pith. sign in

arxiv: 2605.20886 · v1 · pith:LP35N4PFnew · submitted 2026-05-20 · 🌌 astro-ph.GA

The TNG50-SKIRT Atlas: Multi-wavelength nonparametric galaxy morphology

Pith reviewed 2026-05-21 03:42 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords galaxy morphologynonparametric indicatorswavelength dependencedust attenuationdisc-dominated galaxiesTNG50 simulationSINGS survey
0
0 comments X

The pith

Nonparametric morphology indicators of galaxies change significantly with wavelength, more so for disc-dominated systems than bulge-dominated ones.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates how standard nonparametric measures of galaxy shape, such as concentration and Gini coefficients, behave when the same galaxies are viewed in ultraviolet through near-infrared light. Using a large set of simulated images, it demonstrates that these measures shift noticeably across wavelengths and that the shifts are larger in galaxies with prominent disks. The trends match what is seen in real local galaxies, while dust effects remain small for the population as a whole even though they matter for some individual objects. This matters because astronomers routinely compare galaxy appearances across different filters and redshifts without always correcting for wavelength-dependent morphology.

Core claim

Nonparametric morphological indicators calculated with StatMorph on the TNG50-SKIRT Atlas images change significantly with wavelength, with the dependence stronger for disc-dominated than for bulge-dominated galaxies; the wavelength trends are consistent with SINGS survey measurements, and dust attenuation produces only modest changes across the full sample though larger effects occur in specific galaxies.

What carries the argument

The TNG50-SKIRT Atlas of synthetic UV-to-NIR images for 1154 mass-selected galaxies at z=0, processed through the StatMorph code to obtain Gini, M20, concentration, and asymmetry parameters.

Load-bearing premise

The TNG50 simulation combined with SKIRT radiative transfer produces sufficiently realistic multi-wavelength images that the measured wavelength trends can be compared directly to observations such as the SINGS survey.

What would settle it

A measurement of the same nonparametric indicators in a complete sample of local galaxies observed in multiple bands that finds no significant wavelength dependence for disc-dominated systems would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.20886 by Abdissa Tassama Emana, Andrea Gebek, Angelos Nersesian, Maarten Baes, Marco Martorano, Sena Bokona Tulu, Tolu Biressa, Vicente Rodriguez-Gomez.

Figure 1
Figure 1. Figure 1: i-band Gini–M20 diagram for the different samples considered in this work. From left to right: Pan-STARRS, our TNG50 galaxy sample, TNG100, and original Illustris simulations. The solid and dashed black lines are taken from Lotz et al. (2008). The solid black line roughly separates isolated galaxies from mergers, whereas the dashed black line divides galaxies into early- and late-types. The dark (inner) an… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of simulated and observational galaxies in different i-band nonparametric morphology indicators: the concentration index as a function of M20 and Gini coefficient. The relation between the concentration index and each indicator is coloured according to the median value of the bulge statistic F of the TNG50 galaxies. Higher concentration generally correlates with more bulge-dominated systems (hig… view at source ↗
Figure 3
Figure 3. Figure 3: Median trends of nonparametric morphology indicators as a function of stellar mass. The black line shows the median trend for the observational Pan-STARRS sample. The blue line represents the TNG50 simulation, while the yellow and red lines correspond to the TNG100 and original Illustris simulations, respectively, in each panel. The shaded areas around each median line in each panel represent the interquar… view at source ↗
Figure 4
Figure 4. Figure 4: Variation of nonparametric morphology indicators as function of wavelength for bulge- and disc-dominated galaxies. Galaxies are classified based on their bulge statistic F, with red lines representing bulge-dominated galaxies (F > 0) and blue lines for disc-dominated ones (F < 0), whereas the black line indicates the median of all galaxy populations. The x-axis represents wavelengths spanning from the FUV … view at source ↗
Figure 5
Figure 5. Figure 5: Wavelength dependence of the distribution in the Gini–M20 plane. This multi-panel figure shows how galaxy morphology, as measured by the Gini coefficient (y-axis) and M20 (x-axis), varies across 12 different bands from UV to NIR. Each panel corresponds to a different filter and is colour-coded according to the concentration index, a measure of how centrally the light is concentrated. A black dashed line in… view at source ↗
Figure 6
Figure 6. Figure 6: Dependence of nonparametric morphology indicators on dust attenuation. The solid and dotted lines show the nonparametric indicators corresponding to dust-attenuated and dust-free images, respectively. The shaded regions represent the 25% to 75% percentile ranges. in the UV–optical regime (Calzetti 2001; Lotz et al. 2008). This trend is consistent with the increase in the effective radius of galaxies as a r… view at source ↗
Figure 7
Figure 7. Figure 7: Examples on which the effect of dust attenuation is significant in some galaxies in the Gini-M20 diagram. The left panel shows Gini-M20 diagram for dust-attenuated g-band, whereas right hand panel illustrates dust-free g-band galaxies. Dust typically lowers concentration, Gini and increases M20 values, shifting galaxies toward disc-dominated classifications. The black dotted division line (Lotz et al. 2008… view at source ↗
Figure 8
Figure 8. Figure 8: Dust-free (top row) and dust-attenuated (bottom-row) g-band images for five TNG50-SKIRT Atlas galaxies, selected to have the largest morphological shifts of Gini and M20 due to dust attenuation. Each images is a cutout with a field-of-view of 30 kpc. However, its effect is not minimal at the individual galaxy image level. Dust attenuation affects disc-dominated (spi￾ral) galaxies significantly more than bu… view at source ↗
read the original abstract

Context: Galaxy morphology is a fundamental property to describe galaxy evolution. However, the observed morphology of a particular galaxy may depend on the observed wavelength. Aims: Our aim is to investigate the wavelength dependence and the effect of dust attenuation on nonparametric morphology indicators. Methods: We use the TNG50-SKIRT Atlas, an atlas of synthetic UV to near-infrared (NIR) broadband images for a complete stellar-mass-selected sample of 1154 galaxies extracted from the TNG50 cosmological simulation at $z = 0$. For each image, we calculate four nonparametric morphology indicators using the StatMorph code. Results: We find that the known correlations between the stellar mass and the morphological parameters measured in the optical, together with the Gini-$M_{20}$, concentration-Gini, and concentration-$M_{20}$ planes, are fully consistent with observational data. However, nonparametric morphological indicators change significantly with wavelength and that this wavelength dependence is stronger for disc-dominated than for bulge-dominated galaxies. The wavelength dependence of the morphology of our simulated TNG50 galaxies is consistent with measurements of local galaxies from the SINGS survey. We demonstrate that the effect of dust attenuation on nonparametric morphology indicators is modest across the full galaxy population but can be significant for individual galaxies.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The manuscript presents the TNG50-SKIRT Atlas of synthetic UV-to-NIR broadband images for a stellar-mass-selected sample of 1154 z=0 galaxies from the TNG50 simulation, post-processed with SKIRT radiative transfer. Nonparametric morphology indicators (Gini, M20, concentration, asymmetry) are measured with StatMorph. The central results are that these indicators vary significantly with wavelength, with stronger dependence in disc-dominated than bulge-dominated systems; the trends match SINGS observations; stellar-mass correlations and morphological planes are consistent with data; and dust attenuation effects are modest on average but can be large for individual galaxies.

Significance. If the simulated images faithfully reproduce the physical drivers of wavelength-dependent morphology, the atlas supplies a controlled benchmark for interpreting multi-wavelength observations and for developing corrections applicable to high-redshift surveys. The direct SINGS comparison and the separation into disc- versus bulge-dominated subsamples add practical value for the field.

major comments (3)
  1. [§4] §4 (Results, SINGS comparison): the statement that wavelength trends are 'consistent with measurements of local galaxies from the SINGS survey' is presented without a quantitative overlap metric (e.g., KS statistic on the distributions of ΔGini or Δasymmetry between UV and NIR, or median shift per morphological type). This leaves open whether the reported stronger disc dependence could arise from TNG50+SKIRT dust geometry rather than being a general physical result.
  2. [§3.2] §3.2 (Dust modeling): the sub-grid dust assumptions and clumpiness prescription in SKIRT are not compared directly to the observed attenuation maps or scale heights in SINGS galaxies. Because the central claim requires that the simulated attenuation produces realistic wavelength trends, especially the disc-versus-bulge difference, a resolution-convergence test or attenuation-map statistic would be needed to rule out simulation-specific artifacts.
  3. [§4.1] §4.1 (Morphological classification): the assignment of galaxies to 'disc-dominated' versus 'bulge-dominated' is not stated to be performed at a fixed wavelength or with a wavelength-independent criterion. Since the paper itself shows that concentration, Gini, and asymmetry all change with wavelength, the classification step must be shown to be robust before the differential wavelength dependence can be interpreted as physical.
minor comments (3)
  1. [Table 1] Table 1: the mass and size cuts used to define the 1154-galaxy sample should be listed explicitly so that the completeness relative to the full TNG50 z=0 volume is clear.
  2. [Figure 7] Figure 7 (dust effect examples): the selection of the 'individual galaxies' shown to have large dust-induced changes is not described; a quantitative threshold (e.g., ΔC > 0.5) would make the claim reproducible.
  3. [Methods] Notation: the definition of the asymmetry parameter in the StatMorph implementation should be restated or referenced to avoid ambiguity with other common asymmetry indices.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough and constructive review, which has identified several areas where the manuscript can be clarified and strengthened. We address each major comment below and indicate the revisions we will make.

read point-by-point responses
  1. Referee: [§4] §4 (Results, SINGS comparison): the statement that wavelength trends are 'consistent with measurements of local galaxies from the SINGS survey' is presented without a quantitative overlap metric (e.g., KS statistic on the distributions of ΔGini or Δasymmetry between UV and NIR, or median shift per morphological type). This leaves open whether the reported stronger disc dependence could arise from TNG50+SKIRT dust geometry rather than being a general physical result.

    Authors: We agree that adding quantitative metrics will make the consistency claim more robust. In the revised manuscript we will include Kolmogorov-Smirnov tests on the distributions of wavelength-induced changes (ΔGini, ΔM20, ΔC, ΔA) between the TNG50-SKIRT sample and the SINGS galaxies, together with median shifts separated by disc- versus bulge-dominated subsamples. These statistics will be presented in a new table or figure panel in §4. revision: yes

  2. Referee: [§3.2] §3.2 (Dust modeling): the sub-grid dust assumptions and clumpiness prescription in SKIRT are not compared directly to the observed attenuation maps or scale heights in SINGS galaxies. Because the central claim requires that the simulated attenuation produces realistic wavelength trends, especially the disc-versus-bulge difference, a resolution-convergence test or attenuation-map statistic would be needed to rule out simulation-specific artifacts.

    Authors: Direct pixel-by-pixel comparison to SINGS attenuation maps is not feasible because such maps are not publicly available for the full SINGS sample in a format matching our radiative-transfer outputs. We will instead add a concise discussion in §3.2 that places our adopted dust-to-metal ratio and clumpiness parameters in the context of observed dust scale heights and attenuation curves from the literature (including SINGS). We will also report a resolution-convergence test performed on a 10 % subsample of galaxies, confirming that the wavelength trends in morphology indicators remain stable when the dust grid is refined. revision: partial

  3. Referee: [§4.1] §4.1 (Morphological classification): the assignment of galaxies to 'disc-dominated' versus 'bulge-dominated' is not stated to be performed at a fixed wavelength or with a wavelength-independent criterion. Since the paper itself shows that concentration, Gini, and asymmetry all change with wavelength, the classification step must be shown to be robust before the differential wavelength dependence can be interpreted as physical.

    Authors: The disc- versus bulge-dominated classification is performed using intrinsic, wavelength-independent quantities from the TNG50 simulation: the stellar-mass ratio within 2 effective radii and the kinematic decomposition (circular velocity versus velocity dispersion). These quantities are derived from the 3D particle data and do not rely on any projected image. We will explicitly state this in §4.1 and add a short robustness check showing that re-classifying a subset of galaxies using optical images yields the same disc/bulge assignment for >95 % of objects. revision: yes

Circularity Check

0 steps flagged

No circularity: direct measurements from TNG50-SKIRT images compared to external observations

full rationale

The paper generates synthetic multi-wavelength images using the TNG50 cosmological simulation and SKIRT radiative transfer for a mass-selected galaxy sample, then applies the external StatMorph code to compute nonparametric morphology indicators (Gini, M20, concentration, asymmetry). These computed values are compared directly to independent observational datasets including SINGS for wavelength trends and other surveys for mass-morphology correlations. No parameters are fitted to the reported trends and then relabeled as predictions, no self-citations supply load-bearing uniqueness theorems or ansatzes, and the central results (wavelength dependence stronger in discs, modest dust effects) emerge as outputs of the forward simulation pipeline rather than being defined into the inputs. The derivation remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on the TNG50 cosmological simulation and SKIRT dust radiative transfer; these contain many sub-grid physics parameters and assumptions about stellar populations and dust geometry that are inherited from prior literature rather than derived here.

axioms (2)
  • domain assumption TNG50 simulation at z=0 produces a representative sample of galaxies whose stellar mass and morphology distributions match the real universe sufficiently for morphology studies.
    Stated in the methods description of the atlas construction.
  • domain assumption StatMorph nonparametric indicators are robust when applied to synthetic images that include dust attenuation.
    Implicit in the choice to use the same code on all wavelengths.

pith-pipeline@v0.9.0 · 5784 in / 1371 out tokens · 23206 ms · 2026-05-21T03:42:42.902996+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

88 extracted references · 88 canonical work pages · 2 internal anchors

  1. [1]

    G., van den Bergh, S., & Nair, P

    Abraham, R. G., van den Bergh, S., & Nair, P. 2003, ApJ, 588, 218

  2. [2]

    & Camps, P

    Baes, M. & Camps, P. 2015, Astronomy and Computing, 12, 33

  3. [3]

    2025, Research Notes of the American Astronomical Society, 9, 328

    Baes, M., Camps, P., Gebek, A., et al. 2025, Research Notes of the American Astronomical Society, 9, 328

  4. [4]

    I., Dejonghe, H., et al

    Baes, M., Davies, J. I., Dejonghe, H., et al. 2003, MNRAS, 343, 1081

  5. [5]

    2020, A&A, 641, A119

    Baes, M., Nersesian, A., Casasola, V ., et al. 2020, A&A, 641, A119

  6. [6]

    2011, ApJS, 196, 22

    Baes, M., Verstappen, J., De Looze, I., et al. 2011, ApJS, 196, 22

  7. [7]

    K., Glazebrook, K., Brinkmann, J., et al

    Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681

  8. [8]

    J., Calzetti, D., Engelbracht, C

    Bendo, G. J., Calzetti, D., Engelbracht, C. W., et al. 2007, MNRAS, 380, 1313

  9. [9]

    A., Pedrosa, S

    Bignone, L. A., Pedrosa, S. E., Trayford, J. W., Tissera, P. B., & Pellizza, L. J. 2020, MNRAS, 491, 3624

  10. [10]

    A., Tissera, P

    Bignone, L. A., Tissera, P. B., Sillero, E., et al. 2017, MNRAS, 465, 1106

  11. [11]

    Blanton, M. R. & Moustakas, J. 2009, ARA&A, 47, 159

  12. [12]

    Bottrell, C., Torrey, P., Simard, L., & Ellison, S. L. 2017, MNRAS, 467, 2879

  13. [13]

    J., Sheth, K., Athanassoula, E., et al

    Buta, R. J., Sheth, K., Athanassoula, E., et al. 2015, ApJS, 217, 32

  14. [14]

    2001, PASP, 113, 1449

    Calzetti, D. 2001, PASP, 113, 1449

  15. [15]

    & Baes, M

    Camps, P. & Baes, M. 2015, Astronomy and Computing, 9, 20

  16. [16]

    & Baes, M

    Camps, P. & Baes, M. 2020, Astronomy and Computing, 31, 100381

  17. [17]

    2013, A&A, 560, A35

    Camps, P., Baes, M., & Saftly, W. 2013, A&A, 560, A35

  18. [18]

    U., & Grand, R

    Camps, P., Behrens, C., Baes, M., Kapoor, A. U., & Grand, R. 2021, ApJ, 916, 39

  19. [19]

    U., Trcka, A., et al

    Camps, P., Kapoor, A. U., Trcka, A., et al. 2022, MNRAS, 512, 2728 Article number, page 12 of 13 Sena Bokona Tulu et al.: TNG50-SKIRT Atlas

  20. [20]

    & Pan-STARRS Team

    Chambers, K. & Pan-STARRS Team. 2018, in American Astronomical Society Meeting Abstracts, V ol. 231, American Astronomical Society Meeting Ab- stracts #231, 102.01

  21. [21]

    Conselice, C. J. 2003, ApJS, 147, 1

  22. [22]

    Conselice, C. J. 2014, ARA&A, 52, 291

  23. [23]

    J., Yang, C., & Bluck, A

    Conselice, C. J., Yang, C., & Bluck, A. F. L. 2009, MNRAS, 394, 1956 Davé, R., Anglés-Alcázar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827

  24. [24]

    Data Release 1 of the Dark Energy Spectroscopic Instrument

    Davis, T. A., Gensior, J., Bureau, M., et al. 2022, MNRAS, 512, 1522 de Vaucouleurs, G. 1959, Handbuch der Physik, 53, 275 DESI Collaboration, Abdul Karim, M., Adame, A. G., et al. 2025, arXiv e-prints, arXiv:2503.14745

  25. [25]

    2018, ApJ, 853, 194

    Dickinson, H., Fortson, L., Lintott, C., et al. 2018, ApJ, 853, 194

  26. [26]

    1980, ApJ, 236, 351

    Dressler, A. 1980, ApJ, 236, 351

  27. [27]

    2025, A&A, 697, A234

    Drigga, E., Koulouridis, E., Pouliasis, E., et al. 2025, A&A, 697, A234

  28. [28]

    P., Andrews, S

    Driver, S. P., Andrews, S. K., da Cunha, E., et al. 2018, MNRAS, 475, 2891

  29. [29]

    P., Robotham, A

    Driver, S. P., Robotham, A. S. G., Tompkins, S., et al. 2026, MNRAS, submitted

  30. [30]

    Exploring galaxy morphology across cosmic time through Sersic fits

    Dubois, Y ., Beckmann, R., Bournaud, F., et al. 2021, A&A, 651, A109 Euclid Collaboration: Kova ˇci´c, I., Baes, M., Nersesian, A., et al. 2025, A&A, 695, A284 Euclid Collaboration: Mellier, Y ., Abdurro’uf, Acevedo Barroso, J. A., et al. 2025, A&A, 697, A1 Euclid Collaboration: Quilley, L., Damjanov, I., de Lapparent, V ., et al. 2026, A&A, in press (arX...

  31. [31]

    E., Izbicki, R., Lee, A

    Freeman, P. E., Izbicki, R., Lee, A. B., et al. 2013, MNRAS, 434, 282

  32. [32]

    Gadotti, D. A. 2009, MNRAS, 393, 1531

  33. [33]

    A., Baes, M., & Falony, S

    Gadotti, D. A., Baes, M., & Falony, S. 2010, MNRAS, 403, 2053

  34. [34]

    2023, MNRAS, 521, 5645

    Gebek, A., Baes, M., Diemer, B., et al. 2023, MNRAS, 521, 5645

  35. [35]

    2022, MNRAS, 514, 607

    Getachew-Woreta, T., Povi´c, M., Masegosa, J., et al. 2022, MNRAS, 514, 607

  36. [36]

    2025, Galaxies, 13, 84 Gómez, P

    Getachew-Woreta, T., Povi´c, M., Perea, J., et al. 2025, Galaxies, 13, 84 Gómez, P. L., Nichol, R. C., Miller, C. J., et al. 2003, ApJ, 584, 210

  37. [37]

    2025, arXiv e-prints, arXiv:2504.18042

    Gong, J.-Y ., Lin, W., Tang, L., & Lan, Y . 2025, arXiv e-prints, arXiv:2504.18042

  38. [38]

    Goulding, A. D. & Alexander, D. M. 2009, MNRAS, 398, 1165

  39. [39]

    Graham, A. W. & Worley, C. C. 2008, MNRAS, 388, 1708 Guzmán-Ortega, A., Rodriguez-Gomez, V ., Snyder, G. F., Chamberlain, K., &

  40. [40]

    2023, MNRAS, 519, 4920

    Hernquist, L. 2023, MNRAS, 519, 4920

  41. [41]

    W., Pirzkal, N., de Blok, W

    Holwerda, B. W., Pirzkal, N., de Blok, W. J. G., et al. 2011, MNRAS, 416, 2401

  42. [42]

    2009, A&A, 497, 743 Ivezi´c, Ž., Kahn, S

    Huertas-Company, M., Tasca, L., Rouan, D., et al. 2009, A&A, 497, 743 Ivezi´c, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111

  43. [43]

    2006, MNRAS, 372, 2

    Jonsson, P. 2006, MNRAS, 372, 2

  44. [44]

    U., Camps, P., Baes, M., et al

    Kapoor, A. U., Camps, P., Baes, M., et al. 2021, MNRAS, 506, 5703

  45. [45]

    M., White, S

    Kauffmann, G., Heckman, T. M., White, S. D. M., et al. 2003, MNRAS, 341, 54

  46. [46]

    S., Driver, S

    Kelvin, L. S., Driver, S. P., Robotham, A. S. G., et al. 2012, MNRAS, 421, 1007

  47. [47]

    C., Armus, L., Bendo, G., et al

    Kennicutt, Jr., R. C., Armus, L., Bendo, G., et al. 2003, PASP, 115, 928

  48. [48]

    2024, A&A, 689, A13

    Lauwers, A., Baes, M., Camps, P., & Vander Meulen, B. 2024, A&A, 689, A13

  49. [49]

    K., Willis, J

    Leste, O. K., Willis, J. P., Canning, R. E. A., & Rennehan, D. 2024, MNRAS, 533, 2927

  50. [50]

    M., Davis, M., Faber, S

    Lotz, J. M., Davis, M., Faber, S. M., et al. 2008, ApJ, 672, 177

  51. [51]

    M., Jonsson, P., Cox, T

    Lotz, J. M., Jonsson, P., Cox, T. J., & Primack, J. R. 2010, MNRAS, 404, 575

  52. [52]

    M., Madau, P., Giavalisco, M., Primack, J., & Ferguson, H

    Lotz, J. M., Madau, P., Giavalisco, M., Primack, J., & Ferguson, H. C. 2006, ApJ, 636, 592

  53. [53]

    M., Primack, J., & Madau, P

    Lotz, J. M., Primack, J., & Madau, P. 2004, AJ, 128, 163

  54. [54]

    C., Roper, W

    Lovell, C. C., Roper, W. J., Vijayan, A. P., et al. 2025, The Open Journal of Astrophysics, 8, 152

  55. [55]

    2018, MNRAS, 480, 5113

    Marinacci, F., V ogelsberger, M., Pakmor, R., et al. 2018, MNRAS, 480, 5113

  56. [56]

    2023, A&A, 678, A175

    Matsumoto, K., Camps, P., Baes, M., et al. 2023, A&A, 678, A175

  57. [57]

    2023, ApJS, 269, 3 Muñoz-Mateos, J

    Moustakas, J., Lang, D., Dey, A., et al. 2023, ApJS, 269, 3 Muñoz-Mateos, J. C., Gil de Paz, A., Zamorano, J., et al. 2009, ApJ, 703, 1569

  58. [58]

    P., Pillepich, A., Springel, V ., et al

    Naiman, J. P., Pillepich, A., Springel, V ., et al. 2018, MNRAS, 477, 1206

  59. [59]

    J., Robitaille, T., et al

    Narayanan, D., Turk, M. J., Robitaille, T., et al. 2021, ApJS, 252, 12

  60. [60]

    V ., Rafelski, M., Teplitz, H

    Nedkova, K. V ., Rafelski, M., Teplitz, H. I., et al. 2024, ApJ, 970, 188

  61. [61]

    2019, MNRAS, 490, 3234

    Nelson, D., Pillepich, A., Springel, V ., et al. 2019, MNRAS, 490, 3234

  62. [62]

    2018, MNRAS, 475, 624

    Nelson, D., Pillepich, A., Springel, V ., et al. 2018, MNRAS, 475, 624

  63. [63]

    2023, A&A, 673, A63

    Nersesian, A., Zibetti, S., D’Eugenio, F., & Baes, M. 2023, A&A, 673, A63

  64. [64]

    2017, A&A, 601, A92

    Peest, C., Camps, P., Stalevski, M., Baes, M., & Siebenmorgen, R. 2017, A&A, 601, A92

  65. [65]

    2018, MNRAS, 475, 648

    Pillepich, A., Nelson, D., Hernquist, L., et al. 2018, MNRAS, 475, 648

  66. [66]

    2019, MNRAS, 490, 3196

    Pillepich, A., Nelson, D., Springel, V ., et al. 2019, MNRAS, 490, 3196

  67. [67]

    & de Lapparent, V

    Quilley, L. & de Lapparent, V . 2023, A&A, 680, A49

  68. [68]

    S., Li, N., et al

    Ren, J., Liu, F. S., Li, N., et al. 2024, ApJ, 969, 4

  69. [69]

    2021, Astronomy and Computing, 37, 100492

    Reza, M. 2021, Astronomy and Computing, 37, 100492

  70. [70]

    F., Lotz, J

    Rodriguez-Gomez, V ., Snyder, G. F., Lotz, J. M., et al. 2019, MNRAS, 483, 4140

  71. [71]

    2026, The Journal of Open Source Software, 11, 9436

    Roper, W., Lovell, C., Vijayan, A., et al. 2026, The Journal of Open Source Software, 11, 9436

  72. [72]

    2014, A&A, 561, A77

    Saftly, W., Baes, M., & Camps, P. 2014, A&A, 561, A77

  73. [73]

    2013, A&A, 554, A10

    Saftly, W., Camps, P., Baes, M., et al. 2013, A&A, 554, A10

  74. [74]

    A., Bower, R

    Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521

  75. [75]

    F., Torrey, P., Lotz, J

    Snyder, G. F., Torrey, P., Lotz, J. M., et al. 2015, MNRAS, 454, 1886

  76. [76]

    Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report

    Spergel, D., Gehrels, N., Baltay, C., et al. 2015, arXiv e-prints, arXiv:1503.03757

  77. [77]

    2018, MNRAS, 475, 676

    Springel, V ., Pakmor, R., Pillepich, A., et al. 2018, MNRAS, 475, 676

  78. [78]

    S., et al

    Sreejith, S., Pereverzyev, Jr., S., Kelvin, L. S., et al. 2018, MNRAS, 474, 5232

  79. [79]

    A., Conselice, C

    Taylor-Mager, V . A., Conselice, C. J., Windhorst, R. A., & Jansen, R. A. 2007, ApJ, 659, 162

  80. [80]

    D., Bluck, A

    Thorp, M. D., Bluck, A. F. L., Ellison, S. L., et al. 2021, MNRAS, 507, 886

Showing first 80 references.