Pith. sign in

REVIEW 3 major objections 5 minor 109 references

Why do some Ultra Diffuse Galaxies have Rich Globular Cluster Systems?

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Pith's one-line read Cluster-rich ultra diffuse galaxies are best explained as failed galaxies whose globular clusters formed with extremely high efficiency — at least 40–80 percent of field-star formation — followed by modest cluster destruction, a…

desk verdict A clean two-parameter model for GC-rich UDGs, but the adopted destruction fraction is likely too high for UDGs, so the claim of very high formation efficiency is not yet supported. read the letter →

arxiv 2412.06155 v1 pith:7ZDK7VJW submitted 2024-12-09 astro-ph.GA astro-ph.CO

classification astro-ph.GAastro-ph.CO
keywords ultradiffusegalaxiesglobularclustersystemsformationefficiencydestructionfailedstellarpopulationsgalaxyquenching
open problems Dark Matter
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

Some ultra diffuse galaxies (UDGs) carry globular cluster systems whose total mass reaches about 10 percent of the galaxy's stellar mass, far above the roughly 0.5–1 percent typical of classical dwarf galaxies. The paper asks whether that excess comes from forming globular clusters with unusually high efficiency or from destroying fewer of them than usual. A two-parameter model — GC formation efficiency $c$ and destruction fraction $d$ — shows that today's ratios require very high formation efficiency (at least 40–80 percent, depending on $d$) combined with destruction fractions near 70–80 percent, rather than low destruction alone. The accompanying stellar population data loosely follow the model's prediction that field stars become more metal-poor and slightly older as the cluster-mass fraction rises, consistent with disrupted clusters seeding the field. If correct, cluster-rich UDGs are failed galaxies that formed almost entirely through globular clusters and then stopped forming stars early.

What carries the argument

The load-bearing object is a two-parameter identity, equation 6: $M_{\rm GC}/M_* = (1-d)/(1/c+d)$, derived from the assumptions that the galaxy quenches early, that the only field-star growth is through disrupted globular clusters, and that the initial cluster mass is $c$ times the initial field-star mass. A companion expression, equation 7, gives the fraction of the final stellar mass that came from disrupted clusters, $d/(1/c+d)$, and this is what the paper uses to weight the expected stellar-population shift from dwarf-like to globular-cluster-like as the cluster-mass fraction increases. The machinery's work is to turn the observed spread in $M_{\rm GC}/M_*$ — from about 0 percent for puffy-dwarf UDGs to about 10 percent for failed-galaxy candidates — into a constraint on the combination of formation and destruction that produced it.

What would settle it

Measure the integrated stellar metallicity of a robust, spectroscopically confirmed UDG with $M_{\rm GC}/M_*$ near 10 percent: the model predicts field stars there must be strongly metal-poor and GC-like because disrupted clusters dominate the field by equation 7, so finding such a galaxy with normal dwarf-like metallicity for its stellar mass would falsify the claim that high cluster efficiency drives these systems.

Watch

Extended reading notes

Core claim

The central claim is that the extreme globular-cluster richness of some UDGs is set at formation, not by survival. From equation 6, the present-day ratio $M_{\rm GC}/M_* = (1-d)/(1/c+d)$, where $c$ is the initial ratio of globular-cluster mass to field-star mass and $d$ is the fraction of globular-cluster mass destroyed through mass loss and tidal disruption. Reaching $M_{\rm GC}/M_* \sim 10\%$ today requires $c \ge 0.4$ if $d = 0.7$, $c \ge 0.8$ if $d = 0.8$, and $c > 1$ if $d = 0.9$. Since destruction fractions of 0.9 would demand impossibly high formation efficiencies, the authors conclude that cluster-rich UDGs most plausibly formed with very high GC formation efficiencies — consistent with JWST detections of high-redshift lensed galaxies in which bound clusters hold 30–70 percent of the stellar mass — and with only modest subsequent destruction. The model also predicts, and the current data loosely show, that as $M_{\rm GC}/M_*$ rises the stellar populations of UDGs become more metal-poor and slightly older, approaching the properties of old metal-poor globular clusters.

Load-bearing premise

The model's conclusions rest on assuming that ultra diffuse galaxies destroy 70–90 percent of their globular clusters, a destruction fraction taken from simulations of ordinary dwarf galaxies, even though the cluster-rich UDGs are thought to have unusually heavy dark matter halos compared with their stellar mass.

Editorial extensions

If this is right

  • Cluster-rich UDGs with $M_{\rm GC}/M_* \sim 10\%$ are most likely failed galaxies: they formed in massive halos, produced globular clusters with very high efficiency, and quenched before forming most of their field stars.
  • The field stars of the most cluster-rich UDGs should resemble old, metal-poor, alpha-enhanced globular-cluster populations; NGC5846_UDG1, with $M_{\rm GC}/M_* = 9.8\%$, matches this prediction.
  • The model predicts a continuous trend of decreasing metallicity and slightly increasing age with rising $M_{\rm GC}/M_*$, rather than a sharp division between puffy dwarfs and failed galaxies; current data loosely follow this trend.
  • High-redshift lensed galaxies seen by JWST, with 30–70 percent of their stellar mass in bound clusters, may be the formation phase of today's cluster-rich UDGs.
  • If $d = 0.9$, no plausible $c$ can reproduce the observed 10 percent upper limit, so the data favor destruction fractions near 0.7–0.8 rather than higher ones.

Reading between the lines

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

  • A sharper test of the $c$–$d$ degeneracy would be to measure the fraction of GC-like stars in UDG fields: equation 7 shows that at fixed $M_{\rm GC}/M_*$, a higher disrupted-cluster fraction means a more metal-poor, older field, so abundance patterns could break the degeneracy that a single ratio leaves open.
  • If future simulations tailored to low-surface-density, cored-halo UDGs yield destruction fractions below 0.7, the required formation efficiency drops, and cluster-rich UDGs could be ordinary dwarfs with unusually high cluster formation rather than fundamentally failed galaxies; the paper's conclusion would then weaken but its framework would still hold.
  • The model implies that the 'failed galaxy' label is better viewed as a continuum index — the fraction of stars contributed by disrupted globular clusters — rather than a binary classification; this index is measurable from integrated stellar abundances.
  • Because the JWST cluster-mass fractions are lower limits (fainter clusters are undetected), deeper imaging of the same lensed galaxies could push $c$ toward or above 1, which would make the failed-galaxy scenario even more extreme and would predict that field stars in these galaxies should be almost entirely GC-like.
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

3 major / 5 minor

Summary. The paper addresses the origin of ultra diffuse galaxies (UDGs) with unusually massive globular cluster (GC) systems. It constructs a simple two-parameter model in which the present-day GC-to-stellar mass ratio, M_GC/M_*, depends on an initial GC formation efficiency c and a destruction fraction d (Eq. 6). The model assumes early quenching and that disrupted GCs contribute their stars to the host galaxy. Using GC destruction fractions d = 0.7–0.9 from the Moreno-Hilario et al. (2024) simulations and formation efficiencies suggested by recent JWST observations of lensed galaxies, the authors conclude that UDGs with M_GC/M_* ≈ 10% require very high formation efficiencies (≥40%) combined with modest destruction. They further compare stellar population properties ([M/H], age, [Mg/Fe]) of a small UDG sample with model tracks and report a loose trend toward more GC-like populations with increasing M_GC/M_*.

Significance. If the central inference holds, the paper offers a compact, physically motivated framework for connecting GC richness to the integrated stellar populations of UDGs, and it usefully highlights the role of GC destruction in shaping present-day ratios. The model is transparent and falsifiable, and the authors are commendably explicit about the limitations of their sample and assumptions. The use of JWST lensed-galaxy cluster mass fractions as empirical anchors for c is a strength. However, the quantitative conclusion is currently weakly constrained because it hinges on a destruction fraction that is borrowed from classical dwarf simulations, and the empirical comparison is affected by small sample size, selection biases, and a model zero point defined from the same data. These issues leave the main claim defensible but not yet firmly established.

major comments (3)
  1. [Section 5, Eq. (6)] The adopted destruction fraction d = 0.7–0.9 is load-bearing for the paper's main quantitative claim, yet it is taken directly from Moreno-Hilario et al. (2024), whose simulated galaxies follow the standard stellar mass–halo mass relation by the authors' own admission. Section 4 states that this same simulation finds lower disruption rates in lower-mass, lower-density galaxies, which is used to explain why UDGs have high M_GC/M_* today. Applying the classical dwarf range d = 0.7–0.9 to UDGs is therefore inconsistent with the cited trend. The sensitivity of Eq. (6) is large: for M_GC/M_* = 10%, d = 0.7 requires c ≈ 0.43, d = 0.5 requires c ≈ 0.22, and d = 0.3 requires c ≈ 0.15. If the true UDG destruction fraction is lower, the claim that 'very high GC formation efficiencies (≥40%)' are required is no longer supported; c would fall in the range already inferred from JWST lensed galaxies. The authors should either justify a UDG-specific d range or present the inferred c as a function of d across a wider interval.
  2. [Section 6, zero point and Fig. 2] The model tracks in Fig. 2 are anchored by defining the M_GC/M_* = 0 stellar population as the average of the five sample UDGs with M_GC/M_* < 1.5%, with the high-ratio endpoint set to the assumed mean stellar population of old, metal-poor GCs. Because the low-ratio zero point comes from the same sample that is then compared with the model, the predicted trend toward more GC-like populations with increasing M_GC/M_* is partly built into the model construction rather than emerging as an independent test. This is acknowledged as a limitation ('This can be improved in the future...'), but it should be stated more prominently, and an external zero point based on classical dwarf stellar populations should be explored to see whether the trend survives.
  3. [Table 1 and Section 6] The empirical support for the claimed trends is weak. The sample contains only 12 UDGs, mostly in high-density environments, with heterogeneous GC count and stellar population measurements, and the catalogue itself is biased against GC-poor UDGs (as stated in Section 6). The claimed metallicity decrease of ~0.45 ± 0.1 dex and the weak age trend are not quantified by any fit or rank correlation; inspection of Table 1 shows substantial scatter (e.g., DF17 at M_GC/M_* = 2.1% has [M/H] = –0.83 while DF44 at 4.9% has [M/H] = –1.33, yet PUDG-R84 at 3.9% has [M/H] = –1.48). The [Mg/Fe] panel shows no clear trend. The statement that 'the current data loosely follow the model' would be more convincing with a quantitative significance estimate and an explicit discussion of how selection biases affect the comparison.
minor comments (5)
  1. [Section 2] For NGVSUDG-20, '11 GC candidates with a large uncertainty of ±8.6' should specify whether this is a Poisson uncertainty, a total uncertainty, or a confidence interval, and the sign convention should be clarified.
  2. [Fig. 1 caption] The caption would benefit from stating the range of c shown and from marking the specific c values at which the curves intersect the 10% dashed line, since those intersections carry the paper's main quantitative message.
  3. [Fig. 2 caption] The long-dashed blue line representing a constant GC-like stellar population is not defined by an equation or by explicit assumptions in the text; please state how this limiting case is constructed.
  4. [Section 6, Fig. 2] The top axis showing approximate S_N values is not introduced in the text; define the conversion from M_GC/M_* to S_N and state the assumed mass-to-light ratio and GC mean mass used for this axis.
  5. [Section 7] For Eridanus II, the statement that M_GC/M_* ≈ 4% 'from its only GC' should clarify that this assumes the universal mean GC mass of 2 × 10^5 M_sun; the dependence on this assumption is worth stating.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the M_GC/M* model is an algebraic consequence of independently adopted c and d inputs, and the stellar-population comparison has independent content at intermediate and high ratios.

full rationale

The paper's central equation (6) is derived from the definitions c = M_GC,i/M_*,i and d = 1 - M_GC,f/M_GC,i, together with the assumption that disrupted GC stars join the field population. The inference that M_GC/M* ~ 10% requires c >= 0.4 for d = 0.7 is algebra following from these definitions, not an empirical prediction fitted to the UDG data. The adopted ranges for c (roughly 30-70%) and d (0.7-0.9) come from external sources: JWST lensed-galaxy cluster mass fractions and the Moreno-Hilario et al. (2024) dwarf-galaxy simulation, respectively. The paper does not fit c or d to the UDG sample used for comparison. The stellar-population tracks in Fig. 2 are anchored at M_GC/M* = 0 to the average of five low-ratio UDGs in the sample, but the paper explicitly labels this a zero point, acknowledges it can be improved with more GC-poor UDG data, and does not present it as a prediction. The high-ratio endpoint uses an independent Milky Way GC compilation, and intermediate/high-ratio UDGs such as NGC5846_UDG1 are not used to set the track, so the comparison retains independent content. Self-citations such as Forbes & Gannon (2024) and Burkert & Forbes (2020) provide context on halo masses and GC richness but are not load-bearing for the algebraic model. The choice of d = 0.7-0.9 for UDGs may be debatable given the same simulation's trend toward lower disruption at lower density, but that is an assumption sensitivity, not circularity. No step reduces the conclusion to its input by construction.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The model rests on six chosen parameters (c, d, mean GC mass, and three zero-point stellar population values) plus domain assumptions about early quenching, mixing of disrupted GC stars, and the transferability of destruction fractions from classical dwarf simulations to UDGs. No new physical entities are introduced. The zero point is data-derived from the same sample, injecting a mild circularity into the stellar population comparison.

free parameters (6)
  • GC formation efficiency c = varied; 0.4 to 1.0 used to reach 10% ratio
    Initial ratio of GC mass to field star mass. Constraints from JWST lensed galaxies are lower limits and may not represent UDG progenitors.
  • GC destruction fraction d = assumed 0.7 to 0.9
    Taken from the Moreno-Hilario et al. (2024) simulation of classical dwarf galaxies, applied to UDGs without propagating uncertainty.
  • Mean GC mass = 2e5 solar masses
    Assumed constant for all dwarf galaxies to convert GC counts to mass; the paper notes possible variations but does not propagate them.
  • Puffy dwarf zero point metallicity [M/H] = -1.03 dex
    Average of 5 sample UDGs with M_GC/M_* < 1.5%, used as the starting point of the stellar population model.
  • Puffy dwarf zero point age = 9.5 Gyr
    Average of the same 5 UDGs, used as the low-ratio anchor for the age trend.
  • Puffy dwarf zero point [Mg/Fe] = 0.58 dex
    Average of the same 5 UDGs, used as the low-ratio anchor for the alpha-element trend.
assumptions (4)
  • domain assumption The current field star mass equals the initial field star mass plus the mass of stars from disrupted GCs, with no loss or gain of field stars via tidal stripping or accretion (Equation 3).
    Assumed in Section 5 to derive the model; if field stars are stripped or accreted, the M_GC/M_* ratio changes independently of c and d.
  • domain assumption The galaxy is quenched early with no ongoing star formation after the epoch of GC formation.
    Stated in the abstract and Section 5; required for the stellar population mixing model and for interpreting high ratios as formation effects rather than recent quenching.
  • ad hoc to paper A single global destruction fraction d applies uniformly to all GCs in the system.
    Equation 2 uses one destruction fraction for the entire GC system, ignoring the mass and orbit dependence of tidal disruption that the cited simulations include.
  • domain assumption JWST lensed galaxy cluster mass fractions are representative of GC formation efficiencies in UDG progenitors at high redshift.
    Section 5 uses these observations to motivate c values up to 0.7, but the galaxies may not be UDG progenitors and the measured fractions are lower limits.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Why do some Ultra Diffuse Galaxies have Rich Globular Cluster Systems?." pith.science (2026). https://pith.science/paper/7ZDK7VJW

@misc{pith2026241206155,
  author       = {Pith},
  title        = {Pith review of: Why do some Ultra Diffuse Galaxies have Rich Globular Cluster Systems?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7ZDK7VJW}},
  note         = {Machine review of arXiv:2412.06155}
}
abstract

Some ultra diffuse galaxies (UDGs) reveal many more globular clusters (GCs) than classical dwarf galaxies of the same stellar mass. These UDGs, with a mass in their GC system (M$_{GC}$) approaching 10\% of their host galaxy stellar mass (M$_{\ast}$), are also inferred to have high halo mass to stellar mass ratios (M$_{halo}$/M$_{\ast}$). They have been dubbed Failed Galaxies. It is unknown what role high GC formation efficiencies and/or low destruction rates play in determining the high M$_{GC}$/M$_{\ast}$ ratios of some UDGs. Here we present a simple model, which is informed by recent JWST observations of lensed galaxies and by a simulation in the literature of GC mass loss and tidal disruption in dwarf galaxies. With this simple model, we aim to constrain the effects of GC efficiency/destruction on the observed GC richness of UDGs and their variation with the integrated stellar populations of UDGs. We assume no ongoing star formation (i.e. quenching at early times) and that the disrupted GCs contribute their stars to those of the host galaxy. We find that UDGs, with high M$_{GC}$/M$_{\ast}$ ratios today, are most likely the result of very high GC formation efficiencies combined with modest rates of GC destruction. The current data loosely follow the model that ranges from the mean stellar population of classical dwarfs to that of metal-poor GCs as M$_{GC}$/M$_{\ast}$ increases. As more data becomes available for UDGs, our simple model can be refined and tested further.

Figures

Figures reproduced from arXiv: 2412.06155 by the authors.

Figure 1
Figure 1. M𝐺𝐶/M∗ as a function of GC formation efficiency (𝑐). Three curves, from eq. 6, show GC destruction fractions of 𝑑 = 0.7, 0.8 and 0.9. Destruction includes mass loss and tidal disruption. In order to reproduce the observed upper limit today for UDGs of M𝐺𝐶/M∗ ∼10% (dashed line) a com￾bination of either modest GC destruction rates (∼70–80%) and/or very high GC formation efficiencies (≥40%) are required. GC-poor galaxi… view at source ↗
Figure 2
Figure 2. Galaxy stellar populations as a function of GC system mass to host galaxy stellar mass M𝐺𝐶/M∗. Panels show total metallicity (in dex), age (in Gyr) and [Mg/Fe] (in dex) from top to bottom, respectively. The red lines represent our simple model starting at M𝐺𝐶/M∗ = 0 (puffy dwarfs) and as M𝐺𝐶/M∗ increases the stellar population changes to be more GC-like with weights given by the fraction of disrupted GC mass (see te… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

109 extracted references · 9 canonical work pages

  1. [1]

    arXiv:2401.03224

    Adamo A., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2401.03224 , https://ui.adsabs.harvard.edu/abs/2024arXiv240103224A p. arXiv:2401.03224

  2. [2]

    P., 1988, @doi [ ] 10.1086/166961 , https://ui.adsabs.harvard.edu/abs/1988ApJ...335..720A 335, 720

    Aguilar L., Hut P., Ostriker J. P., 1988, @doi [ ] 10.1086/166961 , https://ui.adsabs.harvard.edu/abs/1988ApJ...335..720A 335, 720

  3. [3]

    Alabi A., et al., 2018, @doi [MNRAS] 10.1093/mnras/sty1616 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.3308A 479, 3308

  4. [4]

    C., Loeb A., 2016, @doi [ ] 10.1093/mnrasl/slw055 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459L..51A 459, L51

    Amorisco N. C., Loeb A., 2016, @doi [ ] 10.1093/mnrasl/slw055 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459L..51A 459, L51

  5. [5]

    A., Romanowsky A

    Beasley M. A., Romanowsky A. J., Pota V., Navarro I. M., Martinez Delgado D., Neyer F., Deich A. L., 2016, @doi [ApJL] 10.3847/2041-8205/819/2/L20 , https://ui.adsabs.harvard.edu/abs/2016ApJ...819L..20B 819, L20

  6. [6]

    A., et al., 2021, @doi [Nature Astronomy] 10.1038/s41550-021-01458-1 , https://ui.adsabs.harvard.edu/abs/2021NatAs...5.1255B 5, 1255

    Benavides J. A., et al., 2021, @doi [Nature Astronomy] 10.1038/s41550-021-01458-1 , https://ui.adsabs.harvard.edu/abs/2021NatAs...5.1255B 5, 1255

  7. [7]

    A., 2020, @doi [ ] 10.3847/1538-3881/ab5b0e , https://ui.adsabs.harvard.edu/abs/2020AJ....159...56B 159, 56

    Burkert A., Forbes D. A., 2020, @doi [ ] 10.3847/1538-3881/ab5b0e , https://ui.adsabs.harvard.edu/abs/2020AJ....159...56B 159, 56

  8. [8]

    L., et al., 2022, @doi [ ] 10.1093/mnras/stac2442 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.2231B 517, 2231

    Buzzo M. L., et al., 2022, @doi [ ] 10.1093/mnras/stac2442 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.2231B 517, 2231

Show all 109 references
  1. [9]

    L., et al., 2024, @doi [ ] 10.1093/mnras/stae564 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3210B 529, 3210

    Buzzo M. L., et al., 2024, @doi [ ] 10.1093/mnras/stae564 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3210B 529, 3210

  2. [10]

    Carleton T., Guo Y., Munshi F., Tremmel M., Wright A., 2021, @doi [ ] 10.1093/mnras/stab031 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502..398C 502, 398

  3. [11]

    Y., 2023, @doi [ ] 10.1093/mnras/stad1328 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5638C 522, 5638

    Chen Y., Gnedin O. Y., 2023, @doi [ ] 10.1093/mnras/stad1328 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5638C 522, 5638

  4. [12]

    arXiv:2405.18735

    Chen Y., Mo H., Wang H., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2405.18735 , https://ui.adsabs.harvard.edu/abs/2024arXiv240518735C p. arXiv:2405.18735

  5. [13]

    V., Afanasiev A

    Chilingarian I. V., Afanasiev A. V., Grishin K. A., Fabricant D., Moran S., 2019, @doi [ApJ] 10.3847/1538-4357/ab4205 , https://ui.adsabs.harvard.edu/abs/2019ApJ...884...79C 884, 79

  6. [14]

    Y., 2019, @doi [ ] 10.1093/mnras/stz2097 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.5409C 488, 5409

    Choksi N., Gnedin O. Y., 2019, @doi [ ] 10.1093/mnras/stz2097 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.5409C 488, 5409

  7. [16]

    Collins M. L. M., Tollerud E. J., Rich R. M., Ibata R. A., Martin N. F., Chapman S. C., Gilbert K. M., Preston J., 2020, @doi [MNRAS] 10.1093/mnras/stz3252 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.3496C 491, 3496

  8. [17]

    J., 2019, @doi [ApJL] 10.3847/2041-8213/ab0e8c , https://ui.adsabs.harvard.edu/abs/2019ApJ...874L..12D 874, L12

    Danieli S., van Dokkum P., Conroy C., Abraham R., Romanowsky A. J., 2019, @doi [ApJL] 10.3847/2041-8213/ab0e8c , https://ui.adsabs.harvard.edu/abs/2019ApJ...874L..12D 874, L12

  9. [18]

    Danieli S., et al., 2022, @doi [ ] 10.3847/2041-8213/ac590a , https://ui.adsabs.harvard.edu/abs/2022ApJ...927L..28D 927, L28

  10. [19]

    B., Dutton A

    Di Cintio A., Brook C. B., Dutton A. A., Macci \`o A. V., Obreja A., Dekel A., 2017, @doi [ ] 10.1093/mnrasl/slw210 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466L...1D 466, L1

  11. [20]

    E., et al., 2023, @doi [ ] 10.1093/mnras/stac2818 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.2453D 518, 2453

    Doppel J. E., et al., 2023, @doi [ ] 10.1093/mnras/stac2818 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.2453D 518, 2453

  12. [21]

    Fahrion K., Leaman R., Lyubenova M., van de Ven G., 2022, @doi [ ] 10.1051/0004-6361/202039778 , https://ui.adsabs.harvard.edu/abs/2022A&A...658A.172F 658, A172

  13. [22]

    Fensch J., et al., 2019, @doi [A&A] 10.1051/0004-6361/201834911 , https://ui.adsabs.harvard.edu/abs/2019A&A...625A..77F 625, A77

  14. [23]

    Ferr \'e -Mateu A., et al., 2018, @doi [MNRAS] 10.1093/mnras/sty1597 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.4891F 479, 4891

  15. [24]

    S., Forbes D

    Ferr \'e -Mateu A., Gannon J. S., Forbes D. A., Buzzo M. L., Romanowsky A. J., Brodie J. P., 2023, @doi [ ] 10.1093/mnras/stad3102 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.4735F 526, 4735

  16. [25]

    E., Jones M

    Fielder C. E., Jones M. G., Sand D. J., Bennet P., Crnojevi \'c D., Karunakaran A., Mutlu-Pakdil B., Spekkens K., 2023, @doi [ ] 10.3847/2041-8213/acf0c3 , https://ui.adsabs.harvard.edu/abs/2023ApJ...954L..39F 954, L39

  17. [26]

    A., 2017, @doi [ ] 10.1093/mnrasl/slx148 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472L.104F 472, L104

    Forbes D. A., 2017, @doi [ ] 10.1093/mnrasl/slx148 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472L.104F 472, L104

  18. [27]

    A., Gannon J., 2024, @doi [ ] 10.1093/mnras/stad4004 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528..608F 528, 608

    Forbes D. A., Gannon J., 2024, @doi [ ] 10.1093/mnras/stad4004 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528..608F 528, 608

  19. [28]

    A., et al., 2018a, @doi [Proceedings of the Royal Society of London Series A] 10.1098/rspa.2017.0616 , https://ui.adsabs.harvard.edu/abs/2018RSPSA.47470616F 474, 20170616

    Forbes D. A., et al., 2018a, @doi [Proceedings of the Royal Society of London Series A] 10.1098/rspa.2017.0616 , https://ui.adsabs.harvard.edu/abs/2018RSPSA.47470616F 474, 20170616

  20. [29]

    A., Read J

    Forbes D. A., Read J. I., Gieles M., Collins M. L. M., 2018b, @doi [ ] 10.1093/mnras/sty2584 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.5592F 481, 5592

  21. [30]

    A., Alabi A., Romanowsky A

    Forbes D. A., Alabi A., Romanowsky A. J., Brodie J. P., Arimoto N., 2020, @doi [ ] 10.1093/mnras/staa180 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.4874F 492, 4874

  22. [31]

    A., Gannon J

    Forbes D. A., Gannon J. S., Romanowsky A. J., Alabi A., Brodie J. P., Couch W. J., Ferr \'e -Mateu A., 2021, @doi [ ] 10.1093/mnras/staa3289 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.1279F 500, 1279

  23. [32]

    A., et al., 2023, @doi [ ] 10.1093/mnrasl/slad101 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525L..93F 525, L93

    Forbes D. A., et al., 2023, @doi [ ] 10.1093/mnrasl/slad101 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525L..93F 525, L93

  24. [33]

    A., Lyon D., Gannon J., Romanowsky A

    Forbes D. A., Lyon D., Gannon J., Romanowsky A. J., Brodie J. P., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2405.11749 , https://ui.adsabs.harvard.edu/abs/2024arXiv240511749F p. arXiv:2405.11749

  25. [34]

    arXiv:2402.18543

    Fujimoto S., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2402.18543 , https://ui.adsabs.harvard.edu/abs/2024arXiv240218543F p. arXiv:2402.18543

  26. [35]

    S., Forbes D

    Gannon J. S., Forbes D. A., Romanowsky A. J., Ferr \'e -Mateu A., Couch W. J., Brodie J. P., 2020, @doi [MNRAS] 10.1093/mnras/staa1282 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.2582G 495, 2582

  27. [36]

    S., et al., 2021, @doi [ ] 10.1093/mnras/stab277 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.3144G 502, 3144

    Gannon J. S., et al., 2021, @doi [ ] 10.1093/mnras/stab277 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.3144G 502, 3144

  28. [38]

    S., et al., 2022b, @doi [MNRAS] 10.1093/mnras/stab3297 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510..946G 510, 946

    Gannon J. S., et al., 2022b, @doi [MNRAS] 10.1093/mnras/stab3297 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.510..946G 510, 946

  29. [39]

    S., Forbes D

    Gannon J. S., Forbes D. A., Brodie J. P., Romanowsky A. J., Couch W. J., Ferr \'e -Mateu A., 2023, @doi [MNRAS] 10.1093/mnras/stac3264 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.3653G 518, 3653

  30. [40]

    S., Ferr \'e -Mateu A., Forbes D

    Gannon J. S., Ferr \'e -Mateu A., Forbes D. A., Brodie J. P., Buzzo M. L., Romanowsky A. J., 2024, @doi [ ] 10.1093/mnras/stae1287 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.1856G 531, 1856

  31. [41]

    Y., Puzia T

    Georgiev I. Y., Puzia T. H., Goudfrooij P., Hilker M., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16802.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.406.1967G 406, 1967

  32. [43]

    Y., Ostriker J

    Gnedin O. Y., Ostriker J. P., 1997, @doi [ ] 10.1086/303441 , https://ui.adsabs.harvard.edu/abs/1997ApJ...474..223G 474, 223

  33. [44]

    Gu M., et al., 2018, @doi [ApJ] 10.3847/1538-4357/aabbae , https://ui.adsabs.harvard.edu/abs/2018ApJ...859...37G 859, 37

  34. [45]

    E., Harris G

    Harris W. E., Harris G. L. H., Alessi M., 2013, @doi [ ] 10.1088/0004-637X/772/2/82 , https://ui.adsabs.harvard.edu/abs/2013ApJ...772...82H 772, 82

  35. [46]

    E., Blakeslee J

    Harris W. E., Blakeslee J. P., Harris G. L. H., 2017, @doi [ ] 10.3847/1538-4357/836/1/67 , https://ui.adsabs.harvard.edu/abs/2017ApJ...836...67H 836, 67

  36. [47]

    Heesters N., et al., 2023, @doi [ ] 10.1051/0004-6361/202346441 , https://ui.adsabs.harvard.edu/abs/2023A&A...676A..33H 676, A33

  37. [48]

    E., 2021, @doi [ ] 10.1093/mnras/staa3297 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500..986H 500, 986

    Huang K.-W., Koposov S. E., 2021, @doi [ ] 10.1093/mnras/staa3297 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500..986H 500, 986

  38. [49]

    J., Robison B., 2018, @doi [ ] 10.1093/mnras/sty844 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.3869H 477, 3869

    Hudson M. J., Robison B., 2018, @doi [ ] 10.1093/mnras/sty844 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.3869H 477, 3869

  39. [50]

    Iodice E., et al., 2020, @doi [A&A] 10.1051/0004-6361/202038523 , https://ui.adsabs.harvard.edu/abs/2020A&A...642A..48I 642, A48

  40. [51]

    arXiv:2308.11493

    Iodice E., et al., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2308.11493 , https://ui.adsabs.harvard.edu/abs/2023arXiv230811493I p. arXiv:2308.11493

  41. [52]

    R., Abraham R., Brodie J., Forbes D

    Janssens S. R., Abraham R., Brodie J., Forbes D. A., Romanowsky A. J., 2019, @doi [ ] 10.3847/1538-4357/ab536c , https://ui.adsabs.harvard.edu/abs/2019ApJ...887...92J 887, 92

  42. [53]

    R., et al., 2022, @doi [ ] 10.1093/mnras/stac2717 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517..858J 517, 858

    Janssens S. R., et al., 2022, @doi [ ] 10.1093/mnras/stac2717 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517..858J 517, 858

  43. [54]

    R., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2409.07518 , https://ui.adsabs.harvard.edu/abs/2024arXiv240907518J p

    Janssens S. R., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2409.07518 , https://ui.adsabs.harvard.edu/abs/2024arXiv240907518J p. arXiv:2409.07518

  44. [55]

    P., et al., 2021, @doi [ ] 10.3847/1538-4357/ac1869 , https://ui.adsabs.harvard.edu/abs/2021ApJ...921...32J 921, 32

    Ji A. P., et al., 2021, @doi [ ] 10.3847/1538-4357/ac1869 , https://ui.adsabs.harvard.edu/abs/2021ApJ...921...32J 921, 32

  45. [56]

    J., Dutton A

    Jiang F., Dekel A., Freundlich J., Romanowsky A. J., Dutton A. A., Macci \`o A. V., Di Cintio A., 2019, @doi [ ] 10.1093/mnras/stz1499 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.5272J 487, 5272

  46. [57]

    G., et al., 2023, @doi [ ] 10.3847/2041-8213/acaaab , https://ui.adsabs.harvard.edu/abs/2023ApJ...942L...5J 942, L5

    Jones M. G., et al., 2023, @doi [ ] 10.3847/2041-8213/acaaab , https://ui.adsabs.harvard.edu/abs/2023ApJ...942L...5J 942, L5

  47. [58]

    Jord \'a n A., et al., 2006, @doi [ ] 10.1086/509119 , https://ui.adsabs.harvard.edu/abs/2006ApJ...651L..25J 651, L25

  48. [59]

    D., Makarova L

    Karachentsev I. D., Makarova L. N., Sharina M. E., Karachentseva V. E., 2017, @doi [Astrophysical Bulletin] 10.1134/S1990341317040022 , https://ui.adsabs.harvard.edu/abs/2017AstBu..72..376K 72, 376

  49. [60]

    Karunakaran A., Zaritsky D., 2023, @doi [ ] 10.1093/mnras/stac3622 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519..884K 519, 884

  50. [61]

    C., Remus R.-S., Seidel B., Valenzuela L

    Kimmig L. C., Remus R.-S., Seidel B., Valenzuela L. M., Dolag K., Burkert A., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2310.16085 , https://ui.adsabs.harvard.edu/abs/2023arXiv231016085K p. arXiv:2310.16085

  51. [62]

    Kruijssen J. M. D., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21923.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.426.3008K 426, 3008

  52. [63]

    La Marca A., et al., 2022, @doi [ ] 10.1051/0004-6361/202142367 , https://ui.adsabs.harvard.edu/abs/2022A&A...665A.105L 665, A105

  53. [64]

    S., Strader J., Brodie J

    Larsen S. S., Strader J., Brodie J. P., 2012, @doi [ ] 10.1051/0004-6361/201219897 , https://ui.adsabs.harvard.edu/abs/2012A&A...544L..14L 544, L14

  54. [65]

    S., Strader J., Brodie J

    Larsen S. S., Strader J., Brodie J. P., 2013, @doi [ ] 10.48550/arXiv.1301.4104 , https://ui.adsabs.harvard.edu/abs/2013MmSAI..84...38L 84, 38

  55. [66]

    N., Cooper A

    Le M. N., Cooper A. P., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2408.07124 , https://ui.adsabs.harvard.edu/abs/2024arXiv240807124L p. arXiv:2408.07124

  56. [67]

    K., Koch A., 2010, @doi [ ] 10.1051/0004-6361/200913364 , https://ui.adsabs.harvard.edu/abs/2010A&A...521A..43L 521, A43

    Lianou S., Grebel E. K., Koch A., 2010, @doi [ ] 10.1051/0004-6361/200913364 , https://ui.adsabs.harvard.edu/abs/2010A&A...521A..43L 521, A43

  57. [68]

    W., C \^o t \'e P., Sales L

    Lim S., Peng E. W., C \^o t \'e P., Sales L. V., den Brok M., Blakeslee J. P., Guhathakurta P., 2018, @doi [ ] 10.3847/1538-4357/aacb81 , https://ui.adsabs.harvard.edu/abs/2018ApJ...862...82L 862, 82

  58. [69]

    Lim S., et al., 2020, @doi [ ] 10.3847/1538-4357/aba433 , https://ui.adsabs.harvard.edu/abs/2020ApJ...899...69L 899, 69

  59. [70]

    Mart \' n-Navarro I., et al., 2019, @doi [ ] 10.1093/mnras/stz252 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.3425M 484, 3425

  60. [71]

    F., et al., 2016, @doi [ApJ] 10.3847/1538-4357/833/2/167 , https://ui.adsabs.harvard.edu/abs/2016ApJ...833..167M 833, 167

    Martin N. F., et al., 2016, @doi [ApJ] 10.3847/1538-4357/833/2/167 , https://ui.adsabs.harvard.edu/abs/2016ApJ...833..167M 833, 167

  61. [72]

    Mart \' nez-Delgado D., et al., 2016, @doi [AJ] 10.3847/0004-6256/151/4/96 , https://ui.adsabs.harvard.edu/abs/2016AJ....151...96M 151, 96

  62. [73]

    W., 2012, @doi [AJ] 10.1088/0004-6256/144/1/4 , https://ui.adsabs.harvard.edu/abs/2012AJ....144....4M 144, 4

    McConnachie A. W., 2012, @doi [AJ] 10.1088/0004-6256/144/1/4 , https://ui.adsabs.harvard.edu/abs/2012AJ....144....4M 144, 4

  63. [74]

    C., et al., 2022, @doi [ApJ] 10.3847/1538-4357/ac35d9 , https://ui.adsabs.harvard.edu/abs/2022ApJ...924...87M 924, 87

    Mihos J. C., et al., 2022, @doi [ApJ] 10.3847/1538-4357/ac35d9 , https://ui.adsabs.harvard.edu/abs/2022ApJ...924...87M 924, 87

  64. [75]

    A., et al., 2016, @doi [ ] 10.1093/mnras/stv2435 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.455.2323M 455, 2323

    Mistani P. A., et al., 2016, @doi [ ] 10.1093/mnras/stv2435 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.455.2323M 455, 2323

  65. [76]

    A., Li H., Souza S

    Moreno-Hilario E., Martinez-Medina L. A., Li H., Souza S. O., P \'e rez-Villegas A., 2024, @doi [ ] 10.1093/mnras/stad3306 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.2765M 527, 2765

  66. [77]

    Mowla L., van Dokkum P., Merritt A., Abraham R., Yagi M., Koda J., 2017, @doi [ ] 10.3847/1538-4357/aa961b , https://ui.adsabs.harvard.edu/abs/2017ApJ...851...27M 851, 27

  67. [78]

    arXiv:2402.08696

    Mowla L., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2402.08696 , https://ui.adsabs.harvard.edu/abs/2024arXiv240208696M p. arXiv:2402.08696

  68. [79]

    M \"u ller O., et al., 2020, @doi [ ] 10.1051/0004-6361/202038351 , https://ui.adsabs.harvard.edu/abs/2020A&A...640A.106M 640, A106

  69. [80]

    M \"u ller O., et al., 2021, @doi [ApJ] 10.3847/1538-4357/ac2831 , https://ui.adsabs.harvard.edu/abs/2021ApJ...923....9M 923, 9

  70. [81]

    Okamoto S., et al., 2024, @doi [ ] 10.3847/2041-8213/ad4358 , https://ui.adsabs.harvard.edu/abs/2024ApJ...967L..24O 967, L24

  71. [82]

    C., Springel V., van de Voort F., 2023, @doi [ ] 10.1093/mnras/stac3776 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.2692P 520, 2692

    Pasha I., Mandelker N., van den Bosch F. C., Springel V., van de Voort F., 2023, @doi [ ] 10.1093/mnras/stac3776 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.2692P 520, 2692

  72. [83]

    W., Lim S., 2016, @doi [ ] 10.3847/2041-8205/822/2/L31 , https://ui.adsabs.harvard.edu/abs/2016ApJ...822L..31P 822, L31

    Peng E. W., Lim S., 2016, @doi [ ] 10.3847/2041-8205/822/2/L31 , https://ui.adsabs.harvard.edu/abs/2016ApJ...822L..31P 822, L31

  73. [84]

    Pfeffer J., Kruijssen J. M. D., Crain R. A., Bastian N., 2018, @doi [ ] 10.1093/mnras/stx3124 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.4309P 475, 4309

  74. [85]

    Pfeffer J., et al., 2024, @doi [ ] 10.1093/mnras/stae850 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.4914P 529, 4914

  75. [86]

    J., van der Burg R

    Prole D. J., van der Burg R. F. J., Hilker M., Davies J. I., 2019, @doi [ ] 10.1093/mnras/stz1843 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2143P 488, 2143

  76. [87]

    Recio-Blanco A., 2018, @doi [ ] 10.1051/0004-6361/201833179 , https://ui.adsabs.harvard.edu/abs/2018A&A...620A.194R 620, A194

  77. [88]

    Renzini A., 2017, @doi [ ] 10.1093/mnrasl/slx057 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.469L..63R 469, L63

  78. [89]

    Rom \'a n J., Trujillo I., 2017, @doi [ ] 10.1093/mnras/stx694 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.4039R 468, 4039

  79. [90]

    Ruiz-Lara T., et al., 2018, @doi [MNRAS] 10.1093/mnras/sty1112 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.2034R 478, 2034

  80. [91]

    F., Knapen J

    Saifollahi T., Zaritsky D., Trujillo I., Peletier R. F., Knapen J. H., Amorisco N., Beasley M. A., Donnerstein R., 2022, @doi [ ] 10.1093/mnras/stac328 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.4633S 511, 4633

  81. [92]

    P., et al., 2017, @doi [ ] 10.1093/mnras/stw2162 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..501S 465, 501

    Schiavon R. P., et al., 2017, @doi [ ] 10.1093/mnras/stw2162 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..501S 465, 501

  82. [93]

    Shen Z., et al., 2021, @doi [ApJL] 10.3847/2041-8213/ac0335 , https://ui.adsabs.harvard.edu/abs/2021ApJ...914L..12S 914, L12

  83. [94]

    arXiv:2309.08592

    Shen Z., van Dokkum P., Danieli S., 2023, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2023arXiv230908592S p. arXiv:2309.08592

  84. [95]

    D., 2019, @doi [ ] 10.1146/annurev-astro-091918-104453 , https://ui.adsabs.harvard.edu/abs/2019ARA&A..57..375S 57, 375

    Simon J. D., 2019, @doi [ ] 10.1146/annurev-astro-091918-104453 , https://ui.adsabs.harvard.edu/abs/2019ARA&A..57..375S 57, 375

  85. [96]

    R., Forbes D

    Spitler L. R., Forbes D. A., 2009, @doi [ ] 10.1111/j.1745-3933.2008.00567.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.392L...1S 392, L1

  86. [97]

    Toloba E., et al., 2018, @doi [ApJL] 10.3847/2041-8213/aab603 , https://ui.adsabs.harvard.edu/abs/2018ApJ...856L..31T 856, L31

  87. [98]

    Toloba E., et al., 2023, @doi [ ] 10.3847/1538-4357/acd336 , https://ui.adsabs.harvard.edu/abs/2023ApJ...951...77T 951, 77

  88. [99]

    W., et al., 2024, @doi [ ] 10.1093/mnras/stae682 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3301T 529, 3301

    Topping M. W., et al., 2024, @doi [ ] 10.1093/mnras/stae682 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3301T 529, 3301

  89. [100]

    Torrealba G., et al., 2019, @doi [MNRAS] 10.1093/mnras/stz1624 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2743T 488, 2743

  90. [101]

    M., Moster B

    Valenzuela L. M., Moster B. P., Remus R.-S., O'Leary J. A., Burkert A., 2021, @doi [ ] 10.1093/mnras/stab1701 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.5815V 505, 5815

  91. [102]

    Villaume A., et al., 2022, @doi [ApJ] 10.3847/1538-4357/ac341e , https://ui.adsabs.harvard.edu/abs/2022ApJ...924...32V 924, 32

  92. [103]

    A., et al., 2022, @doi [MNRAS] 10.1093/mnras/stac2417 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.3318W 516, 3318

    Webb K. A., et al., 2022, @doi [MNRAS] 10.1093/mnras/stac2417 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.3318W 516, 3318

  93. [104]

    R., Savino A., Dolphin A

    Weisz D. R., Savino A., Dolphin A. E., 2023, @doi [ ] 10.3847/1538-4357/acc328 , https://ui.adsabs.harvard.edu/abs/2023ApJ...948...50W 948, 50

  94. [105]

    Yagi M., Koda J., Komiyama Y., Yamanoi H., 2016, @doi [ApJS] 10.3847/0067-0049/225/1/11 , https://ui.adsabs.harvard.edu/abs/2016ApJS..225...11Y 225, 11

  95. [106]

    Yozin C., Bekki K., 2015, @doi [ ] 10.1093/mnras/stv1073 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..937Y 452, 937

  96. [107]

    G., Abraham R., Merritt A., Zhang J., Geha M., Conroy C., 2015, @doi [ ] 10.1088/2041-8205/798/2/L45 , https://ui.adsabs.harvard.edu/abs/2015ApJ...798L..45V 798, L45

    van Dokkum P. G., Abraham R., Merritt A., Zhang J., Geha M., Conroy C., 2015, @doi [ ] 10.1088/2041-8205/798/2/L45 , https://ui.adsabs.harvard.edu/abs/2015ApJ...798L..45V 798, L45

  97. [108]

    van Dokkum P., et al., 2016, @doi [ApJL] 10.3847/2041-8205/828/1/L6 , https://ui.adsabs.harvard.edu/abs/2016ApJ...828L...6V 828, L6

  98. [109]

    van Dokkum P., et al., 2017, @doi [ ] 10.3847/2041-8213/aa7ca2 , https://ui.adsabs.harvard.edu/abs/2017ApJ...844L..11V 844, L11

  99. [110]

    van Dokkum P., et al., 2018, @doi [Nature] 10.1038/nature25767 , https://ui.adsabs.harvard.edu/abs/2018Natur.555..629V 555, 629

  100. [111]

    van Dokkum P., et al., 2019, @doi [ ] 10.3847/1538-4357/ab2914 , https://ui.adsabs.harvard.edu/abs/2019ApJ...880...91V 880, 91

  101. [112]

    van der Burg R. F. J., et al., 2017, @doi [ ] 10.1051/0004-6361/201731335 , https://ui.adsabs.harvard.edu/abs/2017A&A...607A..79V 607, A79

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.