pith. sign in

arxiv: 2605.24097 · v1 · pith:6IXSCUCXnew · submitted 2026-05-22 · 🌌 astro-ph.GA · astro-ph.CO

The AGORA High-resolution Galaxy Simulations Comparison Project. XI: Solving the Non-Spherical Morphology and Evolution of Dark Matter Halos with Haskap Pie

Kirk S. S. Barrow , Thinh Huu Nguyen , Santi Roca-F\`abrega , Ji-hoon Kim , Varun Satish , Kentaro Nagamine , Saulius Matusaitis , Edu\'ard Illes
show 21 more authors
Ram\'on Rodr\'iguez-Cardoso Minyong Jung Hyeonyong Kim Anna Genina Pablo Granizo Alessandro Lupi Johnny W. Powell H\'ector Vel\'azquez Tom Abel Oscar Agertz Renyue Cen Daniel Ceverino Boon Kiat Oh Yuri Oku Joel R. Primack Thomas R. Quinn Yves Revaz Alvaro Segovia-Otero Ikkoh Shimizu Edward Skrabacz Romain Teyssier
This is my paper

Pith reviewed 2026-06-30 15:22 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO
keywords dark matter halosnon-spherical morphologygalaxy simulationshalo mergershalo spinoverdensityAGORA projecthalo tracking
0
0 comments X

The pith

A bound-particle method treats dark matter halos as non-spherical and tracks their merger-driven shape changes across simulation codes.

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

The paper introduces a halo solving and tracking procedure based on bound particle searching that identifies and follows dark matter halos without assuming spherical symmetry. Applied to the AGORA hydrodynamic simulations, the method shows that morphological measures such as axis ratios respond strongly to high mass ratio mergers, while most secular trends in spin, overdensity, and dynamical quantities remain consistent across codes. Differences between codes trace mainly to variations in merger timing and pre-merger dynamical states. Halo spin and the semi-major to semi-minor axis ratio both reach maxima between redshift 4 and 2 before declining at lower redshift, and overdensity diverges for low-mass halos at late times. A reader would care because dropping the spherical assumption alters how halo morphology couples to galaxy assembly and merger history.

Core claim

We introduce a halo solving and tracking procedure that intrinsically treats dark matter halos as non-spherical objects by leveraging the bound particle searching techniques used in Haskap Pie. Several morphological and shape measures prove very responsive to high mass ratio mergers. The greatest differences between simulation codes arise from timing discrepancies and the dynamical state of the halos prior to mergers. Most other quantities remain similar across codes, including secular and redshift-dependent trends in dynamical quantities that depart from the Virial Theorem. Halo spin and the semi-major to semi-minor axis ratio peak between 4 > z > 2 before declining at low redshift, while h

What carries the argument

Haskap Pie bound-particle searching procedure for identifying and tracking non-spherical halo boundaries and dynamical states

If this is right

  • Morphological and shape measures change markedly during high mass ratio mergers.
  • Halo spin and the semi-major to semi-minor axis ratio reach a maximum between redshift 4 and 2 then decline toward z=0.
  • Halo overdensity depends on both halo mass and redshift, with low-mass halos diverging at low redshift.
  • Most dynamical quantities evolve similarly across different simulation codes once merger timing is accounted for.
  • Trends in overdensity and halo mass depart from expectations under the Virial Theorem.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Adopting the non-spherical tracking method on larger simulation volumes could revise inferred merger rates for low-mass galaxies at late times.
  • The reported spin peak at 4 > z > 2 offers a concrete prediction that weak-lensing surveys could test by stacking halo shapes at those redshifts.
  • If the method is extended to baryonic particles, it might reveal how gas inflows during mergers alter the non-spherical dark-matter response.
  • Code-to-code differences in merger timing highlight the need for synchronized initial conditions when comparing morphology statistics.

Load-bearing premise

The bound particle searching techniques used in Haskap Pie accurately capture non-spherical halo boundaries and dynamical states across multiple simulation codes without introducing code-specific biases in morphology measures.

What would settle it

Demonstrating that the same merger responses and redshift trends in axis ratio and spin appear when the identical simulation outputs are analyzed with conventional spherical-overdensity halo finders would falsify the claim that non-spherical treatment is required.

Figures

Figures reproduced from arXiv: 2605.24097 by Alessandro Lupi, Alvaro Segovia-Otero, Anna Genina, Boon Kiat Oh, Daniel Ceverino, Edu\'ard Illes, Edward Skrabacz, H\'ector Vel\'azquez, Hyeonyong Kim, Ikkoh Shimizu, Ji-Hoon Kim, Joel R. Primack, Johnny W. Powell, Kentaro Nagamine, Kirk S. S. Barrow, Minyong Jung, Oscar Agertz, Pablo Granizo, Ram\'on Rodr\'iguez-Cardoso, Renyue Cen, Romain Teyssier, Santi Roca-F\`abrega, Saulius Matusaitis, Thinh Huu Nguyen, Thomas R. Quinn, Tom Abel, Varun Satish, Yuri Oku, Yves Revaz.

Figure 1
Figure 1. Figure 1: Comparison of corresponding results on our test simulation using spherical (left) and non-spherical (right) halo￾finding techniques. Particles that are bound to the halo are plotted with the same color as the halo radius. Colors are consistent by halo but rotate through the pallette between halos. Δhalo = Δc Mhalo < Mbound Class I Δhalo ≥ Δc Mhalo = Mbound Class II Δhalo > Δc Mhalo < Mbound Class III Halo … view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the three classes of halos described in Sec. 2.2 showing the relationship between convex hull halos and the total volume encompassing self-bound par￾ticles. Class I halos are classical halos and Class II halos have higher bound overdensities than the virial overdensi￾ties. Class III halos are halos that are recorded at higher overdensity than the virial overdensity target but are also a sub… view at source ↗
Figure 3
Figure 3. Figure 3: Left: The edges and vertices of the convex hull (green) enclosing the bound particles (red) that meet the virial overdensity threshold plotted with the semi-major (black), semi-minor (blue), and intermediate axis (yellow). Right: A 2-D projection of the same hull (green) plotted with a projection of the particles (blue) along with various definitions of center: the center of energy of all the bound particl… view at source ↗
Figure 4
Figure 4. Figure 4: Results from the last timestep of the CosmoRun￾ART simulation (z ∼ 0) showing quantities related to over￾density, radius, and bounded mass. Each scatter point is a single halo. Top: The overdensity versus particle sam￾ple count relationship showing an asymptotic trend at low counts. Middle: The relationship between halo radius and mass showing the total bound mass (orange) and the halo mass within the conv… view at source ↗
Figure 5
Figure 5. Figure 5: Standard deviation of scaled residuals (r − r¯)/r¯ from a smooth trend, r¯, for each measure of radius reported. The smooth trend is generated using a Savgol filter with a window size of 10 and a polynomial order of 3. Each red scatter point represents the standard deviation of all residuals for one halo. The interquartile range and median values of all halos tracks in the CosmoRun-ART simulation are given… view at source ↗
Figure 6
Figure 6. Figure 6: Trends in halo morphological features of the last timestep of the CosmoRun-ART simulation, where each scatter point represents a halo all colored by the semi-major axis to semi-minor axis ratio a/b. Each of the measures is as described in Sec. 2.4 [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The evolution of three shape parameters for the main halo in each of the nine simulations. Left plots show early times, which include a high mass ratio major merger. Right plots show the late-time evolution. Top: a/b ratio showing peaks associated with mergers. Middle: sphere filling fraction (Eq. 4) showing a more impulse-like responsiveness to minor mergers. Bottom, the offset between the center of energ… view at source ↗
Figure 8
Figure 8. Figure 8: Trends connecting the a/b ratio of the high mass major merger at ∼1.1 Gyr to the state of the infalling halo at 1 Gyr. Top: the time of the peak in a/b versus the closing time (distance divided by radial closing velocity) showing a roughly positive correlation. Bottom: The peak value of a/b versus the merger mass ratio also shows a positive correlation with CHANGA’s realization as an outlier. these mergers… view at source ↗
Figure 9
Figure 9. Figure 9: Spin (λ, top), change in spin (∆λ, center), and change in halo mass scaled by halo mass (∆Mh/Mh, bottom) for the main halo as a function of simulation time. Smoothed values are opaque and the full trend is translucent [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Top: halo mass to bound mass ratios (right axis, solid line) for the main halo versus time, as well as halo matter overdensity versus time (left axis, translucent). The main halo tracks the virial overdensity (black dot-dashed line) as designed. Also plotted is 200c as a dashed line. Bottom: the mean trend of both variables for halos with masses greater than 8 × 108 and between 0.5 and 3 virial radii from… view at source ↗
Figure 11
Figure 11. Figure 11: Overdensity versus halo mass within the refined region for each of the CosmoRun simulations at fixed redshifts. Each data value is the mean of a mass bin. Lines are used for mass bins with greater than five halos, and scatter points are used for mass bins with five or fewer halos. reach z = 0. Since the average halo mass to bound mass ratio approaches unity faster for this sample than for other halos in t… view at source ↗
Figure 12
Figure 12. Figure 12: Top: A comparison of escape velocities at the virial radius of halos of different masses (solid-colored lines) to one third the dark matter velocity dispersion (thick brown line) in the CosmoRun GADGET-4 simulation refined region. Bottom: Also plotted as translucent scatter points are the overdensities of halos with masses near the corresponding values used in the escape velocity calculation. The x-axis s… view at source ↗
Figure 13
Figure 13. Figure 13: Oblateness times (1-Sphericity), O(1 − Ψ), for all halos in the refined region of each simulation at z = 2 as a function of mass (solid black line and filled circles) compared to O(1 − Ψ)/2 (dashed line and open circles), which represents neutral oblateness. Each simulation tends towards values above the neutral oblateness line and thus is biased towards oblate orientations. In this work, this means that … view at source ↗
Figure 14
Figure 14. Figure 14: The spin versus halo mass versus redshift trend plotted in the same manner as [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Key dynamical and shape trends for halos in all simulations between 5 × 108 and 109 M⊙. In the top right plot is the sample size as a function of redshift (solid line) and the mean formation redshift of the halos in the sample as a function of redshift (dashed line). For all other plots, we show the mean values of the indicated quantities across the sample. Smoothed values are opaque and the full trend is… view at source ↗
Figure 16
Figure 16. Figure 16: Spin distribution constructed from the combined sample of 5 × 108 and 109 M⊙ halos from simulations that have data available for the given redshifts. The data show a skewed log-normal relationship with occasional bimodal peaks due to the combined sample. Circular points on the bottom show the unweighted mean of the distribution for the corresponding redshift and have a downward trend to lower spin from z … view at source ↗
Figure 17
Figure 17. Figure 17: The region around the main halo at 800 Gyr in the CosmoRun-CHANGA (left) and GIZMO (right) simulations showing the complex assembly of halos that make up the infalling halo for the major merger at 1.1 Gyr. This cluster of CHANGA halos loses its definition just prior to the merger, which causes the outlier mass ratio seen in [PITH_FULL_IMAGE:figures/full_fig_p026_17.png] view at source ↗
read the original abstract

We introduce a halo solving and tracking procedure that intrinsically treats dark matter halos as non-spherical objects by leveraging the bound particle searching techniques used in Haskap Pie. The AGORA Collaboration's hydrodynamic simulation CosmoRun}project provides a useful laboratory to explore trends in dark matter halo morphology that are revealed by our new procedure in the context of any dispersions or similarities between the codes. We find that several morphological and shape measures were very responsive to high mass ratio mergers. The greatest difference in these measures between the simulation codes were related to timing discrepancies and the dynamical state of the halos prior to the mergers. Most other quantities were similar across codes, including several secular and redshift-dependent trends in various dynamical quantities that showed a departure from Virial Theorem (e.g., overdensity and halo mass). We find that halo spin and the ratio between the semi-major and the semi-minor axis peaked at 4>z>2 before declining at low redshift. Also, halo overdensity is both mass-dependent and redshift-dependent, diverging for low mass halos at low redshift. Our method contributes a new perspective on these trends that have not been fully replicated in other works due to our emphasis on fundamentally non-spherical halos and measures of morphology that correspondingly do not assume spherical symmetry.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 1 minor

Summary. The manuscript introduces Haskap Pie, a halo solving and tracking procedure that treats dark matter halos as intrinsically non-spherical objects via bound-particle searching techniques. Applied to the AGORA CosmoRun suite of hydrodynamic simulations, the work reports that several morphological and shape measures respond strongly to high mass-ratio mergers, with the largest inter-code differences arising from merger timing and pre-merger dynamical state. Secular trends include halo spin and the semi-major to semi-minor axis ratio peaking between 4 > z > 2 before declining at low redshift, while halo overdensity is both mass- and redshift-dependent, diverging for low-mass halos at low z. The authors argue that their non-spherical measures provide a new perspective not fully replicated in prior work that assumes spherical symmetry.

Significance. If the Haskap Pie finder is demonstrated to be free of code-specific biases, the results would offer a useful addition to the AGORA code-comparison literature by quantifying how merger history and dynamical state drive non-spherical halo morphology. The explicit focus on departures from the virial theorem in overdensity and mass evolution, together with the spin and axis-ratio trends, could inform interpretations of halo assembly in both simulations and observations.

major comments (1)
  1. [Abstract] Abstract: The claim that 'the greatest difference in these measures between the simulation codes were related to timing discrepancies' presupposes that Haskap Pie’s bound-particle search returns statistically equivalent boundary and shape statistics when applied to snapshots from ART, ENZO, GADGET, etc., at matched resolution. No cross-code validation test of the iterative unbinding or potential estimation step is described, leaving open the possibility that reported morphology trends partly reflect code-specific differences in density estimation or softening rather than physical differences.
minor comments (1)
  1. [Abstract] Abstract: 'CosmoRun}project' contains an apparent typesetting artifact and should read 'CosmoRun project'.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. The single major comment raises a valid methodological concern about cross-code consistency of the halo finder, which we address below. We will incorporate revisions to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'the greatest difference in these measures between the simulation codes were related to timing discrepancies' presupposes that Haskap Pie’s bound-particle search returns statistically equivalent boundary and shape statistics when applied to snapshots from ART, ENZO, GADGET, etc., at matched resolution. No cross-code validation test of the iterative unbinding or potential estimation step is described, leaving open the possibility that reported morphology trends partly reflect code-specific differences in density estimation or softening rather than physical differences.

    Authors: We thank the referee for highlighting this important point regarding potential finder-induced biases. The AGORA CosmoRun suite was explicitly constructed with identical initial conditions, particle masses, and force resolutions across codes (ART, ENZO, GADGET, etc.) to enable direct inter-code comparisons of the resulting halo properties. Haskap Pie applies an identical bound-particle search, iterative unbinding, and potential estimation procedure to every snapshot. While the original manuscript did not include a dedicated cross-code validation test of these steps, the reported similarity across codes in most morphological measures (apart from merger timing) provides supporting evidence that the finder behaves consistently. To directly resolve the concern, we will add a new validation subsection to the Methods section. This will apply Haskap Pie to a set of temporally matched snapshots from two representative codes and demonstrate that the resulting boundary definitions, axis ratios, and spin parameters agree statistically within the level of numerical differences inherent to the codes. We will also revise the abstract to note that the timing-related differences are identified after uniform application of the finder. These changes will appear in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: results are direct simulation outputs, not fitted or self-defined quantities

full rationale

The paper introduces a halo tracking procedure based on Haskap Pie's bound-particle search and applies it to AGORA CosmoRun outputs across codes. All reported trends (merger responsiveness, spin evolution, overdensity mass/redshift dependence) are presented as direct measurements from the simulations. No equations, fitted parameters, or predictions are shown that reduce to the inputs by construction. No self-citation chains or uniqueness theorems are invoked to justify the central claims. The derivation chain is self-contained against external simulation benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, invented entities, or detailed axioms beyond the domain assumption of comparing to Virial Theorem expectations.

axioms (1)
  • domain assumption Departure from Virial Theorem expectations is a meaningful diagnostic for halo dynamical state
    Abstract states several secular trends show departure from Virial Theorem (e.g., overdensity and halo mass).

pith-pipeline@v0.9.1-grok · 5924 in / 1173 out tokens · 34588 ms · 2026-06-30T15:22:35.938922+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

64 extracted references · 60 canonical work pages · 2 internal anchors

  1. [1]

    A., Primack, J

    Allgood, B., Flores, R. A., Primack, J. R., et al. 2006, MNRAS, 367, 1781, doi: 10.1111/j.1365-2966.2006.10094.x

    work page doi:10.1111/j.1365-2966.2006.10094.x 2006

  2. [2]

    2013, JCAP, 2013, 009, doi: 10.1088/1475-7516/2013/04/009 A vila-Reese, V., Colín, P., Gottl¨ ober, S., Firmani, C., &

    Anderhalden, D., & Diemand, J. 2013, JCAP, 2013, 009, doi: 10.1088/1475-7516/2013/04/009 A vila-Reese, V., Colín, P., Gottl¨ ober, S., Firmani, C., &

    work page doi:10.1088/1475-7516/2013/04/009 2013

  3. [3]

    2005, ApJ, 634, 51, doi: 10.1086/491726

    Maulbetsch, C. 2005, ApJ, 634, 51, doi: 10.1086/491726

    work page doi:10.1086/491726 2005

  4. [4]

    2005, ApJ, 627, 647, doi: 10.1086/430397

    Bailin, J., & Steinmetz, M. 2005, ApJ, 627, 647, doi: 10.1086/430397

    work page doi:10.1086/430397 2005

  5. [5]

    1987, ApJ, 319, 575, doi: 10.1086/165480

    Barnes, J., & Efstathiou, G. 1987, ApJ, 319, 575, doi: 10.1086/165480

    work page doi:10.1086/165480 1987

  6. [6]

    Barrow, K. S. S., Nguyễn, T. H., & Skrabacz, E. 2026, ApJ, 999, 72, doi: 10.3847/1538-4357/ae2eb4

    work page doi:10.3847/1538-4357/ae2eb4 2026

  7. [7]

    The Rockstar Phase-Space Temporal Halo Finder and the Velocity Offsets of Cluster Cores

    Behroozi, P. S., Wechsler, R. H., & Wu, H.-Y. 2012, The Astrophysical Journal, 762, 109, doi: 10.1088/0004-637X/762/2/109

    work page internal anchor Pith review doi:10.1088/0004-637x/762/2/109 2012

  8. [8]

    Celotti and G

    Bett, P., Eke, V., Frenk, C. S., et al. 2007, MNRAS, 376, 215, doi: 10.1111/j.1365-2966.2007.11432.x

    work page doi:10.1111/j.1365-2966.2007.11432.x 2007

  9. [9]

    2015, MNRAS, 449, 3171, doi: 10.1093/mnras/stv417

    Bonamigo, M., Despali, G., Limousin, M., et al. 2015, MNRAS, 449, 3171, doi: 10.1093/mnras/stv417

    work page doi:10.1093/mnras/stv417 2015

  10. [10]

    Statistical Properties of X-ray Clusters: Analytic and Numerical Comparisons

    Bryan, G. L., & Norman, M. L. 1998, ApJ, 495, 80, doi: 10.1086/305262

    work page internal anchor Pith review doi:10.1086/305262 1998

  11. [11]

    L., Norman, M

    Bryan, G. L., Norman, M. L., O’Shea, B. W., et al. 2014, ApJS, 211, 19, doi: 10.1088/0067-0049/211/2/19

    work page doi:10.1088/0067-0049/211/2/19 2014

  12. [12]

    Bullock, J. S. 2002, in The Shapes of Galaxies and their Dark Halos, ed. P. Natarajan, 109–113, doi: 10.1142/9789812778017_0018

    work page doi:10.1142/9789812778017_0018 2002

  13. [13]

    S., Dekel, A., Kolatt, T

    Bullock, J. S., Dekel, A., Kolatt, T. S., et al. 2001, ApJ, 555, 240, doi: 10.1086/321477

    work page doi:10.1086/321477 2001

  14. [14]

    , keywords =

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

    work page doi:10.1086/163168 1985

  15. [15]

    2006, ApJ, 649, 1, doi: 10.1086/506377

    Diemand, J., Kuhlen, M., & Madau, P. 2006, ApJ, 649, 1, doi: 10.1086/506377

    work page doi:10.1086/506377 2006

  16. [16]

    2005, Nature, 433, 389, doi: 10.1038/nature03270

    Diemand, J., Moore, B., & Stadel, J. 2005, Nature, 433, 389, doi: 10.1038/nature03270

    work page doi:10.1038/nature03270 2005

  17. [17]

    2024, MNRAS, 533, 3811, doi: 10.1093/mnras/stae2007

    Diemer, B., Behroozi, P., & Mansfield, P. 2024, MNRAS, 533, 3811, doi: 10.1093/mnras/stae2007

    work page doi:10.1093/mnras/stae2007 2024

  18. [18]

    M., & Sanders, J

    Dillamore, A. M., & Sanders, J. L. 2026, MNRAS, doi: 10.1093/mnras/stag226

    work page doi:10.1093/mnras/stag226 2026

  19. [19]

    T., Zhang, B., et al

    Dolag, K., Borgani, S., Murante, G., & Springel, V. 2009, MNRAS, 399, 497, doi: 10.1111/j.1365-2966.2009.15034.x

    work page doi:10.1111/j.1365-2966.2009.15034.x 2009

  20. [20]

    1983, IEEE Transactions on Information Theory, 29, 551, doi: 10.1109/TIT.1983.1056714

    Edelsbrunner, H., Kirkpatrick, D., & Seidel, R. 1983, IEEE Transactions on Information Theory, 29, 551, doi: 10.1109/TIT.1983.1056714

    work page doi:10.1109/tit.1983.1056714 1983

  21. [21]

    1965, Trudy Astrofizicheskogo Instituta Alma-Ata, 5, 87

    Einasto, J. 1965, Trudy Astrofizicheskogo Instituta Alma-Ata, 5, 87

    1965

  22. [22]

    J., & Hut, P

    Eisenstein, D. J., & Hut, P. 1998, ApJ, 498, 137, doi: 10.1086/305535

    work page doi:10.1086/305535 1998

  23. [23]

    R., Cole, S., & Frenk, C

    Eke, V. R., Cole, S., & Frenk, C. S. 1996, MNRAS, 282, 263, doi: 10.1093/mnras/282.1.263

    work page doi:10.1093/mnras/282.1.263 1996

  24. [24]

    J., Ca˜ nas, R., Poulton, R

    Elahi, P. J., Ca˜ nas, R., Poulton, R. J. J., et al. 2019, PASA, 36, e021, doi: 10.1017/pasa.2019.12

    work page doi:10.1017/pasa.2019.12 2019

  25. [25]

    S., White, S

    Frenk, C. S., White, S. D. M., Davis, M., & Efstathiou, G. 1988, ApJ, 327, 507, doi: 10.1086/166213 Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759, doi: 10.1086/427976

    work page doi:10.1086/166213 1988

  26. [26]

    , year = 1972, month = Aug, volume =

    Gunn, J. E., & Gott, III, J. R. 1972, ApJ, 176, 1, doi: 10.1086/151605

    work page doi:10.1086/151605 1972

  27. [27]

    2022, MNRAS, 509, 501, doi: 10.1093/mnras/stab2980

    Maksimova, N. 2022, MNRAS, 509, 501, doi: 10.1093/mnras/stab2980

    work page doi:10.1093/mnras/stab2980 2022

  28. [28]

    , keywords =

    Han, J. J., Conroy, C., Johnson, B. D., et al. 2026, AJ, 164, 249, doi: 10.3847/1538-3881/ac97e9

    work page doi:10.3847/1538-3881/ac97e9 2026

  29. [29]

    Hopkins, P. F. 2015, Monthly Notices of the Royal Astronomical Society, 450, 53, doi: 10.1093/mnras/stv195

    work page doi:10.1093/mnras/stv195 2015

  30. [30]

    V., & Quinn, T

    Jetley, P., Gioachin, F., Mendes, C., Kale, L. V., & Quinn, T. 2008, in 2008 IEEE International Symposium on Parallel and Distributed Processing (Miami, FL, USA: IEEE), 1–12, doi: 10.1109/IPDPS.2008.4536319 28

    work page doi:10.1109/ipdps.2008.4536319 2008

  31. [31]

    Quinn, T. R. 2010, in Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and

    2010

  32. [32]

    Analysis, SC ’10 (USA: IEEE Computer Society), 1–11, doi: 10.1109/SC.2010.49

    work page doi:10.1109/sc.2010.49 2010

  33. [33]

    2024, ApJ, 964, 123, doi: 10.3847/1538-4357/ad245b

    Jung, M., Roca-Fàbrega, S., Kim, J.-H., et al. 2024, ApJ, 964, 123, doi: 10.3847/1538-4357/ad245b

    work page doi:10.3847/1538-4357/ad245b 2024

  34. [34]

    H., et al

    Jung, M., Kim, J.-H., Nguyễn, T. H., et al. 2025, ApJ, 994, 245, doi: 10.3847/1538-4357/ae112d

    work page doi:10.3847/1538-4357/ae112d 2025

  35. [35]

    2014, ApJS, 210, 14, doi: 10.1088/0067-0049/210/1/14

    Kim, J.-h., Abel, T., Agertz, O., et al. 2014, ApJS, 210, 14, doi: 10.1088/0067-0049/210/1/14

    work page doi:10.1088/0067-0049/210/1/14 2014

  36. [36]

    R., Knebe A., 2009, @doi [ ] 10.1088/0067-0049/182/2/608 , https://ui.adsabs.harvard.edu/abs/2009ApJS..182..608K 182, 608

    Knollmann, S. R., & Knebe, A. 2009, ApJS, 182, 608, doi: 10.1088/0067-0049/182/2/608

    work page doi:10.1088/0067-0049/182/2/608 2009

  37. [37]

    Kong, H., Boylan-Kolchin, M., & Bullock, J. S. 2025, arXiv e-prints, arXiv:2503.10766, doi: 10.48550/arXiv.2503.10766

    work page doi:10.48550/arxiv.2503.10766 2025

  38. [38]

    V., Klypin, A

    Kravtsov, A. V., Klypin, A. A., & Khokhlov, A. M. 1997, The Astrophysical Journal Supplement Series, 111, 73, doi: 10.1086/313015

    work page doi:10.1086/313015 1997

  39. [39]

    1994, MNRAS, 271, 676, doi: 10.1093/mnras/271.3.676

    Lacey, C., & Cole, S. 1994, MNRAS, 271, 676, doi: 10.1093/mnras/271.3.676

    work page doi:10.1093/mnras/271.3.676 1994

  40. [40]

    B., Primack, J

    Lahav, O., Lilje, P. B., Primack, J. R., & Rees, M. J. 1991, MNRAS, 251, 128, doi: 10.1093/mnras/251.1.128

    work page doi:10.1093/mnras/251.1.128 1991

  41. [41]

    C., Mestel, L., & Shu, F

    Lin, C. C., Mestel, L., & Shu, F. H. 1965, ApJ, 142, 1431, doi: 10.1086/148428

    work page doi:10.1086/148428 1965

  42. [42]

    Bouchet, F. R. 2009, MNRAS, 396, 1329, doi: 10.1111/j.1365-2966.2009.14825.x

    work page doi:10.1111/j.1365-2966.2009.14825.x 2009

  43. [43]

    2024, ApJ, 970, 178, doi: 10.3847/1538-4357/ad4e33

    Mansfield, P., Darragh-Ford, E., Wang, Y., et al. 2024, ApJ, 970, 178, doi: 10.3847/1538-4357/ad4e33

    work page doi:10.3847/1538-4357/ad4e33 2024

  44. [44]

    2015, Computational Astrophysics and Cosmology, 2, 1, doi: 10.1186/s40668-015-0007-9 Mu˜ noz-Cuartas, J

    Menon, H., Wesolowski, L., Zheng, G., et al. 2015, Computational Astrophysics and Cosmology, 2, 1, doi: 10.1186/s40668-015-0007-9 Mu˜ noz-Cuartas, J. C., Macciò, A. V., Gottl¨ ober, S., &

    work page doi:10.1186/s40668-015-0007-9 2015

  45. [45]

    Dutton, A. A. 2011, MNRAS, 411, 584, doi: 10.1111/j.1365-2966.2010.17704.x

    work page doi:10.1111/j.1365-2966.2010.17704.x 2011

  46. [46]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., & White, S. D. M. 1996, ApJ, 462, 563, doi: 10.1086/177173 Nguyễn, T. H., Barrow, K. S. S., Byrom, S., & Satish, V. 2026, MNRAS, 545, staf2045, doi: 10.1093/mnras/staf2045

    work page doi:10.1086/177173 1996

  47. [47]

    Peebles, P. J. E. 1980, The large-scale structure of the universe

    1980

  48. [48]

    2010, A&A, 519, A94, doi: 10.1051/0004-6361/201014214

    Planelles, S., & Quilis, V. 2010, A&A, 519, A94, doi: 10.1051/0004-6361/201014214

    work page doi:10.1051/0004-6361/201014214 2010

  49. [49]

    2012, Astronomy & Astrophysics, 538, A82, doi: 10.1051/0004-6361/201117402 Roca-Fàbrega, S., Kim, J.-H., Hausammann, L., et al

    Revaz, Y., & Jablonka, P. 2012, Astronomy & Astrophysics, 538, A82, doi: 10.1051/0004-6361/201117402 Roca-Fàbrega, S., Kim, J.-H., Hausammann, L., et al. 2021, ApJ, 917, 64, doi: 10.3847/1538-4357/ac088a Roca-Fàbrega, S., Kim, J.-H., Primack, J. R., et al. 2024a, ApJ, 968, 125, doi: 10.3847/1538-4357/ad43de Roca-Fàbrega, S., Kim, J.-h., Primack, J. R., et...

    work page doi:10.1051/0004-6361/201117402 2012

  50. [50]

    Santos-Olmsted, L., Barrow, K. S. S., & Hartwig, T. 2024, ApJ, 969, 144, doi: 10.3847/1538-4357/ad46fd

    work page doi:10.3847/1538-4357/ad46fd 2024

  51. [51]

    D., Weller, J., Ostriker, J

    Shaw, L. D., Weller, J., Ostriker, J. P., & Bode, P. 2006, ApJ, 646, 815, doi: 10.1086/505016

    work page doi:10.1086/505016 2006

  52. [52]

    J., Norman, M

    Skory, S., Turk, M. J., Norman, M. L., & Coil, A. L. 2010, ApJS, 191, 43, doi: 10.1088/0067-0049/191/1/43

    work page doi:10.1088/0067-0049/191/1/43 2010

  53. [53]

    , keywords =

    Springel, V. 2005, Monthly Notices of the Royal Astronomical Society, 364, 1105, doi: 10.1111/j.1365-2966.2005.09655.x —. 2010, Monthly Notices of the Royal Astronomical Society, 401, 791, doi: 10.1111/j.1365-2966.2009.15715.x

    work page doi:10.1111/j.1365-2966.2005.09655.x 2005

  54. [54]

    , keywords =

    Springel, V., Pakmor, R., Zier, O., & Reinecke, M. 2021, Monthly Notices of the Royal Astronomical Society, 506, 2871, doi: 10.1093/mnras/stab1855

    work page doi:10.1093/mnras/stab1855 2021

  55. [55]

    Springel, V., White, S. D. M., & Hernquist, L. 2004, in IAU

    2004

  56. [56]

    Springel, V., White, S. D. M., Tormen, G., & Kauffmann, G. 2001, MNRAS, 328, 726, doi: 10.1046/j.1365-8711.2001.04912.x

    work page doi:10.1046/j.1365-8711.2001.04912.x 2001

  57. [57]

    R., et al

    Strawn, C., Roca-Fàbrega, S., Primack, J. R., et al. 2024, ApJ, 962, 29, doi: 10.3847/1538-4357/ad12cb

    work page doi:10.3847/1538-4357/ad12cb 2024

  58. [58]

    A new high resolution code called RAMSES

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

    work page doi:10.1051/0004-6361:20011817 2002

  59. [59]

    2016, MNRAS, 458, 4477, doi: 10.1093/mnras/stw606 Vallés-Pérez, D., Planelles, S., & Quilis, V

    Tomassetti, M., Dekel, A., Mandelker, N., et al. 2016, MNRAS, 458, 4477, doi: 10.1093/mnras/stw606 Vallés-Pérez, D., Planelles, S., & Quilis, V. 2022, A&A, 664, A42, doi: 10.1051/0004-6361/202243712

    work page doi:10.1093/mnras/stw606 2016

  60. [60]

    2017, MNRAS, 467, 3226, doi: 10.1093/mnras/stx282

    Vega-Ferrero, J., Yepes, G., & Gottl¨ ober, S. 2017, MNRAS, 467, 3226, doi: 10.1093/mnras/stx282

    work page doi:10.1093/mnras/stx282 2017

  61. [61]

    S., et al

    Wang, J., Bose, S., Frenk, C. S., et al. 2020, Nature, 585, 39, doi: 10.1038/s41586-020-2642-9

    work page doi:10.1038/s41586-020-2642-9 2020

  62. [62]

    2020, The Astrophysical Journal Supplement Series, 248, 32, doi: 10.3847/1538-4365/ab908c

    Weinberger, R., Springel, V., & Pakmor, R. 2020, The Astrophysical Journal Supplement Series, 248, 32, doi: 10.3847/1538-4365/ab908c

    work page doi:10.3847/1538-4365/ab908c 2020

  63. [63]

    2025, A&A, 703, A43, doi: 10.1051/0004-6361/202556036

    Zhu, L., Xue, X.-X., Mao, S., Yang, C., & Zhang, L. 2025, A&A, 703, A43, doi: 10.1051/0004-6361/202556036

    work page doi:10.1051/0004-6361/202556036 2025

  64. [64]

    2017, MNRAS, 466, 1625, doi: 10.1093/mnras/stw2945

    Zjupa, J., & Springel, V. 2017, MNRAS, 466, 1625, doi: 10.1093/mnras/stw2945

    work page doi:10.1093/mnras/stw2945 2017