Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

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

T0 review · 4 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Most intracluster light in a Virgo-like cluster originates from satellite galaxies rather than from the central galaxy itself, with a distinct pre-stripped population tracing dark matter.

desk verdict A serious high-resolution ICL origin decomposition of one Virgo-like cluster, plausible and well executed, but the headline fractions and the most novel claims lean on acknowledged arbitrary thresholds and a subjective preprocessed classification. read the letter →

arxiv 2512.06098 v1 pith:RATA3SDK submitted 2025-12-05 astro-ph.GA

Seyoung Jeon , Emanuele Contini , San Han , Jinsu Rhee , Garreth Martin , Juhan Kim , Jaehyun Lee , Taysun Kimm
show 6 more authors
Christophe Pichon Gyeong-Hwan Byun Yohan Dubois Corentin Cadiou J. K. Jang Sukyoung K. Yi
This is my paper · ORCID
classification astro-ph.GA
keywords intraclusterlightgalaxyclusterstidalstrippingpreprocessingmergertreeshydrodynamicalsimulationsstellarpopulationsdarkmattertracers
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 tries to establish where a galaxy cluster's diffuse intracluster light comes from and what it can reveal about assembly history. Using the high-resolution NewCluster simulation at redshift 0.79, the authors track individual stellar particles across a dense sequence of snapshots and sort the diffuse stellar component into four origins: stars stripped from surviving satellites, stars from disrupted satellites, stars born in the central galaxy, and stars that became unbound before cluster infall (preprocessed). They find that satellite-derived stars make up the majority (roughly 55-60%) of the BCG+ICL, and that the preprocessed component follows the dark matter density profile better than any other stellar population and stands out chemically as old, metal-poor, and alpha-enhanced. If correct, this gives observers a quantitative route from the demographics and chemistry of intracluster light to the dynamical history of the cluster.

What carries the argument

The load-bearing machinery is a time-resolved merger tree built from phase-space-weighted bidirectional match scores, combined with a membership classification that approximates binding without full potential calculations: stars present in more than 90% of pre-infall snapshots are 'tight' members, those present in more than 10% are 'loose' members, and stars showing clear evidence of unbinding before cluster infall are classified as 'preprocessed'. This machinery defines the BCG+ICL sample itself and the four origin channels; every demographic fraction and population contrast in the paper follows from it.

What would settle it

Compute exact gravitational binding energies at infall for the same stellar particles classified as tight, loose, and preprocessed; if most loose members turn out to be bound, or most tight members unbound, the reported origin fractions would not hold. Observationally, a deep spectroscopic map of a Virgo-like cluster at radii beyond about 0.3 R200 that shows uniform [alpha/Fe] rather than an old, metal-poor, alpha-enhanced subpopulation would weigh against the preprocessed identification.

Watch

Extended reading notes

Core claim

The central claim is that, in this Virgo-mass cluster, most BCG+ICL mass is accreted rather than formed in place: stripped stars from surviving satellites contribute 33.4%, disrupted satellites contribute about 22%, in-situ BCG stars contribute about one third, and preprocessed stars contribute roughly 12%. The preprocessed population is the key new finding: stars unbound before cluster infall follow the dark matter density profile more closely than the other stellar components and have distinctive old, low-metallicity, alpha-enhanced populations. The authors also claim that the stripped fraction of a satellite is primarily determined by time since infall and pericenter distance, not by orbi

Load-bearing premise

The central assumption is that the >90% versus >10% membership thresholds, applied within the 500 Myr pre-infall window, correctly separate stars truly bound to satellites from cluster light; if that classification is wrong, every origin fraction and the distinctiveness of the preprocessed population would shift.

Editorial extensions

If this is right

  • Satellite stripping and disruption, not in-situ star formation, dominate the diffuse light of a Virgo-like cluster; the satellite contribution is expected to grow toward lower redshift as surviving satellites merge.
  • The preprocessed component is a candidate luminous tracer of dark matter, since it follows the dark matter density profile better than any other stellar component.
  • Time since infall and minimum pericenter distance determine how much stellar mass a satellite loses to the ICL, so the spatial distribution of intracluster light can be read as a record of orbital history.
  • Age, metallicity, and [alpha/Fe] offsets give the preprocessed population an observable chemical fingerprint, and a phase-space boundary near 0.1 R200 separates BCG-born stars from accreted stars.
  • The measured ~41% BCG+ICL mass fraction at z=0.79 provides a benchmark for ICL evolution at intermediate redshift.

Reading between the lines

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

  • The tight/loose thresholds (>90% and >10% occurrence) and the 500 Myr pre-infall window are arbitrary; a direct comparison with explicit binding-energy calculations for the same particles would test whether the reported origin fractions are stable.
  • If the preprocessed chemical fingerprint is real, deep spectroscopic observations of intracluster light at large cluster radii could identify this component and measure cluster assembly history; the authors note this is not yet feasible with current techniques.
  • At z=0, some currently surviving satellites will merge and migrate from the 'stripped' to the 'disrupted' channel, so the ratio of these channels is a redshift-dependent descriptor that one-halo time tracking could calibrate.
  • The single-cluster design does not constrain cluster-to-cluster variation; testing whether the preprocessed fraction correlates with cluster dynamical state or concentration would turn this method into a statistical tool.
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 / 5 minor

Summary. The paper uses the high-resolution NewCluster zoom-in cosmological simulation at z=0.79 to classify the BCG+ICL stellar particles into four origin channels: stripped from surviving satellites, stripped from disrupted (merged) satellites, in-situ formation in the BCG, and preprocessed (unbound before cluster infall). The authors report that satellite-derived stars make up the majority (~55%) of the BCG+ICL, that the preprocessed component follows the dark matter density profile more closely than other components and is old, metal-poor, and [α/Fe]-enhanced, and that the stripped fraction of satellites is chiefly controlled by time since infall and pericenter distance. The analysis relies on a new merger tree algorithm and particle tracking across dense snapshots.

Significance. If the central demographic and stellar-population results are robust, this would be an important contribution to ICL origin studies: it provides a high-resolution, particle-resolved census of ICL formation channels and identifies the preprocessed component as a potentially distinct tracer of dark matter and assembly history. The paper ships reproducible code (YoungTree, GitHub/Zenodo) and demonstrates a technically demanding tracking procedure on ~200 million stellar particles. However, the results are derived from a single unrelaxed cluster at z=0.79, and the central classification relies on thresholds and visual inspection that are not tested for sensitivity. The significance is therefore conditional on the robustness of the classification.

major comments (4)
  1. [Section 3, Figure 4] The tight/loose membership thresholds (>90% vs >10% occurrence in a 500 Myr pre-infall window) are admittedly 'somewhat arbitrary'. These thresholds directly set the boundary between satellite-bound and BCG+ICL stars, and hence the headline claim that satellites contribute ~55% of the BCG+ICL. A threshold shift could plausibly move the fraction below 50% or change the preprocessed/stripped split. Please provide a sensitivity analysis (e.g., varying the thresholds and the window) and quantify how the fractions and the preprocessed properties respond.
  2. [Section 3.4, Figure 6] The preprocessed classification relies on 'clear evidence of prior unbinding' assessed visually from orbital trajectories. This is subjective and is the basis for the paper's most novel claims (old, metal-poor, α-enhanced, DM-tracing). Please replace or supplement the visual criterion with a quantitative boundness measure (e.g., energy-based or a defined threshold in the peri/apocenter evolution), and test how the preprocessed fraction and its stellar-population contrasts change with alternative definitions.
  3. [Sections 4 and 5] The distinctive properties of the preprocessed component—old age, low metallicity, high [α/Fe]—are partly imposed by the selection: stars classified as preprocessed were unbound before infall, so they cannot include stars born after that epoch, and their extended/mixed orbits follow from the prior-unbinding criterion. This circularity should be acknowledged explicitly, and ideally the paper should show that the properties are not simply an artifact of the selection definition (e.g., by comparing with a control sample of loosely-bound stars without prior unbinding).
  4. [Section 7.1] The analysis covers one cluster at z=0.79 that is explicitly described as unrelaxed and about to merge with a secondary halo. The paper appropriately labels itself a case study, but the abstract and conclusions state the satellite-majority claim and the DM-tracing property without this caveat. Please temper the general claims and quantify how the quoted fractions might depend on the dynamical state (or defer such statements until multi-cluster statistics are available).
minor comments (5)
  1. [Abstract vs. Section 8] The abstract says 'majority' (~55% in the text), while Section 8 reports '~60%'. Please reconcile these numbers.
  2. [Section 2.3, Eq. (7)] The exponent C in q^(3) is described as 'tunable' but its value used in the analysis is not stated. Please report the chosen value and the sensitivity of the merger tree to it.
  3. [Section 4, Figure 7(f)] The mass fractions in the pie chart sum to 100% but the text quotes 33.4% stripped + ~22% disrupted + ~33% in-situ + ~12% preprocessed = ~100.4%. Please ensure the quoted percentages are consistent.
  4. [Section 5, Figure 9] The statement that j* 'does not provide an independent criterion' is based on radial profiles being nearly identical; it would help to show the j* profiles explicitly rather than only in the PDF, since the PDF difference is admittedly a spatial-concentration effect.
  5. [General] Several references are cited in the text but not in the bibliography (e.g., 'Contini et al. 2024c' in Section 4, 'Mayes et al. 2025' in Section 3.4, 'Byun et al. 2025' in Section 7.2). Please check the reference list for completeness and formatting.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor definitional circularity in the preprocessed age statement; central derivation is otherwise self-contained and non-circular.

  1. self definitional [Section 5, Stellar Population Study, age-distribution paragraph following Figure 9]
    "The preprocessed components become unbound before the infall of satellites, and therefore have an inherent lower age limit set by the infall epoch of their host satellites."

    Table 1 defines the preprocessed component as "from satellites but unbound before infall by group environments or neighboring galaxies." A star that is unbound before cluster infall necessarily formed before cluster infall, so the reported old age / lower age limit is an analytic consequence of the selection criterion rather than an independently derived empirical finding. The paper itself labels this relation as "inherent," so the circularity is limited and explicitly acknowledged. The other claimed preprocessed properties (low metallicity, enhanced alpha abundance, and a density profile closer to dark matter) are not imposed by the same definition and remain independent empirical results.

full rationale

The main quantitative claims of the paper—origin fractions of BCG+ICL (stripped 33.4%, disrupted ~22%, in-situ ~33%, preprocessed ~12%), the satellite-majority result (~55%), the dark-matter-like density profile of the preprocessed component, and the orbital dependencies of f_strip—are obtained from direct particle tracking in the NewCluster simulation and are not produced by fitting a parameter to the claimed output or by invoking a load-bearing self-citation. The paper benchmarks f_BCG+ICL (~41%) against external literature values, adding independent context. The time-series membership thresholds (500 Myr window, 10%/90% occurrence) are acknowledged as "somewhat arbitrary," and the preprocessed classification relies in part on visual inspection ("clear evidence of prior unbinding"); these are robustness concerns, not circularity, because changing them would alter the quantitative demographics rather than make a stated prediction equal to an input by construction. The only identifiable circular element is the old age of the preprocessed component: because "preprocessed" is defined as stars unbound before cluster infall, their formation must precede infall, making the reported "inherent lower age limit" a definitional consequence. The paper explicitly uses the word "inherent," so this is not a concealed derivation. No uniqueness theorem, self-citation chain, or renamed/known result is used to force the central conclusions. Overall score 2 reflects this minor, acknowledged definitional coupling while recognizing that the core simulation-based analysis is self-contained.

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

The central claims depend on a chain of classification choices (membership thresholds, window, subjective preprocessed identification) and on the simulation's subgrid physics. No parameter is fitted to a target observational result, but the origin fractions and stellar-population contrasts are conditional on these choices rather than being direct measurements.

free parameters (5)
  • q3 mass-match exponent C
    Equation 7 defines q^(3) = (min/max)^C with 'a tunable exponent'; its value is not specified. It affects merger-tree branch connections and therefore all origin classifications.
  • tight membership threshold = >90%
    Section 3 defines stars present in more than 90% of post-birth snapshots as 'tight' members. This arbitrary threshold controls which stars are considered bound to satellites and hence what becomes BCG+ICL.
  • loose membership threshold = >=10%
    Section 3 defines stars present in more than 10% of their lifetime as 'loose' members; including loose members as satellite stars sets an upper limit on bound stars and changes the BCG+ICL mass fraction.
  • pre-infall membership window = 500 Myr
    Section 3.1 uses a window from 500 Myr before infall to first infall to define initial satellite members. The window length affects how many young stars are classified as stripped versus preprocessed.
  • satellite selection radius = 1.5 R200
    Section 2.4 adopts a 'generous range' of 1.5 R200 for selecting satellites; this determines the sample of 403 surviving and 67 disrupted satellites.
assumptions (5)
  • domain assumption AdaptaHOP density thresholds and hierarchy correctly identify galaxies and subhalos.
    Section 2.2 relies on AdaptaHOP with thresholds of 80 rho_crit for halos and 178 rho_crit for galaxies. All satellite identification and membership tracking depend on these structures being correct.
  • domain assumption Subgrid physics in NewCluster (star formation, SN/AGN feedback, chemical yields) adequately models galaxy evolution.
    Section 2.1 describes prescriptions from Kimm et al. (2017), Kimm & Cen (2014), etc. Stellar ages, metallicities, and stripping behavior depend on these model choices.
  • ad hoc to paper Loose membership approximates gravitational binding without explicit potential calculation.
    Section 3 states: 'Although this threshold is somewhat arbitrary, it approximates binding without explicit potential calculations.' The central classification relies on this approximation.
  • ad hoc to paper Preprocessed stars can be reliably identified from visual orbital signatures.
    Section 3.4 classifies as preprocessed 'stars showing clear evidence of prior unbinding', a subjective criterion illustrated by Figure 6. The distinct properties of the preprocessed component depend on this identification.
  • domain assumption Stellar stripping is numerically converged at NewCluster's resolution.
    The paper cites Lovell et al. (2025) showing that stellar stripping is not converged in TNG resolutions, then argues 68 pc/2e4 Msun minimizes numerical artifacts, but no convergence test is presented for NewCluster itself.

how reviews work

0 comments
Cite this review

Pith. "Pith review of On the Origin of Intracluster Light based on the High-resolution Simulation, NewCluster." pith.science (2026). https://pith.science/paper/RATA3SDK

@misc{pith2026251206098,
  author       = {Pith},
  title        = {Pith review of: On the Origin of Intracluster Light based on the High-resolution Simulation, NewCluster},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RATA3SDK}},
  note         = {Machine review of arXiv:2512.06098}
}
abstract

Intracluster light (ICL) is a key component of galaxy clusters, with the potential to trace their dynamical assembly histories and the underlying dark matter distribution. Despite these prospects, its faint nature makes a consensus on its origin or population properties difficult to achieve, both in observations and simulations. In the hope of finding a breakthrough, we utilize the ongoing high-resolution cluster simulation, NewCluster. By classifying billions of particles in and around the cluster with a rigorous tracking procedure, we find that the majority of the ICL originates from satellites, including surviving and disrupted galaxies. Another notable finding is that the preprocessed component follows the density profile of dark matter better than the other components and has distinctive properties: old age, low metallicity, and enhanced $\alpha$-element abundance. We further investigate the orbital dynamics, and our results demonstrate that the stripped fraction of satellites is primarily determined by the time since infall and the pericenter distance. By linking the demographic, chemical, and orbital properties of ICL stars to their origins, this work proposes a quantitative approach for tracing the assembly history of galaxy clusters from the ICL.

Figures

Figures reproduced from arXiv: 2512.06098 by the authors.

Figure 1
Figure 1. Overview of the NewCluster simulation cen￾tered on the primary cluster halo at z = 0.79. Panel (a) presents a dark matter density map (grayscale), with the virial radii of the two cluster halos (magenta and cyan cir￾cles). The dotted line shows the virial radius (Rvir) from AdaptaHOP, and the dashed line is the R200, which are described in Section 2.2. Panel (b) shows the corresponding r-band surface brightness map … view at source ↗
Figure 2
Figure 2. Schematic diagram of the merger tree. The spiral icons indicate all leaf galaxies connected to the target galaxy (z) across different snapshots, marked with alphabetical IDs. Each line represents a connected branch based on the score (described in the text). The main branch (black) has the same IDs for FIRST and HEAD, indicating a non-fragmented branch. Likewise, the same IDs for TAIL and FINAL mean that this branch… view at source ↗
Figure 3
Figure 3. Orbital tracks of galaxies derived from the merger tree in the NewCluster cluster. Panel (a) shows the comoving-scale tracks in the cosmological box, while panel (b) shows the physical-scale tracks centered on the main cluster at z = 0.79 overlaid on a stellar density map (grayscale). The black dashed circles mark the R200 of the main cluster halo. The red line in panel (a) traces the BCG, and the red marker in pane… view at source ↗
Figures from the paper (8 more)
Figure 5
Figure 5. Figure 5: Top (a)-(g): example of the infall member stars carried by a target surviving satellite galaxy. Panel (a) shows the overall projected smoothed trajectory (green) of the target satellite. Each position is normalized by the R200 of the main cluster progenitors. The purpl…
Figure 6
Figure 6. Figure 6: Example of stellar trajectories in a target satel￾lite galaxy. The x-axis shows the time relative to infall (tinf; black dashed vertical line), and the y-axis is the comov￾ing distance of stars from the satellite center. The black lines denote “stable” stars that remai…
Figure 7
Figure 7. Figure 7: Stellar density maps of classified BCG+ICL stars. Panel (a) presents all BCG+ICL stars within 1.5 R200. Panels (b)-(e) show the subsamples of the BCG+ICL stars. Each subsample is defined as follows in Section 3 and displayed in a different color. Panel (f) shows the ma…
Figure 8
Figure 8. Figure 8: Density profiles of the BCG+ICL stars and dark matter. Panel (a) shows the density profile of dark matter (black dashed line) and all BCG+ICL stars (black solid line). The subsamples of BCG+ICL stars are shown in different col￾ors as indicated in the legend. The gray d…
Figure 9
Figure 9. Figure 9: Physical properties of the BCG+ICL stars. The left panel shows the radial profile, and the right panel shows the probability density function. From top to bottom, the properties shown are stellar age, metallicity, and specific an￾gular momentum. All properties are mass…
Figure 10
Figure 10. Figure 10: Dominant populations in the phase-space dia￾gram. For each phase-space bin, we color the most dominant populations following the text shown. The opacity represents how dominant a population is in each bin; colors range from white (32%) to vivid (100%). The black conto…
Figure 11
Figure 11. Figure 11: Distributions of [α/Fe] and [Fe/H] for the BCG+ICL stars. Panel (c) shows the [α/Fe]-[Fe/H] diagram for each subsample. The contours indicate 1, 1.5, and 2 σ regions, while the shaded background shows the distribution of all BCG+ICL stars. Panels (a) and (b) display t…
Figure 12
Figure 12. Figure 12: Relationships between orbital parameters and the stripped fraction (fstrip). The parameters considered are the time since infall (TSI), the minimum pericenter distance (min(rp)), the number of orbits (Norbit), the orbital anisotropy parameter (β), and the stellar-to-h…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. A Consistent Comparison of Intracluster Light Assembly in Simulations I. Redshift Evolution and Progenitor Galaxies

    astro-ph.GA 2026-06 unverdicted novelty 6.0 of 10

    A homogenized ICL definition applied to Horizon-AGN, TNG100, Gizmo-Simba and Hydrangea yields consistent z=0 fractions of 0.1-0.2 with no significant redshift evolution and dominant contributions from satellites of 10...

Reference graph

Works this paper leans on

124 extracted references · 13 canonical work pages · cited by 1 Pith paper

  1. [1]

    2005, A&A, 429, 39, doi: 10.1051/0004-6361:20041322

    Adami, C., Slezak, E., Durret, F., et al. 2005, A&A, 429, 39, doi: 10.1051/0004-6361:20041322

    doi:10.1051/0004-6361:20041322 2005

  2. [2]

    2016, A&A, 592, A7, doi: 10.1051/0004-6361/201526831

    Adami, C., Pompei, E., Sadibekova, T., et al. 2016, A&A, 592, A7, doi: 10.1051/0004-6361/201526831

    doi:10.1051/0004-6361/201526831 2016

  3. [3]

    L., Bah´ e, Y

    Ahad, S. L., Bah´ e, Y. M., & Hoekstra, H. 2023, MNRAS, 518, 3685, doi: 10.1093/mnras/stac3357 Ald´ as, F., G´ omez, F. A., Vega-Mart ´ ınez, C., Zenteno, A., &

    work page doi:10.1093/mnras/stac3357 2023

  4. [4]

    Carrasco, E. R. 2025, A&A, 699, A313, doi: 10.1051/0004-6361/202451801 Alonso Asensio, I., Dalla Vecchia, C., Bah´ e, Y. M., Barnes, D. J., & Kay, S. T. 2020, MNRAS, 494, 1859, doi: 10.1093/mnras/staa861

    work page doi:10.1051/0004-6361/202451801 2025

  5. [5]

    H., & Evans, N

    An, J. H., & Evans, N. W. 2006, AJ, 131, 782, doi: 10.1086/499305

    work page doi:10.1086/499305 2006

  6. [6]

    2016, A&A, 585, A160, doi: 10.1051/0004-6361/201527399

    Annunziatella, M., Mercurio, A., Biviano, A., et al. 2016, A&A, 585, A160, doi: 10.1051/0004-6361/201527399

    doi:10.1051/0004-6361/201527399 2016

  7. [8]

    J., & Scott, P

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

    arXiv 2009

  8. [10]

    S., Wechsler, R

    Behroozi, P. S., Wechsler, R. H., Wu, H.-Y., et al. 2013, ApJ, 763, 18, doi: 10.1088/0004-637X/763/1/18

    doi:10.1088/0004-637x/763/1/18 2013

Show all 124 references
  1. [11]

    2008, Galactic Dynamics: Second Edition

    Binney, J., & Tremaine, S. 2008, Galactic Dynamics: Second Edition

    2008

  2. [12]

    R., Webb, T

    Bonaventura, N. R., Webb, T. M. A., Muzzin, A., et al. 2017, MNRAS, 469, 1259, doi: 10.1093/mnras/stx722

    2017 doi

  3. [13]

    2001, Nature, 409, 39, doi: 10.1038/409039A010.1038/35051000

    Borgani, S., & Guzzo, L. 2001, Nature, 409, 39, doi: 10.1038/409039A010.1038/35051000

    2001 doi

  4. [14]

    L., Bah´ e, Y

    Brough, S., Ahad, S. L., Bah´ e, Y. M., et al. 2024, MNRAS, 528, 771, doi: 10.1093/mnras/stad3810

    2024 doi

  5. [15]

    J., Martin, G., Pearce, F

    Brown, H. J., Martin, G., Pearce, F. R., et al. 2024, MNRAS, 534, 431, doi: 10.1093/mnras/stae2084

    2024 doi

  6. [16]

    2015, MNRAS, 449, 2353, doi: 10.1093/mnras/stv450

    Burke, C., Hilton, M., & Collins, C. 2015, MNRAS, 449, 2353, doi: 10.1093/mnras/stv450

    2015 doi

  7. [17]

    A., et al

    Butler, J., Martin, G., Hatch, N. A., et al. 2025, MNRAS, 539, 2279, doi: 10.1093/mnras/staf615

    2025 doi

  8. [18]

    K., Scofield, Z

    Byun, G.-H., Jang, J. K., Scofield, Z. P., et al. 2025, arXiv e-prints, arXiv:2508.18374. https://arxiv.org/abs/2508.18374

    2025 arXiv

  9. [19]

    2019, A&A, 621, A96, doi: 10.1051/0004-6361/201834496

    Cadiou, C., Dubois, Y., & Pichon, C. 2019, A&A, 621, A96, doi: 10.1051/0004-6361/201834496

    2019 doi

  10. [20]

    Cha, S., & Jee, M. J. 2023, ApJ, 951, 140, doi: 10.3847/1538-4357/acd111

    2023 doi

  11. [21]

    2003, PASP, 115, 763, doi: 10.1086/376392

    Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392

    2003 doi

  12. [22]

    1943, ApJ, 97, 255, doi: 10.1086/144517 Chandro-G´ omez,´A., Lagos, C

    Chandrasekhar, S. 1943, ApJ, 97, 255, doi: 10.1086/144517 Chandro-G´ omez,´A., Lagos, C. d. P., Power, C., et al. 2025, MNRAS, 539, 776, doi: 10.1093/mnras/staf519

    1943 doi

  13. [23]

    2024, ApJ, 969, 142, doi: 10.3847/1538-4357/ad4a52

    Chun, K., Shin, J., Ko, J., Smith, R., & Yoo, J. 2024, ApJ, 969, 142, doi: 10.3847/1538-4357/ad4a52

    2024 doi

  14. [24]

    2022, ApJ, 925, 103, doi: 10.3847/1538-4357/ac2cbe

    Chun, K., Shin, J., Smith, R., Ko, J., & Yoo, J. 2022, ApJ, 925, 103, doi: 10.3847/1538-4357/ac2cbe

    2022 doi

  15. [25]

    H., et al

    Clowe, D., Bradaˇ c, M., Gonzalez, A. H., et al. 2006, ApJL, 648, L109, doi: 10.1086/508162

    2006 doi

  16. [26]

    2021, Galaxies, 9, 60, doi: 10.3390/galaxies9030060

    Contini, E. 2021, Galaxies, 9, 60, doi: 10.3390/galaxies9030060

    2021 doi

  17. [27]

    Z., & Gu, Q

    Contini, E., Chen, H. Z., & Gu, Q. 2022, ApJ, 928, 99, doi: 10.3847/1538-4357/ac57c4

    2022 doi

  18. [28]

    2014, MNRAS, 437, 3787, doi: 10.1093/mnras/stt2174

    Contini, E., De Lucia, G., Villalobos, ´A., & Borgani, S. 2014, MNRAS, 437, 3787, doi: 10.1093/mnras/stt2174

    2014 doi

  19. [29]

    Contini, E., Han, S., Jeon, S., Rhee, J., & Yi, S. K. 2024a, ApJL, 962, L10, doi: 10.3847/2041-8213/ad21e2

    doi

  20. [30]

    Contini, E., Jeon, S., Rhee, J., Han, S., & Yi, S. K. 2023, ApJ, 958, 72, doi: 10.3847/1538-4357/acfd25

    2023 doi

  21. [31]

    Contini, E., Rhee, J., Han, S., Jeon, S., & Yi, S. K. 2024b, AJ, 167, 7, doi: 10.3847/1538-3881/ad0894

    doi

  22. [32]

    K., & Jeon, S

    Contini, E., Yi, S. K., & Jeon, S. 2024c, arXiv e-prints, arXiv:2404.01560, doi: 10.48550/arXiv.2404.01560

    doi arXiv

  23. [33]

    K., Jeon, S., & Rhee, J

    Contini, E., Yi, S. K., Jeon, S., & Rhee, J. 2024d, ApJS, 274, 41, doi: 10.3847/1538-4365/ad70ac

    doi

  24. [34]

    K., & Kang, X

    Contini, E., Yi, S. K., & Kang, X. 2018, MNRAS, 479, 932, doi: 10.1093/mnras/sty1518 IntraNewCluster Light21 —. 2019, ApJ, 871, 24, doi: 10.3847/1538-4357/aaf41f

    2018 doi

  25. [35]

    2022, MNRAS, 511, 2897, doi: 10.1093/mnras/stac275

    Contreras-Santos, A., Knebe, A., Pearce, F., et al. 2022, MNRAS, 511, 2897, doi: 10.1093/mnras/stac275

    2022 doi

  26. [36]

    2024, A&A, 683, A59, doi: 10.1051/0004-6361/202348474

    Contreras-Santos, A., Knebe, A., Cui, W., et al. 2024, A&A, 683, A59, doi: 10.1051/0004-6361/202348474

    2024 doi

  27. [37]

    2025, arXiv e-prints, arXiv:2508.02837, doi: 10.48550/arXiv.2508.02837 De Lucia, G., & Blaizot, J

    Dacunha, T., Mansfield, P., & Wechsler, R. 2025, arXiv e-prints, arXiv:2508.02837, doi: 10.48550/arXiv.2508.02837 De Lucia, G., & Blaizot, J. 2007, MNRAS, 375, 2, doi: 10.1111/j.1365-2966.2006.11287.x

    2025 doi

  28. [38]

    M., Pascale, M., Frye, B., et al

    Diego, J. M., Pascale, M., Frye, B., et al. 2023, A&A, 679, A159, doi: 10.1051/0004-6361/202345868

    2023 doi

  29. [40]

    M., et al

    Dolag, K., Remus, R.-S., Valenzuela, L. M., et al. 2025, arXiv e-prints, arXiv:2504.01061, doi: 10.48550/arXiv.2504.01061

    2025 doi

  30. [41]

    J., Muriel, H., & Madrid, J

    Donzelli, C. J., Muriel, H., & Madrid, J. P. 2011, ApJS, 195, 15, doi: 10.1088/0067-0049/195/2/15

    2011 doi

  31. [42]

    2014a, MNRAS, 440, 1590, doi: 10.1093/mnras/stu373

    Dubois, Y., Volonteri, M., & Silk, J. 2014a, MNRAS, 440, 1590, doi: 10.1093/mnras/stu373

    doi

  32. [43]

    2014b, MNRAS, 444, 1453, doi: 10.1093/mnras/stu1227

    Dubois, Y., Pichon, C., Welker, C., et al. 2014b, MNRAS, 444, 1453, doi: 10.1093/mnras/stu1227

    doi

  33. [44]

    2021, A&A, 651, A109, doi: 10.1051/0004-6361/202039429

    Dubois, Y., Beckmann, R., Bournaud, F., et al. 2021, A&A, 651, A109, doi: 10.1051/0004-6361/202039429

    2021 doi

  34. [45]

    J., Poulton, R

    Elahi, P. J., Poulton, R. J. J., Tobar, R. J., et al. 2019, PASA, 36, e028, doi: 10.1017/pasa.2019.18

    2019 doi

  35. [46]

    2021, A&A, 649, A38, doi: 10.1051/0004-6361/202038419

    Ellien, A., Slezak, E., Martinet, N., et al. 2021, A&A, 649, A38, doi: 10.1051/0004-6361/202038419

    2021 doi

  36. [47]

    L., et al

    Ellien, A., Montes, M., Ahad, S. L., et al. 2025, A&A, 698, A134, doi: 10.1051/0004-6361/202554460

    2025 doi

  37. [48]

    M., Dell’Antonio, I., & Montes, M

    Englert, A. M., Dell’Antonio, I., & Montes, M. 2025, ApJL, 989, L2, doi: 10.3847/2041-8213/ade8f1 Euclid Collaboration, Bellhouse, C., Golden-Marx, J. B., et al. 2025, A&A, 698, A14, doi: 10.1051/0004-6361/202553887

    2025 doi

  38. [49]

    2004, PASJ, 56, 29, doi: 10.1093/pasj/56.1.29

    Fujita, Y. 2004, PASJ, 56, 29, doi: 10.1093/pasj/56.1.29

    2004 doi

  39. [50]

    2013, MNRAS, 435, 1426, doi: 10.1093/mnras/stt1383

    Genel, S., Vogelsberger, M., Nelson, D., et al. 2013, MNRAS, 435, 1426, doi: 10.1093/mnras/stt1383

    2013 doi

  40. [51]

    1998, MNRAS, 300, 146, doi: 10.1046/j.1365-8711.1998.01918.x

    Ghigna, S., Moore, B., Governato, F., et al. 1998, MNRAS, 300, 146, doi: 10.1046/j.1365-8711.1998.01918.x

    1998

  41. [52]

    B., Zhang, Y., Ogando, R

    Golden-Marx, J. B., Zhang, Y., Ogando, R. L. C., et al. 2025, MNRAS, 538, 622, doi: 10.1093/mnras/staf277

    2025 doi

  42. [53]

    H., George, T., Connor, T., et al

    Gonzalez, A. H., George, T., Connor, T., et al. 2021, MNRAS, 507, 963, doi: 10.1093/mnras/stab2117

    2021 doi

  43. [54]

    H., Zabludoff, A

    Gonzalez, A. H., Zabludoff, A. I., & Zaritsky, D. 2005, ApJ, 618, 195, doi: 10.1086/425896

    2005 doi

  44. [55]

    2020, ApJ, 894, 32, doi: 10.3847/1538-4357/ab845c

    Gu, M., Conroy, C., Law, D., et al. 2020, ApJ, 894, 32, doi: 10.3847/1538-4357/ab845c

    2020 doi

  45. [57]

    2025a, ApJ, 978, 96, doi: 10.3847/1538-4357/ad98f4

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

    doi

  46. [58]

    2018, ApJ, 866, 78, doi: 10.3847/1538-4357/aadfe2

    Han, S., Smith, R., Choi, H., et al. 2018, ApJ, 866, 78, doi: 10.3847/1538-4357/aadfe2

    2018 doi

  47. [59]

    K., Dubois, Y., et al

    Han, S., Yi, S. K., Dubois, Y., et al. 2025b, arXiv e-prints, arXiv:2507.06301, doi: 10.48550/arXiv.2507.06301

    doi

  48. [60]

    Helmi, A., & White, S. D. M. 1999, MNRAS, 307, 495, doi: 10.1046/j.1365-8711.1999.02616.x

    1999

  49. [61]

    1999, ApJS, 125, 439, doi: 10.1086/313278

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

    1999 doi

  50. [62]

    2025, syj3514/YoungTree: YoungTree: multi-snapshot merger tree builder (v1.0.0), v1.0.0, Zenodo, doi: 10.5281/zenodo.17793360 Jim´ enez-Teja, Y., & Dupke, R

    Jeon, S. 2025, syj3514/YoungTree: YoungTree: multi-snapshot merger tree builder (v1.0.0), v1.0.0, Zenodo, doi: 10.5281/zenodo.17793360 Jim´ enez-Teja, Y., & Dupke, R. 2016, ApJ, 820, 49, doi: 10.3847/0004-637X/820/1/49 Jim´ enez-Teja, Y., Rom´ an, J., HyeongHan, K., et al. 202...

    2025 doi

  51. [63]

    Joo, H., & Jee, M. J. 2023, Nature, 613, 37, doi: 10.1038/s41586-022-05396-4

    2023 doi

  52. [64]

    J., Kim, J., et al

    Joo, H., Jee, M. J., Kim, J., et al. 2025, ApJ, 990, 96, doi: 10.3847/1538-4357/adf4d0

    2025 doi

  53. [65]

    L., Choi, H., Wong, O

    Jung, S. L., Choi, H., Wong, O. I., et al. 2018, ApJ, 865, 156, doi: 10.3847/1538-4357/aadda2

    2018 doi

  54. [66]

    2014, ApJ, 788, 121, doi: 10.1088/0004-637X/788/2/121

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

    2014 doi

  55. [67]

    2017, MNRAS, 466, 4826, doi: 10.1093/mnras/stx052

    Kimm, T., Katz, H., Haehnelt, M., et al. 2017, MNRAS, 466, 4826, doi: 10.1093/mnras/stx052

    2017 doi

  56. [68]

    C., Brough, S., Dolag, K., et al

    Kimmig, L. C., Brough, S., Dolag, K., et al. 2025, A&A, 700, A95, doi: 10.1051/0004-6361/202554777

    2025 doi

  57. [69]

    A., Montes, M., et al

    Kluge, M., Hatch, N. A., Montes, M., et al. 2025, A&A, 697, A13, doi: 10.1051/0004-6361/202450772

    2025 doi

  58. [70]

    Ko, J., & Jee, M. J. 2018, ApJ, 862, 95, doi: 10.3847/1538-4357/aacbda

    2018 doi

  59. [71]

    2006, ApJ, 653, 1145, doi: 10.1086/508914

    Ohkubo, T. 2006, ApJ, 653, 1145, doi: 10.1086/508914

    2006 doi

  60. [72]

    Kochanek, C. S. 2006, in Saas-Fee Advanced Course 33: Gravitational Lensing: Strong, Weak and Micro, ed. G. Meylan, P. Jetzer, P. North, P. Schneider, C. S. Kochanek, & J. Wambsganss, 91–268

    2006

  61. [73]

    M., Dunkley, J., et al

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

    2011 doi

  62. [74]

    V., & Borgani, S

    Kravtsov, A. V., & Borgani, S. 2012, ARA&A, 50, 353, doi: 10.1146/annurev-astro-081811-125502

    2012 doi

  63. [75]

    V., Vikhlinin, A

    Kravtsov, A. V., Vikhlinin, A. A., & Meshcheryakov, A. V. 2018, Astronomy Letters, 44, 8, doi: 10.1134/S1063773717120015 22Seyoung Jeon et al

    2018 doi

  64. [76]

    R., Pillepich, A., Engler, C., et al

    Lovell, M. R., Pillepich, A., Engler, C., et al. 2025, arXiv e-prints, arXiv:2509.07078, doi: 10.48550/arXiv.2509.07078

    2025 doi

  65. [77]

    2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    2014 doi

  66. [78]

    2000, A&A, 361, 159, doi: 10.48550/arXiv.astro-ph/0006405

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

    2000 doi arXiv

  67. [79]

    2025, MNRAS, 543, 4020, doi: 10.1093/mnras/staf1717

    Rodriguez-Gomez, V., & Cervantes Sodi, B. 2025, MNRAS, 543, 4020, doi: 10.1093/mnras/staf1717

    2025 doi

  68. [80]

    Martin, G., Kaviraj, S., Devriendt, J. E. G., et al. 2017, MNRAS, 472, L50, doi: 10.1093/mnrasl/slx136

    2017 doi

  69. [81]

    R., Hatch, N

    Martin, G., Pearce, F. R., Hatch, N. A., et al. 2024, MNRAS, 535, 2375, doi: 10.1093/mnras/stae2488

    2024 doi

  70. [82]

    A., Kaviraj, S., et al

    Martin, G., Jackson, R. A., Kaviraj, S., et al. 2021, MNRAS, 500, 4937, doi: 10.1093/mnras/staa3443

    2021 doi

  71. [83]

    E., Spavone, M., et al

    Martin, G., Bazkiaei, A. E., Spavone, M., et al. 2022, MNRAS, 513, 1459, doi: 10.1093/mnras/stac1003 Mart ´ ınez-Lombilla, C., Brough, S., Montes, M., et al. 2023, MNRAS, 518, 1195, doi: 10.1093/mnras/stac3119

    2022 doi

  72. [84]

    S., Sarrouh, G

    Martis, N. S., Sarrouh, G. T. E., Willott, C. J., et al. 2024, ApJ, 975, 76, doi: 10.3847/1538-4357/ad7735

    2024 doi

  73. [85]

    J., G´ omez, F

    Mayes, R. J., G´ omez, F. A., & Monachesi, A. 2025, arXiv e-prints, arXiv:2506.16645, doi: 10.48550/arXiv.2506.16645

    2025 doi arXiv

  74. [86]

    2003, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0305512

    Mihos, C. 2003, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0305512

    2003 doi arXiv

  75. [87]

    Mihos, J. C. 2016, in IAU Symposium, Vol. 317, The General Assembly of Galaxy Halos: Structure, Origin and Evolution, ed. A. Bragaglia, M. Arnaboldi, M. Rejkuba, & D. Romano, 27–34, doi: 10.1017/S1743921315006857

    2016 doi

  76. [88]

    C., Harding, P., Feldmeier, J., & Morrison, H

    Mihos, J. C., Harding, P., Feldmeier, J., & Morrison, H. 2005, ApJL, 631, L41, doi: 10.1086/497030

    2005 doi

  77. [89]

    2006, ApJL, 652, L89, doi: 10.1086/510236

    Monaco, P., Murante, G., Borgani, S., & Fontanot, F. 2006, ApJL, 652, L89, doi: 10.1086/510236

    2006 doi

  78. [90]

    2025, MNRAS, 537, 3954, doi: 10.1093/mnras/staf271

    Rodriguez-Gomez, V., Manuwal, A., & Cervantes-Sodi, B. 2025, MNRAS, 537, 3954, doi: 10.1093/mnras/staf271

    2025 doi

  79. [91]

    2023, MNRAS, 521, 800, doi: 10.1093/mnras/stad586

    Montenegro-Taborda, D., Rodriguez-Gomez, V., Pillepich, A., et al. 2023, MNRAS, 521, 800, doi: 10.1093/mnras/stad586

    2023 doi

  80. [92]

    2022, Nature Astronomy, 6, 308, doi: 10.1038/s41550-022-01616-z

    Montes, M. 2022, Nature Astronomy, 6, 308, doi: 10.1038/s41550-022-01616-z

    2022 doi

  81. [93]

    S., & Santucci, G

    Montes, M., Brough, S., Owers, M. S., & Santucci, G. 2021, ApJ, 910, 45, doi: 10.3847/1538-4357/abddb6

    2021 doi

  82. [94]

    2014, ApJ, 794, 137, doi: 10.1088/0004-637X/794/2/137 —

    Montes, M., & Trujillo, I. 2014, ApJ, 794, 137, doi: 10.1088/0004-637X/794/2/137 —. 2018, MNRAS, 474, 917, doi: 10.1093/mnras/stx2847 —. 2019, MNRAS, 482, 2838, doi: 10.1093/mnras/sty2858 —. 2022, ApJL, 940, L51, doi: 10.3847/2041-8213/ac98c5

    2014 doi

  83. [95]

    2019, MNRAS, 483, 3390, doi: 10.1093/mnras/sty3306

    Mostoghiu, R., Knebe, A., Cui, W., et al. 2019, MNRAS, 483, 3390, doi: 10.1093/mnras/sty3306

    2019 doi

  84. [96]

    2007, MNRAS, 377, 2, doi: 10.1111/j.1365-2966.2007.11568.x

    Murante, G., Giovalli, M., Gerhard, O., et al. 2007, MNRAS, 377, 2, doi: 10.1111/j.1365-2966.2007.11568.x

    2007

  85. [97]

    2004, ApJL, 607, L83, doi: 10.1086/421348

    Murante, G., Arnaboldi, M., Gerhard, O., et al. 2004, ApJL, 607, L83, doi: 10.1086/421348

    2004 doi

  86. [98]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493, doi: 10.1086/304888

    1997 doi

  87. [99]

    2024, A&A, 686, A157, doi: 10.1051/0004-6361/202348608

    Nelson, D., Pillepich, A., Ayromlou, M., et al. 2024, A&A, 686, A157, doi: 10.1051/0004-6361/202348608

    2024 doi

  88. [100]

    2019, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x

    Nelson, D., Springel, V., Pillepich, A., et al. 2019, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x

    2019 doi

  89. [101]

    H., et al

    Oh, S., Kim, K., Lee, J. H., et al. 2018, ApJS, 237, 14, doi: 10.3847/1538-4365/aacd47

    2018 doi

  90. [102]

    2022, ApJ, 937, 15, doi: 10.3847/1538-4357/ac85b5

    Park, C., Lee, J., Kim, J., et al. 2022, ApJ, 937, 15, doi: 10.3847/1538-4357/ac85b5

    2022 doi

  91. [103]

    J., Santos, D

    Pearson, W. J., Santos, D. J. D., Goto, T., et al. 2024, A&A, 686, A94, doi: 10.1051/0004-6361/202349034

    2024 doi

  92. [104]

    2019, MNRAS, 486, 101, doi: 10.1093/mnras/stz822

    Pfister, H., Volonteri, M., Dubois, Y., Dotti, M., & Colpi, M. 2019, MNRAS, 486, 101, doi: 10.1093/mnras/stz822

    2019 doi

  93. [105]

    2018, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    Pillepich, A., Nelson, D., Hernquist, L., et al. 2018, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    2018 doi

  94. [106]

    B., Mutch, S

    Poole, G. B., Mutch, S. J., Croton, D. J., & Wyithe, S. 2017, MNRAS, 472, 3659, doi: 10.1093/mnras/stx2233

    2017 doi

  95. [107]

    2010, MNRAS, 406, 936, doi: 10.1111/j.1365-2966.2010.16786.x

    Puchwein, E., Springel, V., Sijacki, D., & Dolag, K. 2010, MNRAS, 406, 936, doi: 10.1111/j.1365-2966.2010.16786.x

    2010

  96. [108]

    L., et al

    Reina-Campos, M., Trujillo-Gomez, S., Pfeffer, J. L., et al. 2022, arXiv e-prints, arXiv:2204.11861, doi: 10.48550/arXiv.2204.11861

    2022 doi arXiv

  97. [109]

    2017, ApJ, 843, 128, doi: 10.3847/1538-4357/aa6d6c

    Rhee, J., Smith, R., Choi, H., et al. 2017, ApJ, 843, 128, doi: 10.3847/1538-4357/aa6d6c

    2017 doi

  98. [110]

    S., Mihos, J

    Rudick, C. S., Mihos, J. C., Frey, L. H., & McBride, C. K. 2009, ApJ, 699, 1518, doi: 10.1088/0004-637X/699/2/1518

    2009 doi

  99. [111]

    S., Mihos, J

    Rudick, C. S., Mihos, J. C., & McBride, C. K. 2011, ApJ, 732, 48, doi: 10.1088/0004-637X/732/1/48

    2011 doi

  100. [112]

    Sampaio-Santos, H., Zhang, Y., Ogando, R. L. C., et al. 2021, MNRAS, 501, 1300, doi: 10.1093/mnras/staa3680

    2021 doi

  101. [113]

    1992, A&AS, 96, 269

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

    1992

  102. [114]

    2016, ApJ, 833, 109, doi: 10.3847/1538-4357/833/1/109

    Smith, R., Choi, H., Lee, J., et al. 2016, ApJ, 833, 109, doi: 10.3847/1538-4357/833/1/109

    2016 doi

  103. [115]

    2022, A&A, 663, A135, doi: 10.1051/0004-6361/202243290

    Spavone, M., Iodice, E., D’Ago, G., et al. 2022, A&A, 663, A135, doi: 10.1051/0004-6361/202243290

    2022 doi

  104. [116]

    R., et al

    Srisawat, C., Knebe, A., Pearce, F. R., et al. 2013, MNRAS, 436, 150, doi: 10.1093/mnras/stt1545

    2013 doi

  105. [117]

    E., & Babul, A

    Taylor, J. E., & Babul, A. 2001, ApJ, 559, 716, doi: 10.1086/322276 IntraNewCluster Light23

    2001 doi

  106. [118]

    2002, A&A, 385, 337, doi: 10.1051/0004-6361:20011817

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

    2002 doi

  107. [119]

    Tinsley, B. M. 1979, ApJ, 229, 1046, doi: 10.1086/157039

    1979 doi

  108. [120]

    R., Ricarte, A., et al

    Tremmel, M., Quinn, T. R., Ricarte, A., et al. 2019, MNRAS, 483, 3336, doi: 10.1093/mnras/sty3336

    2019 doi

  109. [121]

    2016, ApJ, 823, 123, doi: 10.3847/0004-637X/823/2/123

    Trujillo, I., & Fliri, J. 2016, ApJ, 823, 123, doi: 10.3847/0004-637X/823/2/123

    2016 doi

  110. [122]

    2009, A&A, 506, 647, doi: 10.1051/0004-6361/200911787

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

    2009 doi

  111. [123]

    2004, MNRAS, 355, 159, doi: 10.1111/j.1365-2966.2004.08312.x

    Willman, B., Governato, F., Wadsley, J., & Quinn, T. 2004, MNRAS, 355, 159, doi: 10.1111/j.1365-2966.2004.08312.x

    2004

  112. [124]

    S., et al

    Yoo, J., Shin, J., Hwang, H. S., et al. 2025, ApJ, 988, 229, doi: 10.3847/1538-4357/ade66f

    2025 doi

  113. [125]

    G., et al

    Yoo, J., Ko, J., Sabiu, C. G., et al. 2022, ApJS, 261, 28, doi: 10.3847/1538-4365/ac7142

    2022 doi

  114. [126]

    G., et al

    Yoo, J., Park, C., Sabiu, C. G., et al. 2024, ApJ, 965, 145, doi: 10.3847/1538-4357/ad2df8

    2024 doi

  115. [127]

    2005, MNRAS, 358, 949, doi: 10.1111/j.1365-2966.2005.08817.x

    Brinkmann, J. 2005, MNRAS, 358, 949, doi: 10.1111/j.1365-2966.2005.08817.x

    2005

  116. [128]

    1937, ApJ, 86, 217, doi: 10.1086/143864 —

    Zwicky, F. 1937, ApJ, 86, 217, doi: 10.1086/143864 —. 1951, PASP, 63, 61, doi: 10.1086/126318

    1937 doi

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.