Pith. sign in

REVIEW 4 major objections 4 minor 3 cited by

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

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Pith's one-line read A fixed dust ratio biases how simulated galaxies appear at high redshift

desk verdict Well-controlled OTF-vs-fixed dust comparison shows dust treatment biases morphological classifications at high z, but the physical verdict rests on subgrid recipes we can't audit in the abstract. read the letter →

arxiv 2508.18374 v1 pith:7BDNJ6XZ submitted 2025-08-25 astro-ph.GA

classification astro-ph.GA
keywords dust-to-metalratioon-the-flydustevolutiongalaxymorphologycosmologicalsimulationradiativetransferhigh-redshiftgalaxiesmockJWSTimagesG-M20classification
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

The paper asks whether the common shortcut of giving every gas cell a fixed dust-to-metal ratio distorts how galaxies look when rendered as telescope images. Using the NewCluster cosmological simulation, which tracks dust formation and destruction on the fly, the authors make mock JWST images of the same galaxies under two dust treatments. They find that with on-the-fly dust, galaxy centers are brighter and bulges more pronounced, so fewer galaxies classify as late-type disks, especially at high redshift. The cause is a localized depression in the dust-to-metal ratio, called the DTM cavity, produced by intense bulge starbursts. If correct, fixed dust-ratio models systematically bias morphological comparisons against high-redshift observations.

What carries the argument

The DTM cavity: a localized depression in the dust-to-metal ratio in galaxy centers caused by intense bulge starbursts, which in the on-the-fly dust model reduces central dust attenuation and brightens the bulge relative to fixed-ratio models. The comparison is carried out with radiative transfer code SKIRT to make mock JWST images, followed by G-M20 morphology classification.

What would settle it

Resolved dust-to-metal ratio or attenuation maps of high-redshift star-forming bulges, from ALMA dust continuum or JWST NIRCam/MIRI observations, could settle it: if the centers of starbursting galaxies show no depression in dust-to-metal ratio relative to their disks, the DTM cavity predicted by the on-the-fly model is not present in real galaxies. A simpler internal check is to rerun the mock images with the dust opacity law or destruction efficiency varied and see whether the late-type fraction shift disappears.

Watch

Extended reading notes

Core claim

The central claim is that adopting a fixed dust-to-metal ratio in post-processing changes the apparent morphology of simulated galaxies enough to alter their classification. In NewCluster, the physically evolved dust distribution leaves central regions less dusty relative to metals after bulge starbursts, so stellar light at the center is less attenuated; the same galaxies rendered with a fixed DTM show fainter centers and look more like disks. The G-M20 morphology test then assigns a lower late-type galaxy fraction to the on-the-fly sample, with the difference strongest at high redshift. The paper interprets this as evidence that fixed DTM models fail to capture a key morphological feature.

Load-bearing premise

The on-the-fly dust model's subgrid recipes for dust formation, destruction, and grain opacity accurately represent real dust in starbursting bulges; if these recipes destroy or fail to re-form dust too aggressively, the DTM cavity and the resulting central brightening are artifacts of the model rather than a real feature of galaxies.

Editorial extensions

If this is right

  • Mock JWST images from simulations that assume a fixed dust-to-metal ratio will systematically classify high-redshift galaxies as later types than they are.
  • The late-type fraction measured in simulations depends on dust modeling choices, not only on star formation and feedback physics.
  • Dust evolution needs to be included when rendering simulated galaxies, not only in the hydrodynamic run, to compare morphology with observations.
  • The DTM cavity predicts that bulge regions of high-redshift starbursting galaxies should show measurably lower dust-to-metal ratios than their disks.
  • At higher redshift, where starbursts are more frequent, the morphology bias from fixed dust ratios should grow.

Reading between the lines

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

  • A natural next step is to test the DTM cavity directly with resolved dust or attenuation observations of high-redshift bulges; a clean detection would support the mechanism, while a flat dust-to-metal ratio in starburst centers would challenge it.
  • Re-deriving dust-to-metal ratios from metal maps in older fixed-ratio simulations may recover some of the missing central brightening without rerunning the hydrodynamics.
  • If the effect is general, it could also affect observables that depend on central attenuation, such as half-light radii, color gradients, or SED-derived stellar masses, though the paper does not test these.
  • The bias may be redshift-dependent in a predictable way, allowing observers to use morphology samples to constrain dust evolution timescales.
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

4 major / 4 minor

Summary. The paper asks whether the choice of dust model changes the apparent morphology of high-redshift galaxies in mock JWST images. Using the NewCluster simulation, the authors post-process the same galaxies with an on-the-fly (OTF) dust model and with a fixed dust-to-metal (DTM) ratio, run SKIRT radiative transfer, and apply the G-M20 morphological classification. They report that OTF models produce brighter centers and more pronounced bulges, lowering the late-type galaxy (LTG) fraction especially at high redshift, and attribute this to a 'DTM cavity'—a localized depression in the dust-to-metal ratio in starbursting bulges. They conclude that fixed-DTM models fail to capture key morphological features. The abstract is clear and the differential design is a strength, but the supplied full text is almost entirely garbled and unreadable, including an embedded reference to an unrelated arXiv paper. Consequently, the derivations, figures, tables, and quantitative results could not be independently checked.

Significance. If the result holds, it is significant: it would demonstrate that dust-evolution subgrid physics changes morphological classifications in a way that matters for JWST-based high-z studies, and it would identify a systematic bias in fixed-DTM mock observations. The internal differential comparison (same galaxies, two dust treatments) is well posed, and the use of SKIRT with a JWST comparison sample is appropriate. However, the interpretation that fixed-DTM models 'fail' depends on the OTF dust recipes faithfully representing dust in starbursting bulges. Because the supplied text is unreadable, the calibration and sensitivity of those recipes cannot be evaluated; this is the main barrier to accepting the paper's central claim.

major comments (4)
  1. [Full text (passim)] The supplied full text is largely illegible mojibake; equations, figures, and tables cannot be read. An unrelated identifier, 'arXiv:2508.18373v1 [cond-mat.str-el]', appears embedded mid-manuscript, indicating contamination. This is not a trivial typo: it prevents verification of every quantitative claim in the paper. A clean manuscript must be provided before any scientific assessment can be made.
  2. [Abstract (last sentence)] The statement that 'fixed DTM models fail to capture key morphological features' is stronger than the evidence described. The paper compares one OTF implementation against one fixed-DTM prescription. Without sensitivity tests varying the dust formation/destruction efficiencies and timescales, the failure could be specific to NewCluster's subgrid parameters. The abstract reports no such tests. Please either add robustness tests or soften the claim to state that fixed-DTM models can differ substantially from OTF models.
  3. [Abstract ('DTM cavity')] The DTM cavity is introduced as the physical mechanism, but the abstract provides no quantitative information (depth, radial extent, redshift dependence) and no direct comparison of the simulated cavity to observational constraints on dust-to-metal ratios in bulges. The causal chain 'intense bulge starburst -> localized DTM depression -> central brightening -> lower LTG fraction' is asserted. If the full text contains the demonstration, it must be made legible; if not, this is a gap in the argument.
  4. [Abstract, JWST comparison] The claim of a lower LTG fraction at high redshift relative to JWST requires the comparison sample to be matched in stellar mass, redshift, rest-frame wavelength, PSF, and noise. None of these selection/matching criteria are visible in the abstract, and the full text is unreadable. Please state the sample selection and matching explicitly, as the morphology differences could be affected by sample mismatch.
minor comments (4)
  1. [Abstract] The phrase 'OTF galaxy models exhibit' should be rephrased to 'galaxies in the OTF models exhibit' or similar, for clarity.
  2. [Abstract] Define 'DTM cavity' at its first use, e.g., 'a depression in the dust-to-metal ratio (hereafter the DTM cavity)'.
  3. [Full text] Remove the embedded 'arXiv:2508.18373v1 [cond-mat.str-el]' and any other extraneous or corrupted material.
  4. [Methods (fixed DTM model)] Specify the fixed DTM ratio value and whether it is a constant or metallicity-dependent; the abstract does not indicate which assumption is used.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: OTF-vs-fixed DTM morphology comparison is an internally generated differential prediction checked against external JWST data.

full rationale

The paper's central claim is that on-the-fly (OTF) dust modeling produces brighter centers and lower late-type fractions than fixed DTM models in mock JWST images, and that this is linked to a 'DTM cavity' in the simulated dust field. This is a differential result between two dust prescriptions within one simulation, compared against an external observational benchmark (JWST). The measured morphology indices and G-M20 classifications are post-processing outputs from SKIRT radiative transfer, not quantities fitted to the JWST classifications. The DTM cavity is an interpretation of the simulated dust field produced by the OTF model; while it is a post hoc characterization rather than an independent prediction, that is not circularity in the derivation sense. The abstract does not exhibit any step where a fitted parameter is renamed as a prediction, and no equations or unique citations in the abstract reduce the conclusion to an input assumption. The full text supplied is corrupted beyond reliable reading, so no specific equation-level reduction can be quoted. The remaining concern raised in the skeptical reading—whether the OTF subgrid dust destruction and growth rates are calibrated robustly—is a model-fidelity and validation issue, not a circularity issue. The OTF-vs-fixed comparison is self-contained as a differential statement; its physical interpretation depends on the realism of the dust recipes, but that dependence is not equivalency by construction. Therefore the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The central claim leans on two classes of inputs the reader cannot audit from the abstract: the dust model's subgrid calibration (free parameters) and the fidelity of the mock-observation pipeline (axioms 2 and 3). The DTM cavity is an explanatory construct within the simulation, not a measured entity. None of these are vices by themselves; they are the standard price of a cosmological simulation paper, but they mean the headline result is conditional on choices that are not shown in the abstract.

free parameters (2)
  • Fixed DTM ratio (reference dust model)
    The fixed-DTM comparison run assumes a constant dust-to-metal mass ratio; the chosen value is a modeling input not visible in the abstract.
  • OTF dust model subgrid parameters (formation and destruction efficiencies, timescales)
    The on-the-fly dust model's production, growth, and destruction rates are calibrated inputs inherited from prior NewCluster work; they directly control the depth and extent of the DTM cavity that drives the central brightening.
assumptions (4)
  • domain assumption NewCluster's hydrodynamics, star formation, and feedback recipes produce galaxies whose intrinsic structure is identical across the two image sets, so image differences isolate the dust model.
    The abstract compares OTF and fixed-DTM images of the same simulated galaxies; this premise is what lets the authors attribute morphology differences to dust modeling alone.
  • domain assumption SKIRT mock images with the adopted dust opacities and scattering properties faithfully approximate what JWST observes.
    The JWST comparison only discriminates the two dust models if the mock observation pipeline (PSF, filter, resolution, noise) is faithful; this cannot be checked from the abstract.
  • domain assumption The G-M20 morphological indices classify late- versus early-type galaxies reliably at high redshift in both mock and real imaging.
    The headline output (lower LTG fraction) is a G-M20 classification statement; misclassification in either arm would change the result.
  • domain assumption Bulge starbursts outpace dust production, causing the DTM depression, rather than being offset by rapid dust growth in dense gas.
    The DTM cavity mechanism requires that the dust formation timescale in starbursting bulges exceeds the metal injection timescale; the sign of the effect depends on this microphysical balance.
invented entities (1)
  • DTM cavity
    purpose: Explains why OTF images show brighter centers: a localized depression of the dust-to-metal ratio in bulges lowers extinction so the stellar center shines through.
    The cavity is inferred from the OTF simulation's own dust field; the paper's JWST morphology comparison is consistent with its observable consequence but does not directly measure a DTM depression, which would require attenuation or dust-emission observations. No clean external handle is provided in the abstract.

how reviews work

0 comments
Cite this review

Pith. "Pith review of How Dust Models Shape High-z Galaxy Morphology: Insights from the NewCluster Simulation." pith.science (2026). https://pith.science/paper/7BDNJ6XZ

@misc{pith2026250818374,
  author       = {Pith},
  title        = {Pith review of: How Dust Models Shape High-z Galaxy Morphology: Insights from the NewCluster Simulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7BDNJ6XZ}},
  note         = {Machine review of arXiv:2508.18374}
}
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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The COLIBRE-SKIRT pipeline: Calibration-free dust radiative transfer postprocessing for cosmological simulations

    astro-ph.GA 2026-07 conditional novelty 7.0 of 10

    The COLIBRE-SKIRT pipeline reproduces the observed low-redshift cosmic SED without calibrating the post-processing, using live dust from the simulation and a new 'split & scale' grain-size mapping.

  2. On the Origin of Intracluster Light based on the High-resolution Simulation, NewCluster

    astro-ph.GA 2025-12 conditional novelty 6.0 of 10

    In a high-resolution cluster simulation, most intracluster-light stars come from satellite galaxies, and stars stripped before cluster infall are old, metal-poor, alpha-enhanced and trace dark matter.

  3. DualSparse-MoE: Coordinating Tensor/Neuron-Level Sparsity with Expert Partition and Reconstruction

    cs.LG 2025-08 conditional novelty 6.0 of 10

    DualSparse-MoE cuts about 25% of MoE computation after training using expert partition and static neuron reconstruction, with 0.08-0.28% average accuracy loss and up to 1.41x module speedup.

Reference graph

Works this paper leans on

98 extracted references · 5 canonical work pages · cited by 3 Pith papers

  1. [1]

    Ed98bʕV[f -ZTsյŊ SbŬʃ QZ5h

    thebibliography [1] 20pt to REFERENCES 6pt =0pt \@twocolumntrue 12pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key o...

  2. [3]

    2025, arXiv e-prints, arXiv:2501.10508, 10.48550/arXiv.2501.10508

    Algera , H., Rowland , L., Stefanon , M., et al. 2025, arXiv e-prints, arXiv:2501.10508, 10.48550/arXiv.2501.10508

  3. [4]

    2020, , 491, 3844, 10.1093/mnras/stz3253

    Aoyama , S., Hirashita , H., & Nagamine , K. 2020, , 491, 3844, 10.1093/mnras/stz3253

  4. [5]

    2017, , 466, 105, 10.1093/mnras/stw3061

    Aoyama , S., Hou , K.-C., Shimizu , I., et al. 2017, , 466, 105, 10.1093/mnras/stw3061

  5. [6]

    J., & Scott , P

    Asplund , M., Grevesse , N., Sauval , A. J., & Scott , P. 2009, , 47, 481, 10.1146/annurev.astro.46.060407.145222

  6. [7]

    2004, , 352, 376, 10.1111/j.1365-2966.2004.07883.x

    Aubert , D., Pichon , C., & Colombi , S. 2004, , 352, 376, 10.1111/j.1365-2966.2004.07883.x

  7. [8]

    2015, Astronomy and Computing, 12, 33, 10.1016/j.ascom.2015.05.006

    Baes , M., & Camps , P. 2015, Astronomy and Computing, 12, 33, 10.1016/j.ascom.2015.05.006

  8. [9]

    2013, , 432, 2298, 10.1093/mnras/stt589

    Bekki , K. 2013, , 432, 2298, 10.1093/mnras/stt589

Show all 98 references
  1. [10]

    1996, , 117, 393, 10.1051/aas:1996164

    Bertin , E., & Arnouts , S. 1996, , 117, 393, 10.1051/aas:1996164

  2. [11]

    Bluck , A. F. L., Conselice , C. J., Buitrago , F., et al. 2012, , 747, 34, 10.1088/0004-637X/747/1/34

  3. [12]

    2024, astropy/photutils: 1.13.0, 1.13.0, Zenodo, 10.5281/zenodo.12585239

    Bradley, L., Sip o cz, B., Robitaille, T., et al. 2024, astropy/photutils: 1.13.0, 1.13.0, Zenodo, 10.5281/zenodo.12585239

  4. [13]

    2023, grizli, 1.9.11, Zenodo, 10.5281/zenodo.8370018

    Brammer, G. 2023, grizli, 1.9.11, Zenodo, 10.5281/zenodo.8370018

  5. [14]

    2020, Astronomy and Computing, 31, 100381, 10.1016/j.ascom.2020.100381

    Camps , P., & Baes , M. 2020, Astronomy and Computing, 31, 100381, 10.1016/j.ascom.2020.100381

  6. [15]

    W., Baes , M., et al

    Camps , P., Trayford , J. W., Baes , M., et al. 2016, , 462, 1057, 10.1093/mnras/stw1735

  7. [16]

    2003, , 115, 763, 10.1086/376392

    Chabrier , G. 2003, , 115, 763, 10.1086/376392

  8. [17]

    R., Kere s , D., Sandstrom , K

    Choban , C. R., Kere s , D., Sandstrom , K. M., et al. 2024, , 529, 2356, 10.1093/mnras/stae716

  9. [18]

    Conselice , C. J. 2003, , 147, 1, 10.1086/375001

  10. [19]

    J., Bershady , M

    Conselice , C. J., Bershady , M. A., & Jangren , A. 2000, , 529, 886, 10.1086/308300

  11. [20]

    J., Rajgor , S., & Myers , R

    Conselice , C. J., Rajgor , S., & Myers , R. 2008, , 386, 909, 10.1111/j.1365-2966.2008.13069.x

  12. [21]

    S., & White , S

    Davis , M., Efstathiou , G., Frenk , C. S., & White , S. D. M. 1985, , 292, 371, 10.1086/163168

  13. [22]

    1959, Handbuch der Physik, 53, 275, 10.1007/978-3-642-45932-0_7

    de Vaucouleurs , G. 1959, Handbuch der Physik, 53, 275, 10.1007/978-3-642-45932-0_7

  14. [23]

    L., Schofield , S

    De Vis , P., Gomez , H. L., Schofield , S. P., et al. 2017, , 471, 1743, 10.1093/mnras/stx981

  15. [24]

    2019, , 623, A5, 10.1051/0004-6361/201834444

    De Vis , P., Jones , A., Viaene , S., et al. 2019, , 623, A5, 10.1051/0004-6361/201834444

  16. [25]

    1980, , 236, 351, 10.1086/157753

    Dressler , A. 1980, , 236, 351, 10.1086/157753

  17. [26]

    R., & Gunn , J

    Dressler , A., Oemler , Jr., A., Butcher , H. R., & Gunn , J. E. 1994, , 430, 107, 10.1086/174386

  18. [28]

    2024, , 687, A240, 10.1051/0004-6361/202449784

    Dubois , Y., Rodr \' guez Montero , F., Guerra , C., et al. 2024, , 687, A240, 10.1051/0004-6361/202449784

  19. [29]

    1998, , 501, 643, 10.1086/305829

    Dwek , E. 1998, , 501, 643, 10.1086/305829

  20. [30]

    M., & Efstathiou , G

    Fall , S. M., & Efstathiou , G. 1980, , 193, 189, 10.1093/mnras/193.2.189

  21. [31]

    Federrath , C., & Klessen , R. S. 2012, , 761, 156, 10.1088/0004-637X/761/2/156

  22. [32]

    J., et al

    Ferreira , L., Adams , N., Conselice , C. J., et al. 2022, , 938, L2, 10.3847/2041-8213/ac947c

  23. [33]

    Finner , K., Faisst , A., Chary , R.-R., & Jee , M. J. 2023, , 953, 102, 10.3847/1538-4357/ace1e6

  24. [34]

    L., Ragone-Figueroa , C., Taverna , A., et al

    Granato , G. L., Ragone-Figueroa , C., Taverna , A., et al. 2021, , 503, 511, 10.1093/mnras/stab362

  25. [35]

    1996, , 461, 20, 10.1086/177035

    Haardt , F., & Madau , P. 1996, , 461, 20, 10.1086/177035

  26. [36]

    2025 a , , 978, 96, 10.3847/1538-4357/ad98f4

    Han , S., Dubois , Y., Lee , J., et al. 2025 a , , 978, 96, 10.3847/1538-4357/ad98f4

  27. [37]

    K., Dubois , Y., et al

    Han , S., Yi , S. K., Dubois , Y., et al. 2025 b , arXiv e-prints, arXiv:2507.06301. 2507.06301

  28. [38]

    C., Lanz , L., Ashby , M

    Hayward , C. C., Lanz , L., Ashby , M. L. N., et al. 2014, , 445, 1598, 10.1093/mnras/stu1843

  29. [39]

    2015, , 447, 2937, 10.1093/mnras/stu2617

    Hirashita , H. 2015, , 447, 2937, 10.1093/mnras/stu2617

  30. [40]

    2011, , 416, 1340, 10.1111/j.1365-2966.2011.19131.x

    Hirashita , H., & Kuo , T.-M. 2011, , 416, 1340, 10.1111/j.1365-2966.2011.19131.x

  31. [41]

    2019, , 485, 1727, 10.1093/mnras/stz121

    Hou , K.-C., Aoyama , S., Hirashita , H., Nagamine , K., & Shimizu , I. 2019, , 485, 1727, 10.1093/mnras/stz121

  32. [42]

    Hubble , E. P. 1926, , 64, 321, 10.1086/143018

  33. [43]

    Inoue , A. K. 2003, , 55, 901, 10.1093/pasj/55.5.901

  34. [44]

    1999, , 125, 439, 10.1086/313278

    Iwamoto , K., Brachwitz , F., Nomoto , K., et al. 1999, , 125, 439, 10.1086/313278

  35. [45]

    K., Yi , S

    Jang , J. K., Yi , S. K., Dubois , Y., et al. 2023, , 950, 4, 10.3847/1538-4357/accd68

  36. [46]

    J., Blakeslee , J

    Jee , M. J., Blakeslee , J. P., Sirianni , M., et al. 2007, , 119, 1403, 10.1086/524849

  37. [47]

    A., Weiner , B

    Kassin , S. A., Weiner , B. J., Faber , S. M., et al. 2012, , 758, 106, 10.1088/0004-637X/758/2/106

  38. [48]

    Kauffmann , G., White , S. D. M., & Guiderdoni , B. 1993, , 264, 201, 10.1093/mnras/264.1.201

  39. [49]

    J., & Tielens , A

    Kemper , F., Vriend , W. J., & Tielens , A. G. G. M. 2004, , 609, 826, 10.1086/421339

  40. [50]

    2014, , 788, 121, 10.1088/0004-637X/788/2/121

    Kimm , T., & Cen , R. 2014, , 788, 121, 10.1088/0004-637X/788/2/121

  41. [51]

    2006, , 653, 1145, 10.1086/508914

    Kobayashi , C., Umeda , H., Nomoto , K., Tominaga , N., & Ohkubo , T. 2006, , 653, 1145, 10.1086/508914

  42. [52]

    M., Dunkley , J., et al

    Komatsu , E., Smith , K. M., Dunkley , J., et al. 2011, , 192, 18, 10.1088/0067-0049/192/2/18

  43. [53]

    2024, , 968, L15, 10.3847/2041-8213/ad43eb

    Kuhn , V., Guo , Y., Martin , A., et al. 2024, , 968, L15, 10.3847/2041-8213/ad43eb

  44. [54]

    G., Baugh , C

    Lacey , C. G., Baugh , C. M., Frenk , C. S., et al. 2016, , 462, 3854, 10.1093/mnras/stw1888

  45. [55]

    H., Park , C., Hwang , H

    Lee , J. H., Park , C., Hwang , H. S., & Kwon , M. 2024, , 966, 113, 10.3847/1538-4357/ad3448

  46. [56]

    D., et al

    Leitherer , C., Schaerer , D., Goldader , J. D., et al. 1999, , 123, 3, 10.1086/313233

  47. [57]

    K., Willis , J

    Leste , O. K., Willis , J. P., Canning , R. E. A., & Rennehan , D. 2024, , 533, 2927, 10.1093/mnras/stae1967

  48. [58]

    2019, , 490, 1425, 10.1093/mnras/stz2684

    Li , Q., Narayanan , D., & Dav \'e , R. 2019, , 490, 1425, 10.1093/mnras/stz2684

  49. [59]

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

    Lotz , J. M., Primack , J., & Madau , P. 2004, , 128, 163, 10.1086/421849

  50. [60]

    M., Davis , M., Faber , S

    Lotz , J. M., Davis , M., Faber , S. M., et al. 2008, , 672, 177, 10.1086/523659

  51. [61]

    2000, , 361, 159, 10.48550/arXiv.astro-ph/0006405

    Maeder , A., & Meynet , G. 2000, , 361, 159, 10.48550/arXiv.astro-ph/0006405

  52. [62]

    Martin , G., Kaviraj , S., Devriendt , J. E. G., Dubois , Y., & Pichon , C. 2018, , 480, 2266, 10.1093/mnras/sty1936

  53. [64]

    R., et al

    McCluskey , F., Wetzel , A., Loebman , S. R., et al. 2024, , 527, 6926, 10.1093/mnras/stad3547

  54. [65]

    C., & Marinacci , F

    McKinnon , R., Torrey , P., Vogelsberger , M., Hayward , C. C., & Marinacci , F. 2017, , 468, 1505, 10.1093/mnras/stx467

  55. [66]

    Min , M., Waters , L. B. F. M., de Koter , A., et al. 2007, , 462, 667, 10.1051/0004-6361:20065436

  56. [67]

    J., Mao , S., & White , S

    Mo , H. J., Mao , S., & White , S. D. M. 1998, , 295, 319, 10.1046/j.1365-8711.1998.01227.x

  57. [68]

    1996, , 379, 613, 10.1038/379613a0

    Moore , B., Katz , N., Lake , G., Dressler , A., & Oemler , A. 1996, , 379, 613, 10.1038/379613a0

  58. [69]

    C., Gil de Paz , A., Boissier , S., et al

    Mu \ n oz-Mateos , J. C., Gil de Paz , A., Boissier , S., et al. 2009, , 701, 1965, 10.1088/0004-637X/701/2/1965

  59. [70]

    C., et al

    Narayanan , D., Dey , A., Hayward , C. C., et al. 2010, , 407, 1701, 10.1111/j.1365-2966.2010.16997.x

  60. [71]

    Negroponte , J., & White , S. D. M. 1983, , 205, 1009, 10.1093/mnras/205.4.1009

  61. [72]

    2022, , 513, 1531, 10.1093/mnras/stac695

    Popping , G., & P \'e roux , C. 2022, , 513, 1531, 10.1093/mnras/stac695

  62. [73]

    S., & Galametz , M

    Popping , G., Somerville , R. S., & Galametz , M. 2017, , 471, 3152, 10.1093/mnras/stx1545

  63. [74]

    C., Galliano , F., et al

    R \'e my-Ruyer , A., Madden , S. C., Galliano , F., et al. 2014, , 563, A31, 10.1051/0004-6361/201322803

  64. [75]

    F., Lotz , J

    Rodriguez-Gomez , V., Snyder , G. F., Lotz , J. M., et al. 2019, , 483, 4140, 10.1093/mnras/sty3345

  65. [77]

    1961, The Hubble Atlas of Galaxies

    Sandage , A. 1961, The Hubble Atlas of Galaxies

  66. [78]

    2020, , 899, 85, 10.3847/1538-4357/aba42f

    Sazonova , E., Alatalo , K., Lotz , J., et al. 2020, , 899, 85, 10.3847/1538-4357/aba42f

  67. [79]

    1992, , 96, 269

    Schaller , G., Schaerer , D., Meynet , G., & Maeder , A. 1992, , 96, 269

  68. [80]

    2024, , 32, 2, 10.1007/s00159-024-00151-2

    Schneider , R., & Maiolino , R. 2024, , 32, 2, 10.1007/s00159-024-00151-2

  69. [81]

    P., Jee , M

    Scofield , Z. P., Jee , M. J., Cha , S., & Park , H. 2025, arXiv e-prints, arXiv:2504.08879, 10.48550/arXiv.2504.08879

  70. [82]

    2007, , 172, 1, 10.1086/516585

    Scoville , N., Aussel , H., Brusa , M., et al. 2007, , 172, 1, 10.1086/516585

  71. [83]

    S., & Dav \'e , R

    Somerville , R. S., & Dav \'e , R. 2015, , 53, 51, 10.1146/annurev-astro-082812-140951

  72. [84]

    S., Gilmore , R

    Somerville , R. S., Gilmore , R. C., Primack , J. R., & Dom \' nguez , A. 2012, , 423, 1992, 10.1111/j.1365-2966.2012.20490.x

  73. [85]

    2019, Monthly Notices of the Royal Astronomical Society, 491, 10.1093/mnras/stz2991

    Springer, O., Ofek, E., Weiss, Y., & Merten, J. 2019, Monthly Notices of the Royal Astronomical Society, 491, 10.1093/mnras/stz2991

  74. [86]

    S., & Dopita , M

    Sutherland , R. S., & Dopita , M. A. 1993, , 88, 253, 10.1086/191823

  75. [87]

    2002, , 385, 337, 10.1051/0004-6361:20011817

    Teyssier , R. 2002, , 385, 337, 10.1051/0004-6361:20011817

  76. [88]

    Thob , A. C. R., Crain , R. A., McCarthy , I. G., et al. 2019, , 485, 972, 10.1093/mnras/stz448

  77. [89]

    W., Schaye , J., Correa , C., et al

    Trayford , J. W., Schaye , J., Correa , C., et al. 2025, arXiv e-prints, arXiv:2505.13056, 10.48550/arXiv.2505.13056

  78. [90]

    C., & Mathews , W

    Tsai , J. C., & Mathews , W. G. 1995, , 448, 84, 10.1086/175943

  79. [91]

    2009, , 506, 647, 10.1051/0004-6361/200911787

    Tweed , D., Devriendt , J., Blaizot , J., Colombi , S., & Slyz , A. 2009, , 506, 647, 10.1051/0004-6361/200911787

  80. [92]

    Valentino , F., Brammer , G., Gould , K. M. L., et al. 2023, , 947, 20, 10.3847/1538-4357/acbefa

  81. [93]

    1960, , 131, 558, 10.1086/146869

    van den Bergh , S. 1960, , 131, 558, 10.1086/146869

  82. [94]

    C., Abel , T., Croft , R

    van den Bosch , F. C., Abel , T., Croft , R. A. C., Hernquist , L., & White , S. D. M. 2002, , 576, 21, 10.1086/341619

  83. [95]

    2017, , 608, A9, 10.1051/0004-6361/201731586

    Ventou , E., Contini , T., Bouch \'e , N., et al. 2017, , 608, A9, 10.1051/0004-6361/201731586

  84. [96]

    2024, , 973, L29, 10.3847/2041-8213/ad772d

    Wang , B., Peng , Y., Cappellari , M., Gao , H., & Mo , H. 2024, , 973, L29, 10.3847/2041-8213/ad772d

  85. [97]

    C., & Draine , B

    Weingartner , J. C., & Draine , B. T. 2001, , 548, 296, 10.1086/318651

  86. [98]

    White , S. D. M., & Rees , M. J. 1978, , 183, 341, 10.1093/mnras/183.3.341

  87. [99]

    Yan , H., Lazarian , A., & Draine , B. T. 2004, , 616, 895, 10.1086/425111

  88. [100]

    2023, , 954, 113, 10.3847/1538-4357/ace7b5

    Yao , Y., Song , J., Kong , X., et al. 2023, , 954, 113, 10.3847/1538-4357/ace7b5

  89. [101]

    2013, , 560, A26, 10.1051/0004-6361/201321413

    Zafar , T., & Watson , D. 2013, , 560, A26, 10.1051/0004-6361/201321413

  90. [102]

    2014, , 562, A76, 10.1051/0004-6361/201322989

    Zhukovska , S. 2014, , 562, A76, 10.1051/0004-6361/201322989

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.