Pith. sign in

REVIEW 5 minor 100 references

The TNG50-SKIRT Atlas: Spatially resolved synthetic galaxies from the ultraviolet to the submillimetre (DR2)

T0 review · 0 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

Pith's one-line read The TNG50-SKIRT Atlas DR2 turns 1,154 simulated galaxies into UV-to-submillimetre observables, adding thermal dust emission and spectrally resolved cubes.

desk verdict Solid, incremental data-release paper with a real but acknowledged soft spot: the new dust-emission products rest on a single constant dust-to-metal ratio, and the paper never quantifies that choice. read the letter →

arxiv 2608.01908 v1 pith:GOE5JIFH submitted 2026-08-03 astro-ph.GA

classification astro-ph.GA
keywords syntheticgalaxyatlasradiativetransferdustattenuationemissionTNG50simulationsspectralenergydistributionsstarformationtracersdatarelease
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper presents the second release of a synthetic galaxy atlas built by post-processing 1,154 galaxies from the TNG50 cosmological simulation with the Monte Carlo radiative transfer code SKIRT. The release extends earlier coverage to the full ultraviolet-to-submillimetre range by including thermal dust emission, and it upgrades the treatment of stellar populations and star-forming regions. It also fixes a dust-allocation error that had given a subset of galaxies unrealistically high dust masses. The atlas delivers spectrally resolved cubes, broadband images, physical property maps, and an integrated catalogue, with Monte Carlo noise characterised per band. The authors validate internal consistency by showing that integrated luminosities correlate tightly with the simulated star formation rates.

What carries the argument

The engine is the SKIRT Monte Carlo radiative transfer code applied to TNG50 galaxies on an adaptive octree grid, using the THEMIS dust model with a uniform dust-to-metal fraction of 0.2. Thermal dust emission is handled by discretising silicate and carbonaceous grain sizes into ten bins per component, capturing both stochastic heating and equilibrium emission. A pivotal change is using star-forming gas cells as the spatial source distribution for young stars, which smooths UV morphology, and a dust allocation scheme that assigns dust from gas mass rather than density, preserving total dust mass and eliminating grid-boundary artefacts.

What would settle it

Compute the ratio of total absorbed stellar luminosity (dust-free minus dust-aware bolometric luminosity) to total emitted infrared luminosity for every atlas galaxy; if this ratio departs from unity by more than the Monte Carlo noise, the dust heating and emission treatment is not self-consistent.

Watch

Extended reading notes

Core claim

The central claim is that the TNG50-SKIRT Atlas DR2 provides a self-consistent library of synthetic observations spanning the entire UV-to-submillimetre spectrum for a stellar-mass-complete sample of 1,154 z=0 galaxies. By coupling the TNG50 simulation with the SKIRT code in full dust-emission mode, the atlas computes how starlight is absorbed, scattered, and re-emitted by dust, producing images and spectra in which attenuation and dust emission arise from the same radiative transfer solution. The main upgrades over the first release are the use of binary-population stellar templates, young-star emission placed on star-forming gas cells rather than individual particles, and a mass-conserving

Load-bearing premise

The load-bearing premise is that every ISM gas cell's dust mass is exactly 20% of its metal mass; if the true dust-to-metal ratio varies with metallicity, environment, or galaxy type, then the synthetic attenuation and dust emission are systematically biased.

Editorial extensions

If this is right

  • Researchers can construct images in arbitrary filters from the spectrally resolved cubes, including bands for upcoming facilities not hard-coded in the release.
  • Matched dust-free and dust-aware images allow the effect of dust attenuation on morphology, sizes, and colours to be isolated across the whole UV-to-submm range.
  • The full spectral coverage enables investigations of dust scaling relations, total infrared luminosity, and star-formation tracers within one physically consistent framework.
  • The integrated catalogue permits direct comparison with unresolved surveys, and the face-on/edge-on orientations support inclination-dependent studies.
  • The mass-conserving dust allocation removes the ~40 anomalous galaxies from DR1, bringing the sample into agreement with observed dust-mass scaling relations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the release fixes the dust-to-metal ratio to 0.2, the atlas could usefully be reprocessed with a metallicity- or environment-dependent ratio to bracket the systematic uncertainty in dust emission predictions.
  • The spectrally resolved cubes paired with instantaneous SFRs make the atlas a ready-made training set for machine-learning estimators of physical properties from multi-wavelength images; that use is implied but not developed in this paper.
  • A pixel-level energy-balance test—comparing locally absorbed stellar light with locally re-emitted infrared radiation—would directly probe the self-consistency of the dust model; this paper validates global correlations but does not report such a closure check.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 5 minor

Summary. This paper presents the second data release (DR2) of the TNG50-SKIRT Atlas: 1154 z=0 galaxies from TNG50 (10^9.8–10^12 Msun) are post-processed with the SKIRT Monte Carlo radiative transfer code to produce synthetic observables. Relative to DR1, DR2 extends the wavelength coverage from the UV to the submm by including thermal dust emission, provides spectrally resolved data cubes (0.09 micron to 2 mm, 344 bins), adds dedicated face-on and edge-on views, updates the stellar population and HII-region templates (BPASS and TODDLERS), uses star-forming gas cells as primary sources, and revises the dust allocation scheme to conserve mass. The released products include broadband images, physical property maps, and a catalogue of integrated properties. Validation is based on quality control of Monte Carlo noise and on SFR–luminosity diagnostic relations, which the authors explicitly frame as internal-consistency checks rather than new calibrations.

Significance. If the data are as described, TSA DR2 is a substantial public resource for comparative studies of dust attenuation and emission, star formation tracers, and multi-wavelength morphology. I particularly credit the explicit and quantitative Monte Carlo uncertainty assessment (§5), the detection and correction of a real dust-allocation artefact from DR1 (§4.2, Fig. 6), the transparent caveats in §6.3, and the scale of the public data release. The SFR–luminosity diagnostics are partly self-referential because the reference SFR and the luminosities derive from the same simulated star-forming cells, but the paper states this limitation and does not oversell the relations as independent calibrations. The fixed dust-to-metal ratio is a genuine modelling assumption, but it is explicitly disclosed in §2.2.2 and §6.3; I treat it as a caveat for users rather than an internal inconsistency of the atlas.

minor comments (5)
  1. [§2.2.2, §6.3] The fixed dust-to-metal fraction f_dust=0.2 is load-bearing for the new dust-emission products, and §6.3 correctly notes that live-dust simulations find a non-constant dust-to-metal ratio. I would like to see either a short sensitivity test (e.g., recomputing integrated dust masses or L_TIR for a subset of galaxies with f_dust varied over a plausible range) or a more prominent statement in the abstract/intro that absolute dust masses and absolute FIR/submm fluxes are not calibrated quantities. As written, the caveat is present but the reader is left without a quantitative sense of how much the atlas predictions would shift under this assumption.
  2. [§5.1, Table 2] The three-method Monte Carlo noise cross-check is performed for a single galaxy (TNG000008) in face-on orientation, while the brightness-dependent R thresholds in Table 2 are derived from 25 galaxies. Face-on views may not be representative of high-inclination lines of sight, where multiple scattering and dust emission geometry differ. Please add one sentence noting whether the thresholds were checked for edge-on orientations, or explicitly caution that Table 2 may not apply to the most inclined sightlines.
  3. [§3.2] The text near Fig. 4 and Table 1 does state that the fitted relations are internal-consistency diagnostics, not new calibrations. I recommend moving or repeating that caveat in the caption of Fig. 4 and Table 1, since these relations will be easy to misinterpret when the catalogue is used independently of the paper.
  4. [Fig. 5] The panel label 'LSST/uD454DR1' appears corrupted (likely a LaTeX issue); it should read 'LSST u DR1' to match the corresponding 'SDSS u DR2' label. Please check all labels in the figure for similar encoding errors.
  5. [§2.2.4] The term 'dust-free simulations' is used for runs that still include attenuation by gas and nebular line emission. This is explained, but the wording may confuse users; consider using 'dust-absent' or adding a parenthetical clarification at first use.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the atlas is a forward-modeling data release, and the SFR–luminosity checks are explicitly internal consistency diagnostics rather than independent predictions.

full rationale

The paper constructs synthetic observables by post-processing TNG50 galaxies with the SKIRT radiative transfer code; no step in this chain defines a predicted quantity in terms of the target observable. The SFR-luminosity relations in Sect. 3.2 are presented as 'a diagnostic of the internal consistency of the atlas products' and explicitly 'not immediately intended as new calibrations,' so they are not fitted inputs renamed as predictions. The uniform dust-to-metal fraction f_dust=0.2 (Sect. 2.2.2) is a model assumption, and Sect. 6.3 candidly states that live-dust simulations indicate the dust-to-metal ratio is not constant; this is a physical modeling limitation, not a circular step. The DR1-to-DR2 dust-allocation fix (Sect. 4.2) is a technical correction validated against an external observed dust-mass relation. Self-citations to Baes et al. (2024a) and the SKIRT methodology describe the prior tools and sample definitions, but they are not invoked as an unverified uniqueness or existence theorem. No equation in the paper reduces to its own input by construction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The atlas rests on the TNG50 simulation, the SKIRT radiative transfer code, and several modeling assumptions that are not independently validated in this paper. The most influential are the constant dust-to-metal ratio and the choice of stellar population and dust models. The paper explicitly acknowledges these as limitations. There are no newly invented physical entities.

free parameters (3)
  • Dust-to-metal fraction f_dust = 0.2
    Uniform dust-to-metal ratio assumed for all ISM gas cells; value taken from Trčka et al. (2022) and not independently constrained here. It directly sets the dust mass and hence all attenuation and emission predictions.
  • Young/old stellar age boundary = 10 Myr
    Stellar particles younger than 10 Myr are assigned to star-forming regions with TODDLERS templates; older particles use BPASS SSPs. The boundary affects the spatial distribution of UV emission and SFR tracers.
  • Star-forming source smoothing scale = V_cell^(1/3)
    Each star-forming gas cell is represented as a smoothed particle with smoothing length equal to the cube root of the Voronoi cell volume. This is an ad hoc choice to avoid clumpy UV morphologies; it changes the spatial distribution of young stellar emission.
assumptions (5)
  • domain assumption TNG50 simulation outputs are a faithful representation of galaxy evolution at z=0.
    The atlas is built on TNG50; any limitations of the simulation (subgrid physics, calibration at TNG100 resolution) propagate to the observables. Acknowledged in Sect. 6.3.
  • domain assumption The THEMIS dust model correctly captures dust extinction and emission properties.
    The paper uses THEMIS for grain composition and size distribution without testing alternatives; systematic dust physics uncertainties are noted as potentially dominant.
  • domain assumption The dust distribution is directly proportional to the ISM metal density with a constant dust-to-metal ratio.
    This is the key assumption that translates gas metal content into dust mass; the paper notes that live-dust simulations find a non-constant ratio.
  • domain assumption BPASS and TODDLERS template libraries are appropriate for the stellar populations and star-forming regions.
    The choice of stellar population synthesis models and HII region templates drives the SEDs; the authors note that different choices yield systematically different observables.
  • standard math Standard radiative transfer physics (absorption, scattering, thermal emission) is correctly implemented in SKIRT and is sufficient to model galaxy spectra.
    The Monte Carlo radiative transfer equations are standard physics; the paper cites verification tests but does not re-derive them.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The TNG50-SKIRT Atlas: Spatially resolved synthetic galaxies from the ultraviolet to the submillimetre (DR2)." pith.science (2026). https://pith.science/paper/GOE5JIFH

@misc{pith2026260801908,
  author       = {Pith},
  title        = {Pith review of: The TNG50-SKIRT Atlas: Spatially resolved synthetic galaxies from the ultraviolet to the submillimetre (DR2)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GOE5JIFH}},
  note         = {Machine review of arXiv:2608.01908}
}
abstract

We present the second data release (DR2) of the TNG50-SKIRT Atlas (TSA), a library of synthetic, spatially resolved galaxy observables. The atlas is constructed by post-processing a stellar-mass-complete ($10^{9.8}~{\text{M}}_\odot < M_\star < 10^{12}~{\text{M}}_\odot$) sample of 1154 $z=0$ galaxies from the TNG50 cosmological hydrodynamical simulation with the Monte Carlo radiative transfer code SKIRT. Compared to the first release, TSA DR2 extends the wavelength coverage from the ultraviolet to the submillimetre, including dust emission, and incorporates updated stellar population models together with an improved treatment of dust-enshrouded star-forming regions. The atlas provides spatially resolved spectral energy distributions, broadband images, and physical property maps for multiple viewing orientations, as well as a catalogue of integrated properties enabling direct comparison with unresolved observations. We validate the data products through extensive quality control, including an assessment of Monte Carlo noise, and demonstrate their internal consistency using diagnostic relations between luminosities and star formation rates. TSA DR2 provides a versatile resource for studies of dust attenuation and emission, star formation tracers, galaxy morphology, and multi-wavelength scaling relations across spatial scales. The atlas and associated data products are publicly released and are intended to support a wide range of observationally oriented studies of galaxy evolution.

Figures

Figures reproduced from arXiv: 2608.01908 by the authors.

Figure 1
Figure 1. Multi-wavelength synthetic images and physical property maps for the galaxy TNG 294866, observed from viewing orientation O2, corresponding to an inclination of 18.9 deg. All images and maps have a field of view of 80 kpc × 80 kpc, and a pixel scale of 100 pc. The first and second rows show a representative selection of synthetic images, covering the UV to submm wavelength range. These bands are chosen to illustrate… view at source ↗
Figure 2
Figure 2. RGB composite images for a representative subset of galaxies from the TSA DR2, constructed from synthetic GALEX FUV (blue), SDSS 𝑔 (green), and UKIDSS 𝐽 (red) images generated with SKIRT. The composites were generated using the algorithm of Lupton et al. (2004), as implemented in the Astropy package (Astropy Collaboration et al. 2013). We adopted multiplicative scaling factors of 15, 3, and 1 for the FUV, 𝑔, and 𝐽 b… view at source ↗
Figure 3
Figure 3. RGB composite images for a representative subset of galaxies from the TSA DR2, constructed from synthetic WISE W4 (blue), PACS 160 𝜇m (green), and SPIRE 350 𝜇m (red) images generated with SKIRT. The composites were generated using the algorithm of Lupton et al. (2004), as implemented in the Astropy package (Astropy Collaboration et al. 2013). We adopted multiplicative scaling factors of 40, 1, and 4 for the W4, 160 … view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Correlation between integrated luminosities in selected broad￾band filters and the SFR for TSA DR2 galaxies. The sample includes all galaxies observed in the four independent random viewing orientations O1–O4. The top row shows the GALEX FUV and NUV bands and the SDSS …
Figure 5
Figure 5. Figure 5: Comparison between TSA DR1 and DR2 for the galaxy TNG 294866, observed from viewing orientation O2. Left: Integrated SEDs measured within three square apertures of side length 5 kpc, 20 kpc, and 80 kpc. DR1 fluxes are available only in a limited set of UV to NIR bands,…
Figure 6
Figure 6. Figure 6: Illustration of dust-allocation artefacts identified in TSA DR1 and resolved in TSA DR2 (see Sect. 4.2). Left: Stellar mass versus specific dust mass for TSA DR1 (dark symbols) and TSA DR2 (light symbols), compared to the average relation for late-type galaxies in the …
Figure 7
Figure 7. Figure 7: Assessment of Monte Carlo noise in synthetic images generated with SKIRT, illustrated for the galaxy TNG000008 observed in the face-on orientation. Results are shown for three representative bands spanning the UV, near-infrared, and far-infrared regimes. For each band,…
Figure 8
Figure 8. Figure 8: Monte Carlo reliability of the TSA DR2 synthetic images as a function of surface brightness. For each photometric band, the distribu￾tion of the SKIRT reliability statistic 𝑅 is evaluated in bins of surface brightness using a representative sample of galaxies and viewi…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

100 extracted references · 5 linked inside Pith

  1. [1]

    2022, ApJ, 935, 98 Astropy Collaboration, Robitaille, T

    Abdurro’uf, Lin, Y.-T., Hirashita, H., et al. 2022, ApJ, 935, 98 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33

  2. [2]

    I., Dejonghe, H., et al

    Baes, M., Davies, J. I., Dejonghe, H., et al. 2003, MNRAS, 343, 1081 Article number, page 14 of 15 M. Baes et al.: TNG50-SKIRT Atlas

  3. [3]

    A., et al

    Baes, M., Fritz, J., Gadotti, D. A., et al. 2010, A&A, 518, L39

  4. [4]

    2011, ApJS, 196, 22 Barrientos Acevedo, D., van der Wel, A., Baes, M., et al

    Baes, M., Verstappen, J., De Looze, I., et al. 2011, ApJS, 196, 22 Barrientos Acevedo, D., van der Wel, A., Baes, M., et al. 2023, MNRAS, 524, 907

  5. [5]

    K., Williams, T

    Belfiore, F., Leroy, A. K., Williams, T. G., et al. 2023, A&A, 678, A129

  6. [6]

    Bell, E. F. 2003, ApJ, 586, 794

  7. [7]

    2018, A&A, 620, A112 Bokona Tulu, S., Baes, M., Nersesian, A., et al

    Bianchi, S., De Vis, P., Viaene, S., et al. 2018, A&A, 620, A112 Bokona Tulu, S., Baes, M., Nersesian, A., et al. 2026, A&A, in press, arXiv:2605.20886

  8. [8]

    2019, A&A, 622, A103

    Boquien, M., Burgarella, D., Roehlly, Y., et al. 2019, A&A, 622, A103

Show all 100 references
  1. [9]

    & Salim, S

    Boquien, M. & Salim, S. 2021, A&A, 653, A149

  2. [10]

    & Hani, M

    Bottrell, C. & Hani, M. H. 2022, MNRAS, 514, 2821

  3. [11]

    M., Popping, G., et al

    Bottrell, C., Yesuf, H. M., Popping, G., et al. 2024, MNRAS, 527, 6506

  4. [12]

    Brown, M. J. I., Moustakas, J., Kennicutt, R. C., et al. 2017, ApJ, 847, 136

  5. [13]

    & Charlot, S

    Bruzual, G. & Charlot, S. 2003, MNRAS, 344, 1000

  6. [14]

    K., Scofield, Z

    Byun, G.-H., Jang, J. K., Scofield, Z. P., et al. 2025, ApJ, 992, 92

  7. [15]

    I., Freeman, K

    Byun, Y. I., Freeman, K. C., & Kylafis, N. D. 1994, ApJ, 432, 114

  8. [16]

    C., Engelbracht, C

    Calzetti, D., Kennicutt, R. C., Engelbracht, C. W., et al. 2007, ApJ, 666, 870

  9. [17]

    2010, ApJ, 714, 1256

    Calzetti, D., Wu, S.-Y., Hong, S., et al. 2010, ApJ, 714, 1256

  10. [18]

    & Baes, M

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

  11. [19]

    & Baes, M

    Camps, P. & Baes, M. 2018, ApJ, 861, 80

  12. [20]

    & Baes, M

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

  13. [21]

    2013, A&A, 560, A35

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

  14. [22]

    U., Trcka, A., et al

    Camps, P., Kapoor, A. U., Trcka, A., et al. 2022, MNRAS, 512, 2728

  15. [23]

    2015, A&A, 580, A87

    Camps, P., Misselt, K., Bianchi, S., et al. 2015, A&A, 580, A87

  16. [24]

    W., Baes, M., et al

    Camps, P., Trayford, J. W., Baes, M., et al. 2016, MNRAS, 462, 1057

  17. [25]

    2018, ApJS, 234, 20

    Camps, P., Trčka, A., Trayford, J., et al. 2018, ApJS, 234, 20

  18. [26]

    2020, A&A, 633, A100

    Casasola, V., Bianchi, S., De Vis, P., et al. 2020, A&A, 633, A100

  19. [27]

    2003, PASP, 115, 763

    Chabrier, G. 2003, PASP, 115, 763

  20. [28]

    E., Jarrett, T

    Cluver, M. E., Jarrett, T. H., Dale, D. A., et al. 2017, ApJ, 850, 68

  21. [29]

    E., Jarrett, T

    Cluver, M. E., Jarrett, T. H., Dale, D. A., et al. 2025, ApJ, 979, 18

  22. [30]

    2012, A&A, 540, A52

    Cortese, L., Ciesla, L., Boselli, A., et al. 2012, A&A, 540, A52

  23. [31]

    Crain, R. A. & van de Voort, F. 2023, ARA&A, 61, 473 da Cunha, E., Charlot, S., & Elbaz, D. 2008, MNRAS, 388, 1595 Davé, R., Anglés-Alcázar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827

  24. [32]

    Davies, L. J. M., Driver, S. P., Robotham, A. S. G., et al. 2016, MNRAS, 461, 458

  25. [33]

    2019, MNRAS, 485, 4817

    Donnari, M., Pillepich, A., Nelson, D., et al. 2019, MNRAS, 485, 4817

  26. [34]

    J., Stanway, E

    Eldridge, J. J., Stanway, E. R., Xiao, L., et al. 2017, PASA, 34, e058

  27. [35]

    T., Baes, M., Nersesian, A., et al

    Emana, A. T., Baes, M., Nersesian, A., et al. 2026, A&A, submitted Euclid Collaboration: Abdurro’uf, Tortora, C., Baes, M., et al. 2025, A&A, 702, A72 Euclid Collaboration: Kovačić, I., Baes, M., Nersesian, A., et al. 2025, A&A, 695, A284 Euclid Collaboration: Nersesian, A., A...

  28. [36]

    R., & Macciò, A

    Faucher, N., Blanton, M. R., & Macciò, A. V. 2023, ApJ, 957, 7

  29. [37]

    2022, A&A, 667, A29

    Figueira, M., Pollo, A., Małek, K., et al. 2022, A&A, 667, A29

  30. [38]

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

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

  31. [39]

    C., Calzetti, D., et al

    Galametz, M., Kennicutt, R. C., Calzetti, D., et al. 2013, MNRAS, 431, 1956

  32. [40]

    2021, A&A, 649, A18

    Galliano, F., Nersesian, A., Bianchi, S., et al. 2021, A&A, 649, A18

  33. [41]

    2026, A&A, submitted, arXiv:2607.14901

    Gebek, A., Baes, M., Andreadis, N., et al. 2026, A&A, submitted, arXiv:2607.14901

  34. [42]

    2023, MNRAS, 521, 5645

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

  35. [43]

    2025, A&A, 695, A90

    Gebek, A., Diemer, B., Martorano, M., et al. 2025, A&A, 695, A90

  36. [44]

    2024, MNRAS, 531, 3839 Gordon,K.D.,Misselt,K.A.,Witt,A.N.,&Clayton,G.C.2001,ApJ,551,269

    Gebek, A., Trčka, A., Baes, M., et al. 2024, MNRAS, 531, 3839 Gordon,K.D.,Misselt,K.A.,Witt,A.N.,&Clayton,G.C.2001,ApJ,551,269

  37. [45]

    A., Sutherland, R

    Groves, B., Dopita, M. A., Sutherland, R. S., et al. 2008, ApJS, 176, 438 Guzmán-Ortega, A., Bruzual, G., Rodriguez-Gomez, V., & Hernquist, L. 2025, MNRAS, 544, 4403 Guzmán-Ortega, A., Rodriguez-Gomez, V., Snyder, G. F., Chamberlain, K., &

  38. [46]

    2023, MNRAS, 519, 4920

    Hernquist, L. 2023, MNRAS, 519, 4920

  39. [47]

    K., Dubois, Y., et al

    Han, S., Yi, S. K., Dubois, Y., et al. 2026, A&A, 705, A169

  40. [48]

    C., Johnson, B

    Hao, C.-N., Kennicutt, R. C., Johnson, B. D., et al. 2011, ApJ, 741, 124

  41. [49]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

  42. [50]

    M., Miller, C

    Hopkins, A. M., Miller, C. J., Nichol, R. C., et al. 2003, ApJ, 599, 971

  43. [51]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90

  44. [52]

    P., Köhler, M., Ysard, N., Bocchio, M., & Verstraete, L

    Jones, A. P., Köhler, M., Ysard, N., Bocchio, M., & Verstraete, L. 2017, A&A, 602, A46

  45. [53]

    U., Baes, M., Smith, A., et al

    Kapoor, A. U., Baes, M., Smith, A., et al. 2026a, A&A, in press, arXiv:2607.09961

  46. [54]

    U., Baes, M., van der Wel, A., et al

    Kapoor, A. U., Baes, M., van der Wel, A., et al. 2023, MNRAS, 526, 3871

  47. [55]

    U., Baes, M., van der Wel, A., et al

    Kapoor, A. U., Baes, M., van der Wel, A., et al. 2024, A&A, 692, A79

  48. [56]

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

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

  49. [57]

    U., Gebek, A., Baes, M., et al

    Kapoor, A. U., Gebek, A., Baes, M., et al. 2026b, A&A, in press, arXiv:2606.15184

  50. [58]

    Kennicutt, R. C. & Evans, N. J. 2012, ARA&A, 50, 531

  51. [59]

    Kennicutt, Jr., R. C. 1998, ARA&A, 36, 189

  52. [60]

    Kennicutt, R. C. J., Hao, C.-N., Calzetti, D., et al. 2009, ApJ, 703, 1672

  53. [61]

    S., Lacey, C

    Lu, S., Frenk, C. S., Lacey, C. G., et al. 2026, MNRAS, submitted, arXiv:2605.02022

  54. [62]

    R., Fekete, G., et al

    Lupton, R., Blanton, M. R., Fekete, G., et al. 2004, PASP, 116, 133

  55. [63]

    2018, MNRAS, 480, 5113

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

  56. [64]

    2010, in Proceedings of the 9th Python in Science Conference, ed

    McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. S. van der Walt & J. Millman, 56–61 Möllenhoff, C., Popescu, C. C., & Tuffs, R. J. 2006, A&A, 456, 941

  57. [65]

    V., Allaert, F., Baes, M., et al

    Mosenkov, A. V., Allaert, F., Baes, M., et al. 2018, A&A, 616, A120 Muñoz-Mateos, J. C., Gil de Paz, A., Zamorano, J., et al. 2009, ApJ, 703, 1569

  58. [66]

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

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

  59. [67]

    2022, MNRAS, 515, 320

    Nanni, L., Thomas, D., Trayford, J., et al. 2022, MNRAS, 515, 320

  60. [68]

    2023, MNRAS, 522, 5479

    Nanni, L., Thomas, D., Trayford, J., et al. 2023, MNRAS, 522, 5479

  61. [69]

    2018, MNRAS, 475, 624

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

  62. [70]

    G., Holden, B

    Patel, S. G., Holden, B. P., Kelson, D. D., et al. 2012, ApJ, 748, L27

  63. [71]

    D., Witt, A

    Pierini, D., Gordon, K. D., Witt, A. N., & Madsen, G. J. 2004, ApJ, 617, 1022

  64. [72]

    2019, MNRAS, 490, 3196 Planck Collaboration, Ade, P

    Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13

  65. [73]

    Popescu, C. C. & Tuffs, R. J. 2002, MNRAS, 335, L41

  66. [74]

    2022, MNRAS, 510, 3321

    Popping, G., Pillepich, A., Calistro Rivera, G., et al. 2022, MNRAS, 510, 3321

  67. [75]

    U., González-Martín, O., Fritz, J., et al

    Reyes-Amador, O. U., González-Martín, O., Fritz, J., et al. 2025, MNRAS, 543, 813 Rodriguez-Gomez,V.,Snyder,G.F.,Lotz,J.M.,etal.2019,MNRAS,483,4140

  68. [76]

    Rycroft, C. H. 2009, Chaos, 19, 041111

  69. [77]

    2014, A&A, 561, A77

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

  70. [78]

    2013, A&A, 554, A10

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

  71. [79]

    M., Charlot, S., et al

    Salim, S., Rich, R. M., Charlot, S., et al. 2007, ApJS, 173, 267 Sarmiento,R.,Huertas-Company,M.,Knapen,J.H.,etal.2023,A&A,673,A23

  72. [80]

    2026, MNRAS, 548, stag375

    Schaye, J., Chaikin, E., Schaller, M., et al. 2026, MNRAS, 548, stag375

  73. [81]

    2020, MNRAS, 497, 4773

    Schulz, S., Popping, G., Pillepich, A., et al. 2020, MNRAS, 497, 4773

  74. [82]

    Smith, M. W. L., Gomez, H. L., Eales, S. A., et al. 2012, ApJ, 748, 123

  75. [83]

    Somerville, R. S. & Davé, R. 2015, ARA&A, 53, 51

  76. [84]

    2010, MNRAS, 401, 791

    Springel, V. 2010, MNRAS, 401, 791

  77. [85]

    2018, MNRAS, 475, 676

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

  78. [86]

    Stalevski, M., Fritz, J., Baes, M., Nakos, T., & Popović, L. Č. 2012, MNRAS, 420, 2756

  79. [87]

    2016, MNRAS, 458, 2288

    Stalevski, M., Ricci, C., Ueda, Y., et al. 2016, MNRAS, 458, 2288

  80. [88]

    Stanway, E. R. & Eldridge, J. J. 2018, MNRAS, 479, 75

  81. [89]

    Steinacker, J., Baes, M., & Gordon, K. D. 2013, ARA&A, 51, 63

  82. [90]

    F., Vogelsberger, M., et al

    Torrey, P., Snyder, G. F., Vogelsberger, M., et al. 2015, MNRAS, 447, 2753

  83. [91]

    2019, MNRAS, 484, 5587

    Torrey, P., Vogelsberger, M., Marinacci, F., et al. 2019, MNRAS, 484, 5587

  84. [92]

    2012, MNRAS, 427, 2224

    Torrey, P., Vogelsberger, M., Sijacki, D., Springel, V., & Hernquist, L. 2012, MNRAS, 427, 2224

  85. [93]

    W., Camps, P., Theuns, T., et al

    Trayford, J. W., Camps, P., Theuns, T., et al. 2017, MNRAS, 470, 771 Trčka, A., Baes, M., Camps, P., et al. 2022, MNRAS, 516, 3728 VanderMeulen,B.,Camps,P.,Stalevski,M.,&Baes,M.2023,A&A,674,A123

  86. [94]

    & Baes, M

    Vauterin, P. & Baes, M. 2026, A&A, in press, arXiv:2607.29426

  87. [95]

    2020, A&A, 637, A24

    Verstocken, S., Nersesian, A., Baes, M., et al. 2020, A&A, 637, A24

  88. [96]

    2016, A&A, 586, A13

    Viaene, S., Baes, M., Bendo, G., et al. 2016, A&A, 586, A13

  89. [97]

    2014, A&A, 567, A71 Vogelsberger,M.,Marinacci,F.,Torrey,P.,&Puchwein,E.2020,NatureReviews Physics, 2, 42

    Viaene, S., Fritz, J., Baes, M., et al. 2014, A&A, 567, A71 Vogelsberger,M.,Marinacci,F.,Torrey,P.,&Puchwein,E.2020,NatureReviews Physics, 2, 42

  90. [98]

    2017, MNRAS, 465, 3291 Witt,A.N.,Thronson,HarleyA.,J.,&Capuano,JohnM.,J.1992,ApJ,393,611 X-5 Monte Carlo Team

    Weinberger, R., Springel, V., Hernquist, L., et al. 2017, MNRAS, 465, 3291 Witt,A.N.,Thronson,HarleyA.,J.,&Capuano,JohnM.,J.1992,ApJ,393,611 X-5 Monte Carlo Team. 2003, MCNP – A General Monte Carlo N-Particle Transport Code, Version 5 (Los Alamos National Laboratory, Los Alamos)

  91. [99]

    2017, ApJ, 835, 70

    Zhou, Z., Zhou, X., Wu, H., et al. 2017, ApJ, 835, 70

  92. [100]

    2008, ApJ, 686, 155 Article number, page 15 of 15

    Zhu, Y.-N., Wu, H., Cao, C., & Li, H.-N. 2008, ApJ, 686, 155 Article number, page 15 of 15

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.