
evolution in a physically motivated way.

2. SIMULATION

2.1. *NewCluster*

We utilize the NEWCLUSTER simulation (Han et al. 2025b), a high-resolution zoom-in cosmological simulation using RAMSES-yOMP (Han et al. 2025a), an OpenMP version of RAMSES (Teyssier 2002). The simulation begins with a periodic box of side length 100 Mpc/h and focuses on a single galaxy cluster at the center of the zoom-in region. The target cluster has a virial mass of ~4.7 × 10¹⁴ M☉, with a zoom-in region radius of 3.5Rvir ~ 17.7 Mpc/h at z = 0. The mass resolution of dark matter (DM) in the zoom-in region is mDM ~ 1.3 × 10⁶ M☉, and for stellar particles, m⋆ ~ 2 × 10⁴ M☉. The best spatial resolution varies with the scale factor of the simulation, ranging from 53 to 107 pc in physical size. NEWCLUSTER adopts the WMAP-7 cosmological parameters (H₀ = 70.3 km s⁻¹ Mpc⁻¹, Ωm = 0.272, Ωb = 0.0455, ΩΛ = 0.728, σ8 = 0.810 and n_s = 0.967) (Komatsu et al. 2011).

NEWCLUSTER adopts radiative cooling and heating based on the assumption of collisional ionization equilibrium. Radiative cooling rates are calculated using the tabulations from Sutherland & Dopita (1993), which allow primordial gas to cool down to 10⁴ K. Below this temperature, metal cooling further enables gas to cool to as low as 0.1 K. Radiative heating from a uni-
form UV background is activated at $z = 10$, following the prescription of Haardt & Madau (1996). Additionally, as the gas becomes optically thick, it self-shields the UV background by a factor of $\exp(-n_H/n_{shield})$, where $n_{shield}$ is self shielding threshold density set to $0.01 \text{ H cm}^{-3}$.

NEWCLUSTER incorporates both stellar and supermassive black hole (SMBH) physics. Star formation occurs in gas cells with a hydrogen number density exceeding the threshold of $n_{th} = 5 \text{ cm}^{-3}$, with the star formation efficiency from Federrath & Klessen (2012). To realistically model the chemical evolution of galaxies, NEWCLUSTER includes Type II and Type Ia supernovae (SNe), as well as stellar winds, and tracks the evolution of ten chemical elements: H, D, He, C, N, O, Mg, Fe, Si, and S. To compute chemical enrichment, NEWCLUSTER adopts the Chabrier initial mass function (IMF) (Chabrier 2003) for each stellar particle. Considering each stellar particle as a simple stellar population, the chemical enrichment of ten chemical elements is computed using the Starburst99 code (Leitherer et al. 1999). The yields and energy outputs from stellar winds are based on Schaller et al. (1992); Maeder & Meynet (2000), and they are directly injected into the simulation grid cell containing the corresponding stellar particle.

Every stellar particle triggers SN II explosions once it reaches an age of 5 Myr. For SN II events, the chemical yields depend on the progenitor mass and follow the model of Kobayashi et al. (2006) for progenitors in the range of $8-50 \text{ M}_\odot$. The yields of SNe Ia are derived from Iwamoto et al. (1999), with their frequency determined by a delay time distribution modeled as a power-law function. The feedback energy of a single SN Ia is set to $10^{51} \text{ erg}$. Both the SNe II and SNe Ia feedback are implemented using the mechanical feedback scheme of Kimm & Cen (2014).

NEWCLUSTER incorporates dual-mode active galactic nucleus (AGN) feedback, following the model of Dubois et al. (2012), where the mode is determined by the Eddington ratio. When the Eddington ratio is below 0.01, black holes release mass and energy as jets (jet mode), while above 0.01, they emit thermal energy isotropically (quasar mode). The Eddington ratio is capped at 1 to ensure it does not exceed the Eddington limit.

NEWCLUSTER has passed $z = 0.8$, and a more detailed description of the simulation and some key features of galaxies in NEWCLUSTER can be found in (Han et al. 2025b).

## 2.2. Dust models

In NEWCLUSTER, the dust component is treated as a passive scalar parameter that evolves on the fly. The

dust model implementation is similar to that of Dubois et al. (2024). It should be noted that Dubois et al. (2024) used idealized MW-like galaxy simulations, while NEWCLUSTER is a cosmological simulation. Any deviations caused by this difference with Dubois et al. (2024) are explicitly highlighted in this section.

The dust size distribution of NEWCLUSTER follows the two-size approximation from Hirashita (2015), dividing the grains into small and large size populations. The small and large grain populations have sizes of 5 nm and 0.1 µm, respectively. Each size population is affected differently by various dust evolution processes. Dust models with a two-size approximation are consistent with results obtained by using a full treatment of the size distribution (Aoyama et al. 2020). Since NEWCLUSTER calculates the evolution of 9 elements, it is utilized to make up the two chemical types of dust: silicate and carbonaceous. The silicate grains are assumed to consist of iron-rich olivine (MgFeSiO$_4$) (Kemper et al. 2004; Min et al. 2007), while the carbonaceous grains are composed solely of carbon. The material densities are set to $s_C = 2.2 \text{ g cm}^{-3}$ for the carbonaceous grains and $s_{Si} = 3.3 \text{ g cm}^{-3}$ for the silicate grains.

### 2.2.1. Dust formation

The elements contributing to the chemical composition of dust are released into the interstellar medium by stellar ejecta. Some of these ejected elements condense into dust, with the fraction determined by the condensation efficiency $\delta_i^k$. Here, the superscript k represents the different sources of stellar yield (k = SNII, SNIa, or AGB) and the subscript i represents the chemical type (carbonaceous or silicate). The values of condensation efficiencies are the same as in Dubois et al. (2024). We also adopt the condensation efficiencies from Dwek (1998) with the exception of $\delta_{Si}^{SNIa}$. As noted by Dwek (1998), however, the choice of condensation efficiencies is somewhat arbitrary. Moreover, these efficiencies remain highly uncertain and may vary by up to an order of magnitude for the same objects (Schneider & Maiolino 2024).

For low-metallicity galaxies at high redshift, dust is primarily produced by stellar ejecta, especially from SNe II. As galaxies evolve and metal enrichment progresses, dust accretion becomes the dominant formation mechanism (Zhukovska 2014; Hou et al. 2019; Li et al. 2019; Choban et al. 2024). The accretion follows:

$$ \dot{M}_{\text{D},i,j} = \left(1 - \frac{M_{\text{D},i,j}}{M_{\text{Z},i}}\right) \frac{M_{\text{D},i,j}}{t_{\text{acc},i,j}}, \quad (1) $$

where $M_{Z,i}$ is the total mass of metals in both gas and dust phases, while $M_{D,i,j}$ represents the dust mass of
grain type i and size j. The accretion timescale follows:

$$ t_{\text{acc},i,j} = C_X \alpha^{-1} a_{0.005,j} s_{3,i} \frac{A_X^{1/2} f_X}{Z_X} \times \left( \frac{n}{1 \text{ H cm}^{-3}} \right)^{-1} \left( \frac{T}{50 \text{ K}} \right)^{-1/2} \text{Myr}, \quad (2) $$

where $\alpha$ is the sticking coefficient, $a_{0.005,j} = a_j/5$ nm is the normalized grain size, $s_{3,i} = s_i/3$ g cm$^{-3}$ is normalized grain density, $A_X$ is the atomic number of the limiting element X, $f_X$ is its mass fraction in the grain chemical composition, and $Z_X$ is its mass fraction in the gas phase. T and n denote the gas temperature and density, respectively. The constant $C_X$ takes values of 0.28, 0.34, 0.23, 0.32, or 0.42 when the limiting element is C, Mg, Fe, Si, or O, respectively. To account for unresolved scales, we employ a subgrid accretion model that assumes the gas follows a log-normal probability distribution function in density, temperature, and metallicity for gas cells with $n > 0.1 \text{ H cm}^{-3}$, $T < 10^4 \text{ K}$ (see Dubois et al. (2024) for the detailed implementation). We do not consider the Coulomb enhancement factor, which results from electrostatic effects, in the calculation of the accretion timescale.

### 2.2.2. Dust destruction

Once the dust has been formed, it can be destroyed by SN shocks, thermal sputtering, and astration. The amount of dust destruction, $\Delta M_{\text{SN},j}$, is determined by $M_{100}/M_g$, the mass fraction of gas shocked at above 100 km/s:

$$ \Delta M_{\text{SN},j} = \epsilon_{\text{SN}}(a_j) \min \left( \frac{M_{100}}{M_g}, 1 \right) M_{\text{D},j}. \quad (3) $$

The grain size-dependent destruction efficiency $\epsilon_{\text{SN},i}(a_j)$ follows Aoyama et al. (2020):

$$ \epsilon_{\text{SN}}(a_j) = 1 - \exp\left(-\frac{\delta_{\text{SN}}}{a_{0.1,j}}\right). \quad (4) $$

Here, the destruction efficiency does not depend on the composition of the grain or the type of SNe. Therefore, the destruction efficiency is uniformly given by $\delta_{\text{SN}} = 0.10$. However, Dubois et al. (2024) use different destruction efficiency for the composition of the grain ($\delta_{\text{SN,C}} = 0.10, \delta_{\text{SN,Si}} = 0.15$).

In the thermal sputtering process, dust grains gain energy exceeding their binding energy through interactions with hot gas, resulting in the transfer of mass from dust to gas-phase metals. Since NEWCLUSTER does not include radiative transfer calculations, non-thermal sputtering processes are not implemented. The destruction

rate of dust by thermal sputtering, $\dot{M}_{\text{spu},j}$, is determined by $t_{\text{spu}}$, sputtering timescale:

$$ \dot{M}_{\text{spu},j} = -M_{\text{D},j}/t_{\text{spu}}, \quad (5) $$

where the sputtering timescale follows Tsai & Mathews (1995): 7

$$ \begin{aligned} t_{\text{spu}} &= \frac{a_{0.1}}{\dot{a}_{0.1}} \\ &= 0.165 a_{0.1} \left( \frac{n}{1 \text{ H cm}^{-3}} \right)^{-1} \left[ \left( \frac{2 \times 10^6 \text{ K}}{T} \right)^{2.5} + 1 \right] \text{Myr}. \end{aligned} \quad (6) $$

Unlike Dubois et al. (2024), we do not consider thermal sputtering yields to depend on chemical grain type.

When a stellar particle forms, we enforce that the DTG ratio in the parent gas cell remains unchanged. Since gas mass is removed during star formation, the corresponding amount of dust mass is also removed and incorporated into the stellar particle. This process effectively represents astration, the destruction of dust due to star formation.

### 2.2.3. Dust size evolution

Depending on the velocity dispersion of dust in cold gas, dust grains can coagulate or shatter through direct collisions. These processes transfer mass between grains of different sizes while conserving the total dust mass. Coagulation primarily occurs where the gas density is high ($n \sim 10^2 - 10^3 \text{ H cm}^{-3}$) and the velocity dispersion is low ($\sigma_{\text{D,S}} \sim 0.1 - 1 \text{ km s}^{-1}$) (Yan et al. 2004). Such high densities present a challenge as they are unresolvable at the resolution of galaxy formation simulations. Therefore, NEWCLUSTER adopts a fraction of dense gas, denoted as F (or “fudge factor”), as proposed by Aoyama et al. (2017). For simplicity, we use $F = 0.5$ in this study. In this scheme, half of the gas mass has a density $n > 10^3 \text{ H cm}^{-3}$ in unresolved gas cells. Dubois et al. (2024) conducted tests with two simulations, one with $F = 1$ and the other where $n$ follows a log-normal PDF shaped by turbulence. They showed 0.2% and 3% fewer small grains, respectively, compared to the $F = 0.5$ case. Therefore, the impact of the choice of F is small.

The timescale for coagulation, $t_{\text{coa}}$, is calculated following Aoyama et al. (2017):

$$ t_{\text{coa}} = 0.27 \frac{a_{0.005} s_3}{F} \left( \frac{D_S}{0.01} \right)^{-1} \left( \frac{\sigma_{\text{D,S}}}{0.1 \text{ km s}^{-1}} \right)^{-1} \text{Myr}, \quad (7) $$

<footnote n="7">7 Since it is more likely to be $M_{\text{spu}}/M_D = a/3\dot{a}$, our thermal sputtering time scale is likely overestimated by a factor of 3 in Equation 5.</footnote>
where $D_S$ is dust-to-gas ratio of small grains.

The shattering process used in NEWCLUSTER is identical to that of Dubois et al. (2024). They adopted the model revised by Granato et al. (2021):

$$t_{\text{sha}} = 54a_{0.1}s_3 \left(\frac{D_L}{0.01}\right)^{-1} \left(\frac{n}{1 \text{ H cm}^{-3}}\right)^{-p_{\text{sh}}} \text{Myr}, \quad (8)$$

where $p_{\text{sh}} = 1$ in the gas with $n < 1 \text{ H cm}^{-3}$ and $p_{\text{sh}} = 1/3$ in the gas with $1 < n < 10^3 \text{ H cm}^{-3}$.

To assess the validity of the adopted dust model, we compare the simulated DTG-metallicity relation with observations. Figure 1 shows the DTG ratio as a function of gas-phase metallicity, measured within $r_{80}$, the radius enclosing 80% of the total galaxy mass. The solid black line represents the broken power-law fit from Rémy-Ruyer et al. (2014), where the slope at high metallicity reflects a constant DTM ratio. The green line represents the linear regression fit from Popping & Péroux (2022), derived from high-redshift galaxies in the range $0 < z < 5$. In NEWCLUSTER, the simulated relation well reproduces a near-linear trend above solar metallicity ($12 + \log(O/H)_\odot \approx 8.69$) where $Z_\odot \approx 0.0134$ is adopted (Asplund et al. 2009). In the low-metallicity regime, the simulated galaxies exhibit significantly lower DTG ratios compared to Rémy-Ruyer et al. (2014); Popping & Péroux (2022), while comparable with De Vis et al. (2017). These trends are broadly consistent with previous results from cosmological simulations (e.g., Popping et al. 2017; Hou et al. 2019; Li et al. 2019; Dubois et al. 2024; Trayford et al. 2025). Overall, this comparison suggests that the adopted dust model captures key aspects of dust evolution and is reasonable for further analysis.

## 3. METHODOLOGY

### 3.1. JWST Observation

To provide a reference for NEWCLUSTER, we utilize observational data that satisfy several essential criteria. First, the dataset should include a large sample of galaxies with redshift estimates. Second, we prefer galaxies observed in a blank field to ensure that their morphologies are less affected by gravitational lensing effects. Lastly, the observation requires coverage in filters that probe the morphology of high-redshift galaxies. We choose the JWST PRIMER-COSMOS East field (GO-1837, PI: Dunlop), which is part of the well-studied COSMOS survey (Scoville et al. 2007) and satisfies the selection criteria described above. Although NEWCLUSTER is a cluster simulation, it is hardly a cluster at $z = 1-3$, which is the range investigated in this study. It is clearly a region where many galaxies

<figure><img src="image_2.png" /><figcaption>Figure 1. The relation between the DTG ratio and gas-phase metallicity for NEWCLUSTER galaxies. The color of each point indicates the redshift. The solid black line represents the broken power-law fit to observational data from Rémy-Ruyer et al. (2014), and the green line represents the linear regression fit from Popping & Péroux (2022). The shaded region denotes the uncertainty of the Rémy-Ruyer et al. (2014) fit. The dashed, dot-dashed, and dotted black lines correspond to constant DTM ratios of 0.15, 0.25, and 0.40, respectively. The gray crosses are the observational measurements from Table A1 and A2 in De Vis et al. (2017).</figcaption></figure>

formed early, which allow for a statistical analysis (despite its small volume) to compare with observational data. For a fairer comparison with NEWCLUSTER, we should ideally compare it to a set of observed cluster galaxies. However, identifying cluster galaxies at high redshifts requires spectroscopic redshifts, which are currently largely unavailable. Besides, we use the observed data only as a guideline, rather than as an aim of the simulation. Consequently, rather than pursuing a direct comparison with the simulation, we employ these galaxies as a morphological reference to interpret trends in LTG fractions.

All the JWST data are provided by the Dawn JWST archive (Valentino et al. 2023) and the galaxy images are reduced using the grizli software (Brammer 2023). This field provides deep JWST imaging, making it an excellent choice for studying high-redshift galaxies. We focus on galaxies within the redshift range $0.83 < z < 3.14$ and with stellar masses greater than $10^{10} M_\odot$. Given the wide redshift range of our sample, it is crucial to account for the dependence of morphology indices on rest-frame
wavelength. Previous studies have shown that morphology indices become nearly independent of wavelength at $\lambda > 1000$ nm (Yao et al. 2023). Within our redshift range, this corresponds to JWST's F277W and F356W filters. Therefore, we measure morphology indices in the F277W filter for lower redshifts ($z = 0.95 - 1.33$) and in the F356W filter for higher redshifts ($z = 1.86 - 3.01$). To ensure reliable measurements, we further restrict our sample to galaxies with $18 < m < 30$, where $m$ denotes the apparent magnitude for a filter of our choice. Additionally, we require a signal-to-noise ratio (SNR) greater than 10 in each filter to select galaxies with more reliable photometric redshift estimates. After applying these selection criteria, our final sample consists of 8,723 galaxies.

## 3.2. NewCluster Mock Observation

To compare with the selected JWST observation and obtain realistic galaxy morphologies, we perform mock observations of NEWCLUSTER galaxies. Our sample consists of galaxies with stellar masses ranging from $10^{10}$ to $10^{12} M_{\odot}$ across five snapshots ($z = 0.95, 1.33, 1.86, 2.44$, and 3.01) of the NEWCLUSTER simulation. The stellar mass of each galaxy is defined as the total mass of member stellar particles identified by an AdaptaHOP galaxy-finder algorithm (Aubert et al. 2004; Tweed et al. 2009). In Figure 2, we present the stellar mass distribution of galaxies in the JWST observation and NEWCLUSTER. Their mass distributions are similar over the mass range of interest. As noted in Section 3.1, because the JWST sample is used as a qualitative reference and not a statistically weighted control sample, we do not marginalize simulation data over stellar mass.

To perform the mock observation, we process the simulation data as follows:

* Generating mock images of the NEWCLUSTER galaxies using the three-dimensional radiative transfer code SKIRT (Baes & Camps 2015; Camps & Baes 2020) in JWST filters.

* Downscaling the simulated galaxy images from a pixel scale of 0.05 kpc/pixel to match the observation pixel scale of 0.04 pixel$^{-1}$.

* Convolving the downscaled images with the mean point spread function modeled from the observation.

* Introducing characteristic JWST noise to the images.

The downscaling process simply involves using photometric galaxy redshifts under the assumption of a flat

<figure><img src="image_3.png" /><figcaption>**Figure 2.** The stellar mass distribution of galaxies in NEWCLUSTER and JWST PRIMER-COSMOS East field. The number in the legend is the number of galaxies in each dataset.</figcaption></figure>

$\Lambda$CDM cosmology to convert from the original physical pixel scale to the necessary angular pixel scale.

### 3.2.1. SKIRT

We generate the mock flux maps (i.e., mock images) of NEWCLUSTER galaxies using the SKIRT radiative transfer code. SKIRT simulates the effect of dust on photons emitted from light sources, such as stellar particles, by accounting for absorption, emission, and scattering. We apply the Starburst99 stellar population model (Leitherer et al. 1999) with a Chabrier IMF (Chabrier 2003) to each stellar particle, to be consistent with NEWCLUSTER's setup.

In SKIRT, the number of photon packets, $N$, determines the statistical accuracy of the Monte Carlo radiative transfer simulation. A convergence test up to $N = 10^9$ confirms that the resulting galaxy morphology shows no significant variation with increasing $N$. Based on this, we adopt $N = 2 \times 10^7$, which provides a good balance between noise suppression and computational efficiency. The pixel scale for the mock images is set to 0.05 kpc/pixel to ensure a detailed resolution. To improve statistical robustness, we generate mock images from 16 different viewing angles, covering four inclination angles and four azimuthal angles.

We employ two different dust models as input for SKIRT to compare their effects on morphology. In the OTF model, we directly use the dust density distribution computed within NEWCLUSTER, as described in Section 2.2. In contrast, the fixed DTM model obtains the dust density distribution by scaling the metallicity dis-
tribution of NEWCLUSTER with a fixed DTM ratio. In this study, we adopt a fixed DTM value of 0.25, which corresponds to the average value measured for galaxies in NEWCLUSTER, as shown in Figure 1. Additionally, we assume a maximum temperature of 30,000 K for the survival of dust species in the fixed DTM model.

Both the OTF and fixed DTM models adopt a two-component grain size population (small and large grains), and the small-to-large dust mass ratio is directly taken from the NEWCLUSTER outputs. This ratio reflects the physical processes that govern grain evolution, such as growth and shattering. We input the small and large grain abundances into the post-processing radiative transfer code, SKIRT. When doing so, we assume a log-normal grain size distribution with fixed minimum and maximum grain sizes of $1.0 \times 10^{-7}$ cm and $1.0 \times 10^{-4}$ cm, respectively, for each grain type and (size) population. The peak sizes of the distributions are set to 5 nm for small grains and 0.1 µm for large grains. For the SKIRT post-process, each distribution is discretized into 7 size bins per grain population. Although increasing the number of size bins could marginally improve accuracy, we find that seven bins are sufficient to capture the relevant galaxy morphology for our study. Finally, both dust models adopt the grain optical properties from Weingartner & Draine (2001).

Using these dust properties, SKIRT performs radiative transfer simulations to compute dust attenuation effects in the mock observations. In Figure 3, we present the color images of two NEWCLUSTER galaxies in JWST filters, generated with SKIRT. The comparison between the OTF and fixed DTM models reveals notable morphological differences. For the left sample galaxy, the OTF model produces a brighter central region compared to the fixed DTM model. For the right sample galaxy, the edge-on image generated with the fixed DTM model appears significantly redder, indicating stronger dust attenuation.

### 3.2.2. Point spread function modeling

A point spread function (PSF) is the response of a detector to a point source of light and represents the distortion introduced by the telescope. To model the PSF, we utilize stars in the observation field for a principal component analysis (PCA). PCA defines basis functions from the data itself rather than relying on predefined analytic functions to reconstruct the PSF. Thus, we derive a PSF that fits the target observation, ensuring that the characteristics of the mock observations closely match those of the target observation. To achieve this, we perform a PCA on stars in the JWST PRIMER-COSMOS

East field following the methodology of Jee et al. (2007) and Finner et al. (2023).

When introducing the PSF model, we convolve the downscaled image of the simulated galaxy with the mean PSF. This PSF is reconstructed using principal components that capture 90% of the total variance, while the remaining components are discarded as they primarily contain noise rather than signal.

### 3.2.3. Background noise

In JWST observations, there are various sources of noise, such as Poisson noise and read noise. Over long exposures, the combination of these noise sources tends to approach a Gaussian distribution. While modeling the background noise with a simple Gaussian function can be sufficient in some cases, it is important to account for a realistic noise profile in morphological studies, as noise can significantly impact morphological measurements, especially for faint galaxies. The background noise distribution in a mosaic image can deviate from following a Gaussian function for various reasons, with one significant cause being the drizzling procedure. Drizzling exposures onto a higher resolution grid leads to unphysical correlation between pixels, and accounting for this correlation is crucial when measuring galaxy morphology. To this end, we adopt the correlated background noise modeling method from Springer et al. (2019), with the detailed implementation described in Appendix A.

The overall mock observation process is summarized in Figure 4. Each row shows the images at successive stages of the process, illustrating how the galaxy images are transformed from top to bottom. The final mock-observed images are presented in Figure 5. In Figure 5, the left four panels show an observed galaxy from the JWST COSMOS East field in four different filters. The middle four panels display the mock-observed images of a galaxy from NEWCLUSTER using the fixed DTM dust model, while the right four panels present the same but using the OTF dust model. The redshift of the observed JWST galaxy is 1.71, and the redshift of the NEWCLUSTER galaxy is 1.86. A comparison between the observed and model images shows that the noise levels and image quality are highly consistent, demonstrating the effectiveness of our mock observation procedure.

### 3.3. Morphology indices

To quantitatively compare the morphologies of mock-observed galaxies from the two different dust models, we use the concentration index (C), asymmetry index (A) (Conselice 2003), the Gini coefficient (G) and the second-order moment of the brightest 20% of each galaxy's flux ($M_{20}$) (Lotz et al. 2004).
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Mock images of two NEWCLUSTER simulation galaxies rendered with two dust models. The face-on and edge-on images are in the JWST filters. Red and blue colors correspond to JWST F200W and F070W filters. For green colors, JWST F090W and F150W filters are used. The first and third panels from the left display mock images generated using the OTF dust model, whereas the second and fourth panels correspond to the fixed DTM dust model.</figcaption></figure>

For each mock galaxy image, we create a segmented image using the photutils photometry package (Bradley et al. 2024). Using these segmented images, we mask other sources and calculate the morphology indices.

The concentration index (C) is defined as

$$C = 5 \log_{10} \left( \frac{r_{80}}{r_{20}} \right), \qquad (9)$$

where $r_{80}$ and $r_{20}$ denote the circular radii enclosing 80% and 20% of the total galaxy flux, respectively. Galaxies with bright central regions and concentrated light distributions, such as ETGs, exhibit higher C values. Among LTGs, those with prominent bulges also show elevated C values.

The asymmetry index (A) is defined as

$$A = \frac{\sum_{i,j} |I_{i,j} - I_{i,j}^{180}|}{\sum_{i,j} |I_{i,j}|}, \qquad (10)$$

where $I_{i,j}$ is the original pixel flux, and $I_{i,j}^{180}$ is the flux at the same position in an image rotated by 180 degrees. The calculation includes only pixels within $r_{70}$ where $r_{70}$ denotes the circular radius enclosing 70% of the total galaxy flux. Higher A values indicate greater asymmetry, typically caused by features such as mergers or spiral arms in LTGs (Conselice et al. 2000).

The Gini coefficient (G) is calculated as

$$G = \frac{1}{|\bar{I}|n(n-1)} \sum_{i=1}^{n} (2i - n - 1)|I_i|, \qquad (11)$$

where $I_i$ is the original pixel flux, and n is the number of pixels. Same with A, the calculation of G is restricted to pixels within $r_{70}$. The Gini coefficient quantifies the inequality in a galaxy’s flux distribution, with G = 0 indicating a perfectly uniform distribution. Similar to C, ETGs with bright centers or bulge-dominated LTGs exhibit higher G values. However, G is more sensitive to merger relic features than C (Lotz et al. 2004).

Finally, the second-order moment of the brightest 20% of the galaxy’s flux ($M_{20}$) is defined as

$$M_{20} = \log_{10} \left( \frac{\sum_i M_i}{M_{\text{tot}}} \right), \quad \text{until} \quad \sum_i I_i < 0.2 I_{\text{tot}}, \qquad (12)$$

where the total second-order central moment $M_{\text{tot}}$ is given by

$$M_{\text{tot}} = \sum_{i=1}^{n} M_i = \sum_{i=1}^{n} I_i [(x_i - x_c)^2 + (y_i - y_c)^2], \qquad (13)$$

where $(x_i, y_i)$ denote the pixel coordinates and $(x_c, y_c)$ denote the coordinates of the galaxy center. To compute $M_{20}$, pixels are sorted by flux, and $M_i$ is summed until the total flux reaches 20% of $I_{\text{tot}}$. $M_{20}$ generally decreases with increasing C. Like G, $M_{20}$ is sensitive to merger relics, as it is not constrained by a circular aperture centered on the galaxy (Lotz et al. 2004).

There are lots of morphology classification schemes that use those morphology indices. However, high-redshift galaxies exhibit diverse morphologies, including merging systems, which makes some morphology classification schemes less effective. To address this, we adopt
<figure><img src="image_5.png" /><figcaption>Figure 4. The overall mock observation process for five NEWCLUSTER galaxies. Each row represents the images at different stages of the mock observation process, illustrating how the images are transformed through each step. From top to bottom, each stage corresponds to SKIRT simulation, rescaling, PSF convolution, and noise addition. The SKIRT images in the first row are visualized in the same manner as those in Figure 3, while the subsequent rows display images in the JWST F277W filter.</figcaption></figure>

the $G-M_{20}$ morphology from Lotz et al. (2004), for analyzing high-z galaxies. Unlike schemes that rely on parametric models, the $G-M_{20}$ morphology is model-independent and robust against variations in resolution, making it particularly suitable for classifying galaxies in low-resolution high-redshift observations.

Given these advantages, we employ the $G-M_{20}$ morphology to classify our sample. $G-M_{20}$ morphology categorizes galaxies based on their positions in the two-dimensional $G-M_{20}$ parameter plane. It distinguishes merging galaxies, early-type galaxies (E, S0, Sa Hubble types), and late-type galaxies (Sb, Sc, Irr Hubble types). Merging galaxies or those exhibiting merger relic features are identified by the criterion:

$$ \text{Merger: } G > -0.14M_{20} + 0.33. \quad (14) $$

Non-merging galaxies, which fall below the merger classification line, are further classified as early-type or late-

type galaxies. Late-type galaxies satisfy the condition:

$$ \text{Late type galaxy: } G < 0.14M_{20} + 0.80. \quad (15) $$

The merger classification line and the morphology classification line are adopted from Lotz et al. (2008). Galaxies classified as early-type by this scheme are bulgedominated, while late-type galaxies tend to have disk-like or irregular morphologies.

# 4. RESULTS

## 4.1. Morphological difference between dust models

Using the mock-observed NEWCLUSTER galaxies, we find that the most notable morphological difference between the OTF and fixed DTM models lies in the central brightness. As shown in Figure 3, galaxies modeled with the OTF model exhibit significantly brighter centers and more prominent bulges compared to those modeled with the fixed DTM model. While the galaxies in Figure 3
<figure><img src="image_6.png" /><figcaption>**Figure 5.** JWST COSMOS East galaxy images and the final mock-observed images from NEWCLUSTER. The left four panels show a galaxy from the JWST COSMOS East field observed with four different filters. The middle four panels display a mock observed NEWCLUSTER galaxy utilizing a fixed DTM dust model. The right four panels show the same mock observed galaxy, instead utilizing the OTF dust model. The magnitudes are shown in the lower right corner of each panel.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 6.** Comparison of the concentration (C) and asymmetry (A) measured in the JWST F277W filter for the same galaxies but with different dust models. The x-axis represents morphological indices derived from the fixed DTM model, while the y-axis represents those from the OTF model. The top panels compare morphological indices across different redshift ranges, with red contours representing high redshift galaxies ($z = 1.86 - 3.01$) and blue contours representing low redshift galaxies ($z = 0.95 - 1.33$). The bottom panels categorize the comparisons based on stellar mass, where red contours indicate high mass galaxies ($\log M_{\star}/M_{\odot} > 10$) and blue contours indicate low mass galaxies ($\log M_{\star}/M_{\odot} < 10$). Each contour level encloses 68%, 90%, 95%, 99% of the corresponding galaxy subset. The black dashed line ($y = x$) serves as a reference, indicating no offset between the two dust models. The gray points represent the entire galaxy sample.</figcaption></figure>

were selected to highlight this effect, similar trends are observed across the sample.

To quantify these differences, Figure 6 presents a comparison of the concentration (C) and asymmetry (A) indices between the two dust models. The x-axis corresponds to the values from the fixed DTM model, while the y-axis represents those from the OTF model. The top panels show variations across redshifts, and the bottom panels categorize galaxies by stellar mass. The black dashed line indicates the one-to-one reference, allowing us to identify systematic differences between the two models.

As shown in Figure 6, the offset in C appears in the high-redshift ($z = 1.86 - 3.01$) and high-mass ($\log M_{\star}/M_{\odot} > 10$) samples, where OTF-modeled galaxies systematically deviate from the one-to-one line. This trend reflects the higher central brightness in the OTF model.

In contrast, A exhibits a symmetric distribution along the equality line. Since the differences between dust models mainly appear in central brightness, they do not affect A.

## 4.2. $G-M_{20}$ distribution

To quantitatively compare the morphology of mock observed NEWCLUSTER galaxies with JWST COSMOS East field galaxies, we adopt the $G-M_{20}$ morphology from Lotz et al. (2004) as introduced in Section 3.3. From this section onward, we use only the galaxies with masses greater than $10^{10} M_{\odot}$, as the morphology classifications based on the $G-M_{20}$ morphology and visual inspection are well aligned with each other above this mass cut.
<figure><img src="image_8.png" /><figcaption>**Figure 7.** The distribution of galaxies in the $G-M_{20}$ plane for JWST COSMOS East field observation and NEWCLUSTER galaxies with different dust models. The x-axis represents $M_{20}$, while the y-axis represents the Gini coefficient ($G$). The black dashed line separates ETGs and LTGs, while the blue dashed line denotes the merger classification threshold, as defined in Section 3.3. The top row corresponds to galaxies in the low-redshift range ($z = 0.95 - 1.33$), and the bottom row represents galaxies in the high-redshift range ($z = 1.86 - 3.01$). The leftmost panels show observational data from JWST, while the middle and rightmost panels show results from the NEWCLUSTER simulation using OTF and fixed DTM models, respectively. The contours indicate the number density of galaxies. All the galaxies have stellar mass higher than $10^{10} M_{\odot}$ in this figure.</figcaption></figure>

Figure 7 presents the distribution of galaxies in the $G-M_{20}$ plane for both observed JWST COSMOS East field galaxies and simulated galaxies from the NEWCLUSTER dataset using different dust models. The black dashed line separates ETGs and LTGs, while the blue dashed line represents the merger classification criterion, as defined in Section 3.3. The figure is divided into two redshift ranges: the top row shows the galaxies at $z = 0.95 - 1.33$, and the bottom row for $z = 1.86 - 3.01$. The number density of galaxies is represented by the contour levels.

To quantify the similarity of two distributions, we measure the Jensen-Shannon distance (JSD), which quantifies the similarity between two probability distributions and ranges from 0 (identical) to 1 (maximally different). In Figure 7, the OTF dust model exhibits a similar distribution of galaxies on the $G-M_{20}$ plane to the fixed DTM model in the low-redshift range ($z = 0.95-1.33$), as reflected in a relatively low JSD of 0.24. However, in the high-redshift range ($z = 1.86-3.01$), the JSD increases to 0.53, highlighting a more pronounced divergence in the $G-M_{20}$ distributions between the two
models. The OTF galaxies are more tightly clustered around the ETG region, whereas the DTM galaxies exhibit more extension towards the LTG region. This result arises from the morphological differences between the dust models, as discussed in Section 4.1. A brighter galaxy center or a more prominent bulge-dominated feature leads to higher *G* values and lower *M*<sub>20</sub> values. Consequently, galaxies utilizing the OTF model are more frequently classified as ETGs. Since morphological differences between the dust models become more significant at high redshifts, their impact on the *G*–*M*<sub>20</sub> distribution is also more pronounced in this regime.

NEWCLUSTER simulates a dense cluster environment, whereas the JWST galaxy sample used for comparison primarily consists of galaxies in relatively low-density, field-like environments. Due to this fundamental difference, the distributions of NEWCLUSTER and JWST galaxies may differ. Nonetheless, when comparing the JWST observation with two different dust models from the NEWCLUSTER simulation, all galaxy samples exhibit a two-peak distribution with similar peak positions at low redshifts, with similar peak positions and similar JSD values of 0.54. At high redshifts, however, the difference between the JWST and NEWCLUSTER distributions becomes more pronounced. Although both dust models yield high JSD values, the OTF model (0.83) shows a slightly lower divergence from the observed distribution than the fixed DTM model (0.85). Meanwhile, both the observed and simulated galaxy samples display broader distributions at high redshift, particularly in regions associated with mergers, compared to their low-redshift counterparts. This trend suggests a higher merger fraction and more diverse morphologies at higher redshifts, consistent with previous studies (Conselice et al. 2008; Bluck et al. 2012; Ventou et al. 2017). Although both JWST and NEWCLUSTER show similar trends, NEWCLUSTER predicts fewer merger galaxies than observed. This discrepancy arises because, in simulations, galaxies are identified in 3D space, allowing clear separation even when galaxies are close in projection. In contrast, observations are limited to 2D projections, so galaxies that are physically distant can appear close along the line of sight, leading to a higher merger fraction (see also Rodriguez-Gomez et al. 2019).

### 4.3. Late-type galaxy fraction

Using the *G*–*M*<sub>20</sub> morphology shown in Figure 7, we define the LTG fraction as the ratio of galaxies classified in the LTG region to the total number of galaxies, excluding those in the merger region.

Figure 8 shows the fraction of late-type galaxies ($f_{\text{LTG}}$) as a function of redshift for galaxies with stellar

<figure><img src="image_9.png" /></figure>

Figure 8. The fraction of late-type galaxies ($f_{\text{LTG}}$) as a function of redshift for galaxies with stellar masses in the range $10 < \log M_{\star}/M_{\odot} < 13$. The black line with error bars represents the LTG fraction for JWST COSMOS East field galaxies, while the blue and red lines show LTG fractions for NEWCLUSTER galaxies using the fixed DTM and OTF dust models, respectively. The green line represents the LTG fraction based on kinematic classifications, with the shaded green region indicating variations due to different $V/\sigma$ criteria (see text). Poisson errors are shown as error bars, and the numbers above the black data points indicate the sample size for JWST COSMOS East field galaxies in each redshift bin. The shaded gray region marks the high-redshift range, where the small sample size of JWST observation increases uncertainty in this work.

masses in the range $10 < \log M_{\star}/M_{\odot} < 13$. The shaded gray region highlights the high-redshift range, where the sample size is significantly smaller, leading to increased uncertainty in the LTG fraction. Additionally, the green line represents kinematic LTG fractions for NEWCLUSTER galaxies, determined based on the rotation-to-dispersion ratio of the stars, $V/\sigma$.

As noted in Section 3.1, NEWCLUSTER simulates a dense cluster environment, whereas the JWST galaxies are located in relatively low-density, field-like environments. Given the well established correlation between galaxy morphology and environment—where denser regions tend to host lower LTG fractions (e.g., Dressler 1980; Sazonova et al. 2020)—a lower LTG fraction in NEWCLUSTER compared to the JWST observations is expected. This is consistent with the LTG fractions predicted by the OTF dust model, as well as with the kinematic LTG fractions presented in Figure 8. To ensure that our comparison is not biased by differences in stellar mass distributions, we first examine the distribu-
tions of the two samples and find an acceptable differ-
ence. However, unlike the OTF model, the fixed DTM
model predicts a significantly higher LTG fraction than
the JWST observation at high redshifts ($z > 1.5$). This
discrepancy arises from the systematically fainter galaxy
centers produced by the fixed DTM model, a trend con-
sistently observed throughout our analysis, which was
also visible in Figures 6 and 7.

Kinematic morphology, like photometric morphology,
provides crucial insights into galaxy formation and evo-
lution. It is based on their internal kinematics, and in
this study, we use the rotation-to-dispersion ratio, $V/\sigma$.
The $V/\sigma$ values are computed in a cylindrical coordinate
system aligned with the galactic rotation axis, where the
mean rotational velocity is given by $V$ and the velocity
dispersion is defined as $\sigma = \sqrt{(\sigma_r^2 + \sigma_t^2 + \sigma_z^2)/3}$. Galax-
ies with $V/\sigma$ values exceeding a certain threshold are
typically classified as kinematical LTGs. Since the clas-
sification of kinematic LTGs is sensitive to the choice
of $V/\sigma$ threshold, we present the kinematic LTG frac-
tion of NEWCLUSTER galaxies using a threshold range of
$V/\sigma = 1 \pm 0.1$ in this figure.

As seen in Figure 8, the kinematic LTG fraction ap-
pears lower than those derived from photometric classifi-
cations based on any dust model. This discrepancy may
partly arise from the characteristics of the G–M<sub>20</sub> mor-
phology used for photometric classification. Although
merger relic galaxies have been excluded, the G–M<sub>20</sub>
morphology can still classify some irregular galaxies as
LTGs, potentially leading to an overestimation of the
photometric LTG fraction compared to the kinematic
classification.

More fundamentally, kinematic and photometric mor-
phologies do not always align perfectly. In simulations,
photometric morphology indicators tend to yield higher
LTG fractions than kinematic indicators (Thob et al.
2019; Jang et al. 2023). Likewise, in observations, not
all high-redshift disk galaxies are true rotational sup-
ported disks. They may exhibit flattened shapes due
to anisotropy in stellar velocity dispersion rather than
coherent rotation (Wang et al. 2024). These discrepan-
cies naturally lead to different evolutionary trends in the
LTG fraction depending on the classification method, as
seen in Figure 8.

5. DISCUSSION: DTM CAVITY FORMATION

The most prominent morphological difference between
the two dust models is that galaxies in the OTF model
exhibit brighter galactic centers, leading to a lower LTG
fraction. These brighter centers arise from a lower DTM
ratio in the central region compared to the outer stel-
lar regions. To quantitatively analyze this central dust

depletion, we define “DTM cavity index”, as the ratio
$DTM_{in}/DTM_{out}$, where $DTM_{in}$ and $DTM_{out}$ are the
mass-weighted average DTM ratios measured inside and
outside the bulge radius, respectively. The DTM ratio is
computed as the ratio of dust mass to total metal mass
in both the gas and dust phases within each gas cell. The
“bulge” radius is determined at redshift $z = 0.95$, as the
location of the inflection point in the surface brightness
profile measured in the SDSS r-band.

Figure 9 illustrates the evolution of the DTM cavity
in a sample galaxy from NEWCLUSTER. In NEWCLUSTER,
dust mass is modeled by four processes (Section 2.2):
stellar ejecta, dust accretion, SN-driven destruction, and
thermal sputtering. Therefore, we investigate the for-
mation of DTM cavities in the context of these four
channels. The two panels on the left show the evolu-
tion of various dust-related quantities. The top panel
presents the DTM cavity index and the star formation
rate (SFR) measured within the bulge region over a
10 Myr timescale. The bottom left panel tracks the evo-
lution of gas-phase metal mass, dust mass, gas mass,
and stellar mass. The nine panels on the right show
the maps in DTM, SFR, and SDSS r-band flux at three
epochs that are denoted in the left panels as vertical
lines.

The top left panel shows that a strong central star-
burst occurs at $z \approx 2 - 1.7$, which coincides with a
drop in DTM cavity index. The DTM maps of the OTF
models in the top-right panels show clearly the reduced
DTM ratio at the center. This means a significantly
lower dust extinction in the central region compared to
the fixed DTM model.

The strong central starburst is likely associated with
bulge formation. During this short period, stellar and
metal masses increase sharply, but dust mass hardly
changes (bottom left panel). This is due to the de-
struction of dust by SN feedback, leading to a transient
dip in the central DTM ratio (top right panels). The
DTM cavity gradually fills in as dust growth becomes
more efficient due to increased metallicity. These results
indicate a feedback-driven mechanism for central dust
depletion during bulge formation, followed by chemical
evolution-driven dust accretion that replenishes the cav-
ity over time.

This phenomenon is common among massive bulge-
hosting galaxies in NEWCLUSTER. Figure 10 shows the
relation between the DTM cavity index and central SFR
divided by gas mass for galaxies with $M_{*} > 10^{10} M_{\odot}$
at redshift $z = 0.95$. Each data point represents a
snapshot of a galaxy during the phase of its most rapid
stellar mass growth within the bulge radius, capturing
an active phase of bulge formation. A bulge with a
<figure><img src="image_10.png" /></figure>

**Figure 9.** Formation and evolution of a central DTM cavity in a representative galaxy from the NEWCLUSTER simulation. Left panels: Evolution of DTM cavity index and bulge properties. The top panel shows the evolution of the DTM cavity index (red; *DTM*<sub>in</sub>/DTM<sub>out</sub>) and the SFR within the bulge region (black). The bottom panel presents the evolution of gas-phase metal mass (solid red), dust mass (dot-dashed red), and gas mass (dashed red), while the stellar mass is plotted in black. Each vertical line indicates the epoch for the right panels. Right panels: Projected maps of the same galaxy at three epochs (z = 2.03, 1.92, and 1.02; columns from left to right). From top to bottom: DTM, SFR, and SDSS r-band surface brightness. The dashed circle denotes the bulge radius. The central DTM cavity emerges shortly after a central starburst (marked by the blue vertical line in the left panels), and persists for several snapshots before being gradually filled in by dust growth.

<figure><img src="image_11.png" /></figure>

**Figure 10.** Relation between DTM cavity index and central SFR divided by gas mass for galaxies of stellar mass $M_* > 10^{10} M_\odot$. The x-axis shows the SFR divided by the gas mass within the bulge, measured over a 10 Myr time window, while the y-axis indicates the DTM cavity index. Each point corresponds to a galaxy snapshot when the stellar mass within the bulge radius is growing most rapidly. The shaded region indicates DTM cavities.

stronger (gas-specific) SFR shows a more pronounced DTM cavity. Observational studies have similarly proposed that starburst-driven feedback can drive central dust depletion (Mattsson & Andersen 2012), and some bulge-dominated S0–Sa galaxies show central depressions in their dust radial profiles (Muñoz-Mateos et al. 2009), lending further support to our scenario.

By construction, fixed-DTM models cannot reproduce DTM cavities. As shown in Figure 9 (bottom left panel), metal mass alone does not lead to dust mass correctly without taking dust destruction into account. For the same reason, even trying different values of *fixed* DTM ratio does not help much, as illustrated in Figure 11. The use of different values of fixed DTM (0.15, 0.25, and 0.40) exhibits similar trends of LTG fractions.

Altogether, these results indicate that bulge-driven starbursts not only enrich the central ISM with metals but also preferentially deplete dust through feedback, leading to the formation of distinct DTM cavities. These DTM cavities gradually disappear due to dust accretion, therefore, they are more frequently expected in the early universe. Considering the complexity of dust evolution, a proper modeling of dust is necessary to determine the morphology of galaxies from their images, especially at high redshifts where star formation rates were high.
<figure><img src="image_12.png" /><figcaption>**Figure 11.** Similar format to Fig. 8. Different lines represent the LTG fraction from NEWCLUSTER using various dust models: Fixed DTM with values of 0.15 (cyan), 0.25 (blue), 0.40 (navy), and the OTF model (red). Poisson errors, calculated based on the number of sample galaxies in each redshift bin, are shown as error bars. This comparison highlights the impact of different dust models on the predicted morphological evolution of galaxies.</figcaption></figure>

## 6. CONCLUSION

This study explores the impact of different dust models on galaxy morphology using the NEWCLUSTER cosmological hydrodynamics simulation, which incorporates an advanced on-the-fly dust model. By comparing the OTF and fixed DTM ratio models, we investigate how dust influences the observed structure of galaxies, particularly at high redshifts. Mock observations were generated using SKIRT radiative transfer simulations, and morphology indices—including concentration (C), asymmetry (A), Gini coefficient (G), and $M_{20}$—were analyzed to classify galaxies and compare them with JWST observations.

The key findings are summarized as follows.

* The fixed DTM model leads to fainter galactic centers and less pronounced bulges compared to those using the OTF dust model, especially in high-mass galaxies at high redshifts.

* Late-type galaxy fractions defined by the $G-M_{20}$ test differ significantly between the two models, with the fixed DTM model predicting higher LTG fractions as a result of producing galaxies with fainter central regions. This discrepancy between dust models becomes more pronounced at $z > 1.5$.

* The enhanced central brightness in OTF galaxies is primarily driven by the presence of DTM cavities, which form due to central starburst and efficient SN-driven dust destruction. The fixed DTM model cannot reproduce these cavities, leading to systematic differences in galaxy concentration between the two dust models.

DTM cavities are expected to be more frequent in the early universe, where bulge formation was more active, but today's galaxies may also show it if they experience unusually enhanced star formation at the center.

Future work will focus on improving dust subgrid models, such as incorporating Coulomb enhancement factors in the dust accretion process. We are conducting a spin-off simulation using revised dust prescriptions; we plan to investigate their impacts on the evolution of dust, especially in the early universe. We also find that DTM cavities are present in this spin-off simulation, suggesting that the DTM cavity observed in NEWCLUSTER is unlikely to be an artifact or a feature specific to the particular dust model employed. In addition, although this paper focuses on the formation of DTM cavities, our forthcoming study will explain why grain growth is not efficient immediately after starbursts and how DTM cavities evolve.

Our results indicate that the OTF model provides a more realistic description of galaxy morphology, showing better agreement with the observed trends in the $G-M_{20}$ plane and LTG fractions. These findings underscore the necessity of on-the-fly dust evolution models for accurately simulating galaxy structure and evolution, particularly in the early Universe where the dust properties were substantially different from the empirical data measured in the local Universe.

## ACKNOWLEDGEMENTS

We thank the referee for the constructive and insightful comments, which significantly helped us improve the clarity and depth of our work. In particular, Appendix B has been added in response to the referee's suggestion. Some of the data products presented herein were retrieved from the Dawn JWST Archive (DJA). DJA is an initiative of the Cosmic Dawn Center (DAWN), which is funded by the Danish National Research Foundation under grant DNRF140. S.K.Y. acknowledges support from the Korean National Research Foundation (RS-2025-00514475; RS-2022-NR070872). J.R. was supported by the KASI-Yonsei Postdoctoral Fellowship and was supported by the Korea Astronomy and Space Science Institute under the R&D program (Project No. 2025-1-831-02), supervised by the Korea AeroSpace Administration. This work was partially supported by the Institut de Physique des deux infinis of Sorbonne Université and by the ANR grant ANR-19-CE31-0017 of
the French Agence Nationale de la Recherche. This
work was granted access to the HPC resources of KISTI
under the allocations KSC-2021-CRE-0486, KSC-2022-
CRE-0088, KSC-2022-CRE-0344, KSC-2022-CRE-0409,

KSC-2023-CRE-0343, KSC-2024-CHA-0009, and KSC-
2025-CRE-0031 and of GENCI under the allocation
A0150414625 and A0180416216. The large data trans-
fer was supported by KREONET, which is managed and
operated by KISTI.

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# APPENDIX

## A. CORRELATED NOISE

Following the methodology from Springer et al. (2019), we extracted $m \approx 3 \times 10^8$ background-subtracted patches, each of size 7×7 pixels, with the background being estimated using source-extractor (Bertin & Arnouts 1996). Using these stamps, an empirical covariance matrix can be constructed as follows:

$$ (\Sigma_{BG})_{j,k} = \left( \frac{1}{m-1} \sum_{i=1}^{m} p_i p_i^{\top} \right)_{j,k}, \quad (A1) $$

where $p_i$ are the vectorized forms of the extracted patches, and each element of $(\Sigma_{BG})_{j,k}$ represents the covariance between the $j^{th}$ and $k^{th}$ pixel in the vectorized patches. The matrix square root $L$ of $\Sigma_{BG}$ satisfies $\Sigma_{BG} = LL^{\top}$, which allows $L$ to transform uncorrelated i.i.d. standard noise variables into correlated variables that share the covariance structure of $\Sigma_{BG}$:

$$ \begin{aligned} \mathrm{Cov}(L\mathbf{z}) &= L\mathrm{Cov}(\mathbf{z})L^{\top} = L I_n L^{\top} \\ &= L L^{\top} = \Sigma_{BG}. \end{aligned} \quad (A2) $$

Here, $\mathbf{z}$ represents a vector of uncorrelated i.i.d. standard noise variables. The central row of $L$ represents the correlation of the central pixel with itself and all other 48 pixels in a characteristic noise patch. This row can be reshaped into a 7 × 7 kernel and convolved with an i.i.d. standard normal noise map, and as a result of this convolution, each pixel in the new noise map becomes the weighted sum of its neighbors in the standard normal noise map. This process effectively introduces the observed correlations and transforms the standard normal noise stamp into a correlated noise stamp that exhibits the covariance properties seen in the observed background noise.

To evaluate whether the noise characteristics are properly introduced, we investigate the resulting power spectra of the correlated noise maps in a manner similar to Scofield et al. (2025). For i.i.d. standard normal noise, a flat power spectrum is expected, showing consistent power across all frequencies. Figure A.1 confirms this expectation, displaying a flat profile for standard normal noise. The power spectrum for the correlated background noise aligns well with the observed background noise spectrum at higher frequencies, demonstrating the ability of this noise modeling method to reproduce small-scale correlations introduced by the drizzling process and detector effects. At lower frequencies (corresponding to larger scales), the divergence is anticipated since the method cannot capture or replicate

<figure><img src="image_13.png" /><figcaption>**Figure A.1.** F277W filter power spectral densities for observed noise (black), standard normal noise (red), and correlated noise (blue). At high frequencies, the correlated and observed background noise power spectra are consistent, demonstrating that our method effectively models the small-scale correlations present in the observed background noise.</figcaption></figure>

correlations on scales larger than the kernel size. Nevertheless, the method effectively reproduces background noise correlations on scales that are most affected by detector properties and drizzling, and those that could significantly influence galaxy morphology. This ensures the reliability of our morphological analysis in the presence of correlated noise.

## B. IMPACT OF MOCK OBSERVATION PROCESS ON $G-M_{20}$ DISTRIBUTION

We perform a test to evaluate the impact of mock observational processes on the $G-M_{20}$ distribution of NEWCLUSTER galaxies. The results are presented in Figure B.1.

For low-redshift galaxies ($0.95 < z < 1.33$), we find that the PSF convolution step has the most significant impact on the morphological distribution in the $G-M_{20}$ plane. Before PSF convolution, the distribution is substantially broader, and the separation between the two peaks—corresponding to ETG and LTG—is more pronounced. The peak position of LTGs remains relatively stable throughout the mock observation steps. In contrast, ETGs exhibit a noticeable shift due to their centrally concentrated light profiles. The PSF convolution redistributes this concentrated flux, leading to a reduction in the Gini coefficient and an increase in $M_{20}$. Con-
<figure><img src="image_14.png" /><figcaption>**Figure B.1.** The Gini–$M_{20}$ distribution of NEWCLUSTER galaxies with the OTF dust model at two different redshift bins: $0.95 < z < 1.33$ (left) and $1.85 < z < 3.01$ (right). Each panel shows the morphological distribution under different mock observation processing steps: raw images (top-left), rescaling (top-right), PSF convolution (bottom-left), and noise addition (bottom-right). The layout and format follow those of Figure 7.</figcaption></figure>

sequently, the ETG population moves toward lower Gini and higher $M_{20}$ values after PSF convolution.

A similar trend is observed for high-redshift galaxies ($1.85 < z < 3.01$): PSF convolution again dominates the changes in the $G–M_{20}$ distribution. However, the effect of noise becomes more prominent compared to the low-redshift case. This is as expected, as high-redshift galaxies typically have lower signal-to-noise ratios, making them more vulnerable to noise-induced uncertainties in morphological measurements.