A MARVEL-ous study of how well galaxy shapes reflect Dark Matter halo shapes in Cold Dark Matter Simulations

Akaxia Cruz; Alyson M. Brooks; Anna Engelhardt; Ben Keller; Blake Keith; Ferah Munshi; James Wadsley; Jordan Van Nest; Thomas Quinn

REVIEW 1 major objections 5 minor 4 cited by

A MARVEL-ous study of how well galaxy shapes reflect Dark Matter halo shapes in Cold Dark Matter Simulations

T0 review · 1 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read In diskless dwarf galaxies, the stellar distribution mirrors the dark matter halo's 3D shape, making stellar shapes a practical proxy for halo shapes and a route to testing dark matter models.

desk verdict A solid, careful simulation study showing nondisky dwarf stellar shapes track DM halo shapes across 1e6-1e10 Msun; side claims on mergers and feedback are softer than the abstract implies. read the letter →

arxiv 2501.16317 v2 pith:T57VVGZA submitted 2025-01-27 astro-ph.GA

Blake Keith , Ferah Munshi , Alyson M. Brooks , Jordan Van Nest , Anna Engelhardt , Akaxia Cruz , Ben Keller , Thomas Quinn

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James Wadsley

This is my paper · ORCID

classification astro-ph.GA PACS 95.35.+d98.62.Gq

keywords dwarfgalaxiesdarkmatterhaloshapesgalaxyintrinsictriaxialitycoldsimulationscosmologicalzoom-instellardiskssupernovafeedback

topics Dark Matter

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

The paper sets out to establish that the 3D shape of a dwarf galaxy's stars can stand in for the 3D shape of its dark matter halo — a claim that matters because halo shape is one of the few predicted differences between cold dark matter and self-interacting dark matter. Using 80 dwarf galaxies from three cosmological zoom-in simulation suites spanning stellar masses $10^6$–$10^{10}\,M_\odot$, the authors compare axis ratios and triaxiality of stars and dark matter measured at twice the effective radius. They find that in galaxies without a stellar disk the stellar and dark-matter axis-ratio distributions are statistically indistinguishable, whereas in disky galaxies the disk flattens the stellar distribution and breaks the correspondence. If correct, the result lets observers use plain starlight in low-mass, diskless dwarfs to infer the shape of the underlying dark matter halo and thereby discriminate between dark matter models.

What carries the argument

The load-bearing object is the shape tensor $S_{ij} = (1/M)\sum_k m_k r_{k,i} r_{k,j}$ — the moment-of-inertia tensor of particle positions — evaluated in iteratively fitted ellipsoidal shells whose eigenvalues give the principal axes $A \geq B \geq C$ and hence the axis ratios $Q = B/A$ and $S = C/A$. After smoothing the axis-ratio profiles $Q(r)$ and $S(r)$ with 3rd- to 5th-order polynomials, the shapes are read off at twice the effective radius ($2 R_{\rm eff}$), the radius where self-interacting-dark-matter sphericalization would be detectable. Adaptive radial binning with a floor of 5000 star particles sets the resolution limits, and the triaxiality parameter $T = (1-Q^2)/(1-S^2)$ places each galaxy between oblate ($T < 1/3$), triaxial ($1/3 < T < 2/3$), and prolate ($T > 2/3$). The disk classification — thin ($S_* < 0.4$) and circular ($Q_* > 0.65$) — separates the cleanly dark-matter-tracing population from the disk-dominated one.

What would settle it

Recompute the shapes of the same simulated galaxies with a different measurement pipeline — for instance the standard (non-reduced) inertia tensor, a different radial binning scheme, or much stricter per-bin particle thresholds — and repeat the Kolmogorov–Smirnov tests: if the $Q$ distributions of nondisky stars and dark matter are no longer statistically indistinguishable (p=0.49) and the per-galaxy $S_{\rm DM}/S_*$ ratios drift away from unity, the claimed correspondence is a product of the fitting procedure rather than the physics. An observational check would be deep imaging of a few dozen field dwarfs below $10^{7.5}\,M_\odot$ whose deprojected stellar shapes come out systematically rounder or more oblate than the prolate, triaxial halo shapes the simulations predict.

Watch

Extended reading notes

Core claim

The paper claims that stellar shape follows dark matter halo shape across its 80 simulated dwarf galaxies. The correspondence is strongest where baryons are dynamically subdominant: in nondisky galaxies, which dominate below stellar mass about $10^{7.5}\,M_\odot$, a two-sample Kolmogorov–Smirnov test finds the stellar and dark-matter distributions of the intermediate-to-major axis ratio $Q$ statistically indistinguishable (p=0.49) and the minor-to-major ratio $S$ only modestly different (p=0.015), with per-galaxy $S_{\rm DM}/S_*$ ratios near unity. Stellar triaxiality tracks halo triaxiality with slope $0.99\pm0.08$ ($R^2=0.77$) in nondisky galaxies, while disky galaxies deviate from the one-to-one relation because the disk makes stars flatter ($S_{\rm DM}/S_*$ averages $2.92\pm0.60$). The authors further argue that disk formation above about $10^{7.5}\,M_\odot$ starts to round and flatten the dark matter itself, that stellar and dark-matter axes are strongly aligned (most clearly for the minor axis in disky systems), that swapping between two supernova feedback implementations leaves both shapes unchanged, and that recent mergers with ratios above about 4 move the axis ratios by no more than about 0.05. The paper concludes that stellar shape measurements are a reliable tool for inferring dark matter halo shapes in dwarf galaxies.

Load-bearing premise

The whole stellar–DM comparison rests on the assumption that the iterative shape-tensor fitting, with its chosen radial bins, particle-count floors, and polynomial smoothing, recovers the true 3D shapes of stars and dark matter equally well at twice the effective radius; if the measurement pipeline biases the two components differently, the apparent shape correspondence could be partly artificial.

Editorial extensions

If this is right

Observed stellar shapes in nondisky dwarf galaxies below about $10^{7.5}\,M_\odot$ can be read directly as dark matter halo shapes, giving observers a handle on halo shape without kinematics or lensing.
Measuring stellar triaxiality in a sample of diskless dwarfs can discriminate between the prolate halos that cold dark matter produces and the rounder halos that self-interacting dark matter predicts.
The stellar disk is the main thing that breaks the stellar–DM correspondence: above $10^{7.5}\,M_\odot$ the disk makes stars much flatter than the halo, so dark-matter inference should be restricted to diskless galaxies.
Shape measurements appear insensitive to the supernova feedback implementation, so dwarf-galaxy shapes do not encode subgrid baryonic physics and results from different simulation suites can be combined.
Recent mergers with ratios above 4 change both stellar and dark-matter axis ratios by less than about 0.05, so merger activity does not need to be accounted for when interpreting dwarf galaxy shapes.

Reading between the lines

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

If stellar shape is a faithful halo-shape tracer, then the sphericalization that self-interacting dark matter produces in inner halos should appear as a measurable roundness trend in the stellar axis-ratio profiles $Q(r)$ and $S(r)$ of low-mass diskless dwarfs toward the center — a test the paper gestures at but does not run.
The $10^{7.5}\,M_\odot$ disk-formation threshold suggests a practical survey design rule: shape-based dark-matter probes should be restricted to diskless dwarfs below this mass, while shape distributions of higher-mass dwarfs should be treated as baryon-contaminated.
The proxy claim could be stress-tested by forward-modeling the observational pipeline — projecting the simulated galaxies to 2D, measuring ellipticities as a survey would, and applying standard deprojection techniques — to verify that the stellar–DM shape correlation survives the inference process before trusting it on real data.
If the proxy holds, the population distribution of dwarf galaxy shapes becomes a dark-matter-model diagnostic: the fractions of prolate, triaxial, and oblate field dwarfs are set by structure formation in CDM and would be shifted by dark-matter self-interactions, so shape surveys could constrain the self-interaction cross section.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's my take. The useful new result is the systematic, multi-suite comparison of intrinsic 3D shapes of stars and DM in dwarfs from 1e6 to 1e10 Msun, with the mass-dependent stellar/DM axis ratio ratios, triaxiality slopes, alignment fractions, and robustness checks. The central claim—nondisky dwarf stellar shapes trace DM halo shapes—is well supported: KS p=0.49 for Q, near-unity SDM/S* at low mass, triaxiality slope 0.99±0.08 with R2=0.77, and alignment consistent with shape categories. They also validate against Kado-Fong et al., which is a genuine check rather than a fit.

What the paper does well: it measures shapes with an iterative inertia tensor, enforces particle thresholds and convergence, reports slopes and scatter, and includes radial-profile appendices. The disk classification is checked visually and kinematically; only 14% sit near the boundary. Code and figure data are public. Simulations are proprietary, but the paper says as much and offers collaboration.

Soft spots, in proportion. The merger claim is the weakest: it rests on two galaxies with ratios 4.4 and 5.6, and the abstract overstates it more than the text does. The feedback null is based on eight matched galaxies in one volume; useful, but underpowered for a strong claim of robustness. The three suites differ in resolution and cosmology; they do check environment and see no difference, which mitigates this.

The core-radius concern is worth taking seriously. They cite Fischer & Valenzuela that shape measurements are undefined in near-constant-density regions, and they assert 2Reff lies outside typical DM cores, but they do not measure density slopes or core radii for their sample. For the lowest-mass galaxies this is checkable. It is not obviously fatal—their nondisky low-mass shapes are prolate/triaxial, not spherical, so they are not simply reporting round objects—but a density-slope measurement at 2Reff would settle it. I would want that added before citing this as the definitive proxy result.

Circularity is not an issue: shapes are measured and compared, not fitted to the conclusion. The citation pattern is appropriate; they engage EDGE, FIRE, Chua et al., and Valenzuela, and the novelty claim holds.

Who is this for: observers who want to use dwarf stellar shapes to infer DM shapes, and simulators comparing CDM vs SIDM predictions. It deserves a serious referee, and I would cite it with the core-radius caveat noted.

Referee Report

1 major / 5 minor

Summary. The paper measures intrinsic 3D shapes of the stellar and dark-matter components in 80 simulated dwarf galaxies from the Marvel, DC Justice League, and Massive Dwarfs zoom-in suites, spanning stellar masses of about 1e6 to 1e10 Msun. Shapes are derived from iterative inertia-tensor fits in radial shells, evaluated at twice the effective radius (2 Reff), and summarized by axis ratios Q=B/A and S=C/A and by triaxiality. The central claim is that, for galaxies without a stellar disk, the stellar distribution closely tracks the dark-matter halo shape, so stellar shapes can serve as a proxy for DM shapes in low-mass dwarfs; the paper also reports that disks produce measurable stellar-DM shape differences, that triaxiality tracks between the two components, that axes are generally aligned, that two supernova feedback implementations give similar shapes, and that recent mergers with ratios greater than about 4 do not strongly perturb the measured shapes. The results are validated against observed dwarf shapes from Kado-Fong et al. (2020).

Significance. If the central claim is correct, the paper provides a practical route to infer DM halo shapes from stellar photometry in dwarf galaxies, which would be valuable for discriminating CDM from SIDM and for understanding baryonic effects on halo structure. The study has notable strengths: it uses multiple simulation suites with different resolutions and feedback models, it reports per-galaxy comparisons as well as ensemble KS tests, it includes an observational validation, and it states that the analysis code and figure data will be publicly available. The main caveat, discussed below, is whether the shape measurement at 2 Reff for the lowest-mass nondisky galaxies is well defined in regions that may have near-constant DM density; if that concern is not resolved, the inferred stellar-DM shape agreement could be partly an artifact of the measurement pipeline.

major comments (1)

[Section 2.3 / Section 3.2] The load-bearing claim that stellar shapes trace DM shapes in nondisky dwarfs depends on shape measurements at 2 Reff, but the paper does not measure the local DM density slope or core radius at 2 Reff for the low-mass sample. Section 2.3 asserts that 2 Reff is 'a higher radius than a typical DM core' and cites Read et al. (2016) and Fitts et al. (2017, 2019), yet no density profile or core radius measurement is presented. For the lowest-mass galaxies (M* ~ 1e6 Msun, where Section 3.3 reports 2 Reff values as small as 0.6 kpc), feedback-generated cores on the order of 0.5-1 kpc would place 2 Reff inside a near-constant-density region. In such a region the iterative ellipsoid fit is ill-conditioned, and the recovered axis ratios for both stars and DM may be dominated by Poisson noise and smoothing rather than by physical shape, biasing the two components toward spurious agreement. Figure 16, which shows shapes becoming rounder toward the center, is consistent with this concern. The authors should report d log rho_DM/d log r at 2 Reff (or equivalently, core radii) for the nondisky sample and demonstrate that the KS result (p=0.49 for Q) and the near-unity SDM/S* ratios at low masses are not driven by galaxies with flat central density slopes, for example by repeating the analysis after excluding systems with slope near zero at 2 Reff.

minor comments (5)

[Section 3.5 / Abstract] The conclusion that mergers with ratios greater than about 4 do not perturb galaxy shapes is based on two merger events with ratios 4.4 and 5.6, as the text acknowledges. The abstract and conclusions state this as a general result ('a dwarf galaxy's shape is largely unperturbed by recent mergers (with merger ratios >4)'); this should be rephrased to indicate the small number of events and the limited range of merger ratios probed.
[Section 4.2 / Abstract] The claim that shape measurements are 'robust to different implementations of baryonic feedback' rests on a comparison of eight galaxies in one simulation volume. The authors note the small sample in Section 4.2, but the abstract and summary conclusion do not carry this caveat; the wording should be softened to reflect the limited statistical power.
[Appendix D] In the final paragraph of Appendix D, the text says 'Q = B/A and S = B/A values closer to one'; the second expression should be S = C/A.
[Section 3.2 / Figure 4] The slopes quoted for QDM/Q* and SDM/S* versus stellar mass do not include a discussion of whether the individual galaxy ratios are consistent with unity within the estimated measurement uncertainties. Adding representative error bars or a statement about typical per-galaxy uncertainties would make the low-mass 'near-unity' claim easier to evaluate.
[Section 3.4] The statement that '82% of our sample is aligned according to our expectations from shapes' uses shape categories that are derived from the same axis-ratio measurements used to define the expected alignment patterns, so this is a consistency check rather than an independent confirmation; the wording should make that explicit.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: stellar and DM axis ratios are independently measured from the simulations and compared, with no fitted target, self-citation chain, or definitional equivalence.

full rationale

Score 0. The paper's central claim is an empirical comparison of two independently measured shape distributions in the same simulated galaxies, not a derived quantity that reduces to its inputs. Section 2.3 defines the reduced inertia tensor (Eq. 2) and applies the same iterative ellipsoid-fitting algorithm to DM and stellar particles; axis ratios Q and S are then read off at 2Reff for each component separately. Section 3.2 compares these measurements with KS tests (p=0.49 for Q, p=0.015 for S in nondisky galaxies) and per-galaxy ratios QDM/Q* and SDM/S*. None of these ratios is a fitted parameter, and no stellar value is constructed from DM values or vice versa. The disky/nondisky split uses stellar shape thresholds (S*<0.4, Q*>0.65) with robustness margins and kinematic checks, and the DM shapes are not used in the classification, so the comparison is not circular by selection. Self-citations to Munshi et al. (2019, 2021), Bellovary et al. (2019), Keller et al. (2014), and Tremmel et al. (2017) describe the simulation codes and subgrid models, i.e., the inputs, and do not carry the conclusion; the observational validation against Kado-Fong et al. (2020) is an external benchmark. The Fischer and Valenzuela (2023) caveat about ill-defined shapes in constant-density cores is explicitly acknowledged in Section 2.3, but it is a measurement-validity concern, not a circular one: even if 2Reff lay inside a core for the lowest-mass galaxies, the resulting noise-dominated agreement would be a common-cause artifact rather than an equation-level equivalence. No uniqueness theorem, ansatz-smuggling citation, or redefinition of a known result is present. The paper is self-contained: all central comparisons are direct measurements from the simulations with stated convergence and resolution criteria.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The analysis is empirical: axis ratios are measured from particle distributions, not derived by fitting the conclusion. The hand-set thresholds and binning choices listed above affect the sample and interpretation, but none of them encodes the target result. The axioms are standard assumptions of the simulation program and shape-measurement method. No new particles, forces, or physical entities are introduced.

free parameters (7)

Disk classification thresholds = S* < 0.4 and Q* > 0.65 at 2 Reff
Hand-chosen cutoffs to separate disky from nondisky galaxies; the authors test a +/-0.05 margin and report 14% of galaxies lie near the boundary.
Minimum star particle count = 5000
Hand-set to guarantee at least 15 radial bins and 300 particles per bin; this also sets the sample's stellar mass floor.
Radial binning constants (modified Zemp) = 1000, 10, 20, 3
Constants in the adaptive bin count formula are chosen to balance resolution and noise; not fitted to the shape result.
Polynomial smoothing order = 3 to 5
Q(r) and S(r) are smoothed with polynomials of order 3-5 to reduce noise; the choice can influence measured values at 2 Reff.
Measurement radius = 2 Reff
Primary shape measurements are taken at twice the effective radius, chosen because it falls in the region where SIDM shape differences may be detectable.
Alignment threshold = 10 degrees
An axis is called well aligned when the DM-stellar eigenvector angle is below 10 degrees; an arbitrary but common tolerance.
Spherical threshold for degeneracy categories = S > 0.8
Used in Section 3.4 to classify systems whose axis orientations are poorly defined; affects the alignment interpretation but not the main shape ratios.

assumptions (7)

domain assumption CDM N-body plus SPH simulations with the adopted subgrid physics reproduce the real stellar and DM structure of dwarf galaxies.
The whole inference from simulations to galaxies rests on this; invoked throughout Section 2.
domain assumption The iterative reduced inertia tensor yields unbiased intrinsic 3D axis ratios.
Section 2.3 relies on Allgood, Tomassetti, Zemp, and Vera-Ciro for convergence and accuracy of the method.
domain assumption AHF identifications and the R200c definition give the correct halo and subhalo boundaries.
Section 2.2 defines the sample and virial radius through Amiga Halo Finder.
domain assumption Sersic profile fits to face-on V-band surface brightness profiles provide reliable effective radii.
Section 2.3 uses these Reff values to set the measurement radius for every galaxy.
domain assumption The Kado-Fong et al. deprojected observational shapes are a valid comparison benchmark.
Section 3.1 uses these data to validate simulated shapes; the paper notes deprojection may add scatter.
domain assumption Blastwave and superbubble SN feedback models bracket plausible baryonic feedback in dwarfs.
Section 4.2 combines two feedback implementations and treats agreement as evidence of robustness.
domain assumption A 600 Myr window (last three outputs) is sufficient for merger-perturbed galaxies to return to equilibrium at stellar radii.
Section 3.5 uses dynamical time estimates to justify measuring only the last three timesteps.

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Cite this review

Pith. "Pith review of A MARVEL-ous study of how well galaxy shapes reflect Dark Matter halo shapes in Cold Dark Matter Simulations." pith.science (2026). https://pith.science/paper/T57VVGZA

@misc{pith2026250116317,
  author       = {Pith},
  title        = {Pith review of: A MARVEL-ous study of how well galaxy shapes reflect Dark Matter halo shapes in Cold Dark Matter Simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T57VVGZA}},
  note         = {Machine review of arXiv:2501.16317}
}

abstract

We present a 3D shape analysis of both dark matter (DM) and stellar matter (SM) in simulated dwarf galaxies to determine whether stellar shape traces DM shape. Using 80 central and satellite galaxies from three simulation suites (Marvelous Massive Dwarfs, Marvelous Dwarfs, and DC Justice League) spanning stellar masses of $10^6$--$10^{10}$ $M_\odot$, we measure 3D shapes through the moment of inertia tensor at two times the effective radius to derive axis ratios ($C/A$, $B/A$) and triaxiality. We find that stellar shape does indeed follow DM halo shape for our dwarf galaxies. However, the presence of a stellar disk in more massive dwarfs ($M_* \gtrsim 10^{7.5}$ $M_\odot$) pulls the distribution of stellar $C/A$ ratios to lower values, while in lower mass galaxies the gravitational potential remains predominantly shaped by DM. Similarly, stellar triaxiality generally tracks dark matter halo triaxiality, with this relationship being particularly strong for non-disky galaxies though weaker in disky systems. This correlation is reinforced by strong alignment between SM and DM axes, particularly in disk galaxies. Further, we find no detectable difference in either SM or DM shape comparing two different SNe feedback implementations, demonstrating that shape measurements may be robust to different implementations of baryonic feedback in dwarf galaxies. We also observe that a dwarf galaxy's shape is largely unperturbed by recent mergers (with merger ratios $>4$). This comprehensive study demonstrates that stellar shape measurements can serve as a reliable tool for inferring DM shapes in dwarf galaxies.

Figures

Figures reproduced from arXiv: 2501.16317 by the authors.

**Figure 1.** Simulated red–green–blue images in the i, V, and u bands generated using pynbody.plot.stars.render. Left panels show face-on orientations, while right panels show edge-on orientations. The cyan projected ellipse indicates shape and scale at 2 Reff. Following the disk classiDcation criteria deDned in Section 2.5, we present three representative cases: (top) a clear disky galaxy; (middle) a nondisky galaxy; and (botto… view at source ↗

**Figure 2.** shows the relationship between stellar mass and axis ratio (Q* and S* ) measured at 2 Reff for both disky and nondisky galaxies. The average minor-to-major axis ratio (S) differs signiDcantly between these populations, with disky galaxies showing = ± * S 0.26 0.06 and nondisky galaxies having = ± * S 0.54 0.13. The intermediate-to-major axis ratio Q* shows only a weak mass dependence on stellar mass (slope = −0.03 ±… view at source ↗

**Figure 3.** Axis ratio measurements for DM and stars at twice the effective radius (2 Reff). The main plot shows the minor-to-major axis ratio (S = C/A) vs. the intermediate-to-major axis ratio (Q = B/A) for DM (circles) and stellar components (stars). Each galaxy is linked to its halo with a solid gray line. The gray dashed 1:1 line represents perfectly prolate objects where B = C. The region above this is empty as C < B < A b… view at source ↗

Figures from the paper (12 more)

**Figure 4.** Figure 4: Stellar mass ([ ( )]) / * log M M vs. relative changes in shape between DM and stellar components. The left panel shows QDM/Q*, where Q (intermediate-tomajor axis ratio) indicates elongation along the major axis, while the right panel shows SDM/S* , where S (minor-to-…

**Figure 5.** Figure 5: Triaxiality, T = (1 − Q 2 )/(1 − S 2 ), for our galaxies as a function of stellar mass ([ ( )]) / * log M M . Vertical lines link the T values for DM (circles) and SM (stars) for each galaxy. Galaxies with pronounced stellar disks are colored blue, those without are bl…

**Figure 6.** Figure 6: Triaxiality of the DM (TDM) vs. the triaxiality of the SM (T* ) at twice the effective radius (2 Reff). Disky galaxies are colored in blue, while nondisky are black. The dashed line shows T* = TDM The shaded gray region represents the vicinity of the dashed line, deDne…

**Figure 8.** Figure 8: presents the distribution of misalignment angles between the DM and SM components for each principal axis. We Dnd distinct alignment patterns, with the strongest correlation along the C axis (60/80 galaxies show strong alignment). This alignment is particularly pronoun…

**Figure 9.** Figure 9: shows neither Q nor S vary more than ∼0.05 across the total time period (z = 0–0.04). This result would hint at the idea that even mergers of similar mass do not seem to dramatically rearrange the central density structure of DM halos (e.g., B. Moore et al. 2004). With…

**Figure 10.** Figure 10: Recreation of [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

**Figure 12.** Figure 12: that are within 1 < Jz/Jcirc < 3 and do not overlap with the disky sample, one of which is a merging system. These systems may contain unbound star particles that contribute signDcantly to the average Jz/Jcirc. Appendix C Galaxy Environments As shown in [PITH_FULL_IM…

**Figure 11.** Figure 11: shows a contour plot of how Q = B/A and S = C/A map to triaxiality. In spherical systems where Q and S are close to one, small changes in directions perpendicular to the contours lead to disproportional changes in triaxiality, despite reGecting largely similar shapes.…

**Figure 13.** Figure 13: Recreation of [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]

**Figure 14.** Figure 14: Recreation of [PITH_FULL_IMAGE:figures/full_fig_p015_14.png]

**Figure 15.** Figure 15: ). We do not Dnd notable changes in the shape of DM or stars in galaxies without a stellar disk compared to measurements at 2 Reff. Similar to our results at 2 Reff, we see considerable, but not complete overlap, with the results from E. Kado-Fong et al. (2020). In […

**Figure 16.** Figure 16: Shape proDles of three representative galaxies showing triaxiality parameters Q = B/A (upper panels) and S = C/A (lower panels) as a function of radius (0 < r < 3 Reff). DM (blue) and SM (red) components are shown with raw measurements (circles) and smoothed proDles (…

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Forward citations

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Reference graph

Works this paper leans on

91 extracted references · 79 canonical work pages · cited by 4 Pith papers

[1]

V., Leitner, S

Agertz, O., Kravtsov, A. V., Leitner, S. N., & Gnedin, N. Y. 2013,ApJ, 770, 25

2013
[2]

I., et al

Agertz, O., Pontzen, A., Read, J. I., et al. 2020,MNRAS ,491, 1656

2020
[3]

A., Primack, J

Allgood, B., Flores, R. A., Primack, J. R., et al. 2006,MNRAS ,367, 1781

2006
[4]

M., Christensen, C

Applebaum, E., Brooks, A. M., Christensen, C. R., et al. 2021,ApJ,906, 96

2021
[5]

2024,ApJ,970, 40

Azartash-Namin, B., Engelhardt, A., Munshi, F., et al. 2024,ApJ,970, 40

2024
[6]

P., Bendek, E., Monacelli, B., et al

Bailey, V. P., Bendek, E., Monacelli, B., et al. 2023,Proc. SPIE,12680, 126800T

2023
[7]

2005,ApJ,627, 647

Bailin, J., & Steinmetz, M. 2005,ApJ,627, 647

2005
[8]

M., Cleary, C

Bellovary, J. M., Cleary, C. E., Munshi, F., et al. 2019,MNRAS ,482, 2913

2019

Show all 91 references

[9]

1995,ApJL,447, L25

Burkert, A. 1995,ApJL,447, L25

1995
[10]

V., Dutton, A

Butsky, I., Macciò, A. V., Dutton, A. A., et al. 2016,MNRAS ,462, 663

2016
[11]

1993,MNRAS ,265, 1013

Caon, N., Capaccioli, M., & D’Onofrio, M. 1993,MNRAS ,265, 1013

1993
[12]

E., Tissera, P

Cataldi, P., Pedrosa, S. E., Tissera, P. B., & Artale, M. C. 2021,MNRAS , 501, 5679

2021
[13]

K., Kereš, D., Oñorbe, J., et al

Chan, T. K., Kereš, D., Oñorbe, J., et al. 2015,MNRAS ,454, 2981

2015
[14]

2012,MNRAS ,425, 3058 Christensen,C.R.,Governato,F.,Quinn,T.,etal.2014,MNRAS ,440,2843

Christensen, C., Quinn, T., Governato, F., et al. 2012,MNRAS ,425, 3058 Christensen,C.R.,Governato,F.,Quinn,T.,etal.2014,MNRAS ,440,2843

2012
[15]

Chua, K. T. E., Pillepich, A., Vogelsberger, M., & Hernquist, L. 2019, MNRAS ,484, 476

2019
[16]

Chua, K. T. E., Vogelsberger, M., Pillepich, A., & Hernquist, L. 2022, MNRAS ,515, 2681 Colín,P.,Avila-Reese,V.,Valenzuela,O.,&Firmani,C.2002,ApJ,581,777

2022
[17]

Collins, M. L. M., & Read, J. I. 2022,NatAs,6, 647 Davé, R., Spergel, D. N., Steinhardt, P. J., & Wandelt, B. D. 2001,ApJ, 547, 574

2022
[18]

P., Moore, B., Quinn, T., et al

Debattista, V. P., Moore, B., Quinn, T., et al. 2008,ApJ,681, 1076 delosReyes,M.A.C.,Asali,Y.,Wechsler,R.,etal.2024,arXiv:2409.03959 Di Cintio, A., Brook, C. B., Macciò, A. V., et al. 2014,MNRAS ,437, 415

2008 arXiv
[19]

A., Peter, A

Dooley, G. A., Peter, A. H. G., Vogelsberger, M., Zavala, J., & Frebel, A. 2016,MNRAS ,461, 710

2016
[20]

A., Buck, T., Macciò, A

Dutton, A. A., Buck, T., Macciò, A. V., et al. 2020,MNRAS ,499, 2648

2020
[21]

S., & Valenzuela, L

Fischer, M. S., & Valenzuela, L. M. 2023,A&A ,670, A120

2023
[22]

2019,MNRAS ,490, 962

Fitts, A., Boylan-Kolchin, M., Bozek, B., et al. 2019,MNRAS ,490, 962

2019
[23]

Fitts, A.,Boylan-Kolchin,M., Elbert,O.D.,etal.2017,MNRAS ,471, 3547

2017
[24]

1991,ApJ,383, 112

Franx, M., Illingworth, G., & de Zeeuw, T. 1991,ApJ,383, 112

1991
[25]

B., Governato, F., Pontzen, A., et al

Fry, A. B., Governato, F., Pontzen, A., et al. 2015,MNRAS ,452, 1468

2015
[26]

2010,Natur,463, 203

Governato, F., Brook, C., Mayer, L., et al. 2010,Natur,463, 203

2010
[27]

2012,MNRAS ,422, 1231

Governato, F., Zolotov, A., Pontzen, A., et al. 2012,MNRAS ,422, 1231

2012
[28]

2006,PhRvD,74, 123522

Gustafsson, M., Fairbairn, M., & Sommer-Larsen, J. 2006,PhRvD,74, 123522

2006
[29]

2012,ApJ,746, 125

Haardt, F., & Madau, P. 2012,ApJ,746, 125

2012
[30]

F., Wetzel, A., Kereš, D., et al

Hopkins, P. F., Wetzel, A., Kereš, D., et al. 2018,MNRAS ,480, 800 Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019,ApJ,873, 111

2018
[31]

A., Kaviraj, S., Yi, S

Jackson, R. A., Kaviraj, S., Yi, S. K., et al. 2024,MNRAS ,528, 1655

2024
[32]

E., Huang, S., et al

Kado-Fong, E., Greene, J. E., Huang, S., et al. 2020,ApJ,900, 163

2020
[33]

Katz, N., & Gunn, J. E. 1991,ApJ,377, 365

1991
[34]

Keller, B. W. 2022,ApJ,939, 4 Keller,B.W.,Kruijssen,J.M.D.,&Chevance,M.2022,MNRAS ,514,5355

2022
[35]

W., Wadsley, J., Benincasa, S

Keller, B. W., Wadsley, J., Benincasa, S. M., & Couchman, H. M. P. 2014, MNRAS ,442, 3013 Keller,B.W.,Wadsley,J.,&Couchman,H.M.P.2015,MNRAS ,453,3499 Keller,B.W.,Wadsley,J.,&Couchman,H.M.P.2016,MNRAS ,463,1431

2014
[36]

R., & Knebe, A

Knollmann, S. R., & Knebe, A. 2009,ApJS,182, 608 Lazar,A.,Bullock,J.S.,Boylan-Kolchin,M.,etal.2020,MNRAS ,497,2393

2009
[37]

2020,PDU,30, 100719

Leauthaud, A., Singh, S., Luo, Y., et al. 2020,PDU,30, 100719

2020
[38]

Leung, G. Y. C., Leaman, R., Battaglia, G., et al. 2021,MNRAS ,500, 410

2021
[39]

D., Schaye, J., Schaller, M., & Bower, R

Ludlow, A. D., Schaye, J., Schaller, M., & Bower, R. 2020,MNRAS , 493, 2926

2020
[40]

R., Cuillandre, J.-C., Cantiello, M., et al

Marleau, F. R., Cuillandre, J.-C., Cantiello, M., et al. 2025,A&A ,697, A12

2025
[41]

Mateo, M. L. 1998,ARA&A ,36, 435

1998
[42]

2006, MNRAS ,369, 1021

Mayer, L., Mastropietro, C., Wadsley, J., Stadel, J., & Moore, B. 2006, MNRAS ,369, 1021

2006
[43]

F., & Ostriker, J

McKee, C. F., & Ostriker, J. P. 1977,ApJ,218, 148

1977
[44]

2015,ComAC ,2, 1

Menon, H., Wesolowski, L., Zheng, G., et al. 2015,ComAC ,2, 1

2015
[45]

2004,MNRAS , 354, 522

Moore, B., Kazantzidis, S., Diemand, J., & Stadel, J. 2004,MNRAS , 354, 522

2004
[46]

M., Applebaum, E., et al

Munshi, F., Brooks, A. M., Applebaum, E., et al. 2021,ApJ,923, 35

2021
[47]

M., Christensen, C., et al

Munshi, F., Brooks, A. M., Christensen, C., et al. 2019,ApJ,874, 40

2019
[48]

B., & Shchekinov, Y

Nath, B. B., & Shchekinov, Y. 2013,ApJL,777, L12

2013
[49]

F., Frenk, C

Navarro, J. F., Frenk, C. S., & White, S. D. M. 1996,ApJ,462, 563

1996
[50]

F., Ludlow, A., Springel, V., et al

Navarro, J. F., Ludlow, A., Springel, V., et al. 2010,MNRAS ,402, 21

2010
[51]

2018,MNRAS ,480, L106

Ogiya, G. 2018,MNRAS ,480, L106

2018
[52]

Orkney, M. D. A., Taylor, E., Read, J. I., et al. 2023,MNRAS ,525, 3516 Peter,A.H.G.,Rocha,M.,Bullock, J.S.,&Kaplinghat,M.2013,MNRAS , 430, 105 Peñarrubia, J., Pontzen, A., Walker, M. G., & Koposov, S. E. 2012,ApJL, 759, L42 PlanckCollaboration,Ade,P.A.R.,Aghanim,N.,etal.2016,...

2023
[53]

2012,MNRAS ,421, 3464

Pontzen, A., & Governato, F. 2012,MNRAS ,421, 3464

2012
[54]

P., Cadiou, C., et al

Pontzen, A., Rey, M. P., Cadiou, C., et al. 2021,MNRAS ,501, 1755

2021
[55]

Pontzen, A., Roškar, R., Stinson, G., & Woods, R., 2013 pynbody: N-Body/ SPH Analysis for Python, Astrophysics Source Code Library, ascl: 1305.002

2013
[56]

2018,ApJS,237, 23

Pontzen, A., & Tremmel, M. 2018,ApJS,237, 23

2018
[57]

F., Jenkins, A., et al

Power, C., Navarro, J. F., Jenkins, A., et al. 2003,MNRAS ,338, 14

2003
[58]

I., & Gilmore, G

Read, J. I., & Gilmore, G. 2005,MNRAS ,356, 107

2005
[59]

I., Iorio, G., Agertz, O., & Fraternali, F

Read, J. I., Iorio, G., Agertz, O., & Fraternali, F. 2016,MNRAS ,462, 3628

2016
[60]

P., Pontzen, A., Agertz, O., et al

Rey, M. P., Pontzen, A., Agertz, O., et al. 2019,ApJL,886, L3

2019
[61]

L., Brooks, A

Riggs, C. L., Brooks, A. M., Munshi, F., et al. 2024,ApJ,977, 20

2024
[62]

H., Bullock, J

Robles, V. H., Bullock, J. S., Elbert, O. D., et al. 2017,MNRAS ,472, 2945

2017
[63]

C., Torrey, P., Vogelsberger, M., & O’Neil, S

Rose, J. C., Torrey, P., Vogelsberger, M., & O’Neil, S. 2023,MNRAS , 519, 5623

2023
[64]

N., Karachentsev, I

Roychowdhury, S., Chengalur, J. N., Karachentsev, I. D., & Kaisina, E. I. 2013,MNRAS: Letters,436, L104

2013
[65]

J., Mutlu-Pakdil, B., Jones, M

Sand, D. J., Mutlu-Pakdil, B., Jones, M. G., et al. 2024,ApJ,977, 5

2024
[66]

B., White, S

Scannapieco, C., Tissera, P. B., White, S. D. M., & Springel, V. 2008, MNRAS ,389, 1137

2008
[67]

2010,MNRAS ,407, 1581 Simpson,C.M.,Grand,R.J.J.,Gómez,F.A.,etal.2018,MNRAS ,478,548

Shen, S., Wadsley, J., & Stinson, G. 2010,MNRAS ,407, 1581 Simpson,C.M.,Grand,R.J.J.,Gómez,F.A.,etal.2018,MNRAS ,478,548

2010
[68]

N., Bean, R., Doré, O., et al

Spergel, D. N., Bean, R., Doré, O., et al. 2007,ApJS,170, 377

2007
[69]

N., & Steinhardt, P

Spergel, D. N., & Steinhardt, P. J. 2000,PhRvL,84, 3760

2000
[70]

2006,MNRAS ,373, 1074 Sánchez-Janssen, R., Méndez-Abreu, J., & Aguerri, J

Stinson, G., Seth, A., Katz, N., et al. 2006,MNRAS ,373, 1074 Sánchez-Janssen, R., Méndez-Abreu, J., & Aguerri, J. A. L. 2010,MNRAS: Letters,406, L65

2006
[71]

2014, MNRAS ,441, 470

Tenneti, A., Mandelbaum, R., Di Matteo, T., Feng, Y., & Khandai, N. 2014, MNRAS ,441, 470

2014
[72]

2016,MNRAS ,458, 4477

Tomassetti, M., Dekel, A., Mandelker, N., et al. 2016,MNRAS ,458, 4477

2016
[73]

Tremmel, M., Governato, F., Volonteri, M., & Quinn, T. R. 2015,MNRAS , 451, 1868

2015
[74]

2017,MNRAS ,470, 1121

Tremmel, M., Karcher, M., Governato, F., et al. 2017,MNRAS ,470, 1121

2017
[75]

A., Long, H., Hirata, C

Troxel, M. A., Long, H., Hirata, C. M., et al. 2021,MNRAS ,501, 2044

2021
[76]

2018,PhR,730, 1

Tulin, S., & Yu, H.-B. 2018,PhR,730, 1

2018
[77]

M., Remus, R.-S., Dolag, K., & Seidel, B

Valenzuela, L. M., Remus, R.-S., Dolag, K., & Seidel, B. A. 2024,A&A , 690, A206 van der Wel, A., Chang, Y.-Y., Bell, E. F., et al. 2014,ApJL,792, L6

2024
[78]

2015,MNRAS ,453, 721

Velliscig, M., Cacciato, M., Schaye, J., et al. 2015,MNRAS ,453, 721

2015
[79]

A., Sales, L

Vera-Ciro, C. A., Sales, L. V., Helmi, A., & Navarro, J. F. 2014,MNRAS , 439, 2863

2014
[80]

2014,MNRAS , 444, 3684 17 The Astrophysical Journal, 986:138(18pp), 2025 June 20 Keith et al

Vogelsberger, M., Zavala, J., Simpson, C., & Jenkins, A. 2014,MNRAS , 444, 3684 17 The Astrophysical Journal, 986:138(18pp), 2025 June 20 Keith et al

2014
[81]

W., Keller, B

Wadsley, J. W., Keller, B. W., & Quinn, T. R. 2017,MNRAS ,471, 2357

2017
[82]

W., Stadel, J., & Quinn, T

Wadsley, J. W., Stadel, J., & Quinn, T. 2004,NewA ,9, 137

2004
[83]

G., Mateo, M., & Olszewski, E

Walker, M. G., Mateo, M., & Olszewski, E. W. 2009,AJ,137, 3100

2009
[84]

2019,MNRAS ,485, 2083

Wang, L., Xu, D., Gao, L., et al. 2019,MNRAS ,485, 2083

2019
[85]

H., Abel, T., Turk, M

Wise, J. H., Abel, T., Turk, M. J., Norman, M. L., & Smith, B. D. 2012, MNRAS ,427, 311

2012
[86]

L., & Randall, L

Xu, W. L., & Randall, L. 2020,ApJ,900, 69

2020
[87]

Yadav, N., Mukherjee, D., Sharma, P., & Nath, B. B. 2017,MNRAS , 465, 1720

2017
[88]

Zavala, J., Okamoto, T., & Frenk, C. S. 2008,MNRAS ,387, 364

2008
[89]

Y., Gnedin, N

Zemp, M., Gnedin, O. Y., Gnedin, N. Y., & Kravtsov, A. V. 2011,ApJS, 197, 30

2011
[90]

R., Faber, S

Zhang, H., Primack, J. R., Faber, S. M., et al. 2019,MNRAS ,484, 5170

2019
[91]

M., Willman, B., et al

Zolotov, A., Brooks, A. M., Willman, B., et al. 2012,ApJ,761, 71 18 The Astrophysical Journal, 986:138(18pp), 2025 June 20 Keith et al

2012

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

V., Leitner, S

Agertz, O., Kravtsov, A. V., Leitner, S. N., & Gnedin, N. Y. 2013,ApJ, 770, 25

2013

[2] [2]

I., et al

Agertz, O., Pontzen, A., Read, J. I., et al. 2020,MNRAS ,491, 1656

2020

[3] [3]

A., Primack, J

Allgood, B., Flores, R. A., Primack, J. R., et al. 2006,MNRAS ,367, 1781

2006

[4] [4]

M., Christensen, C

Applebaum, E., Brooks, A. M., Christensen, C. R., et al. 2021,ApJ,906, 96

2021

[5] [5]

2024,ApJ,970, 40

Azartash-Namin, B., Engelhardt, A., Munshi, F., et al. 2024,ApJ,970, 40

2024

[6] [6]

P., Bendek, E., Monacelli, B., et al

Bailey, V. P., Bendek, E., Monacelli, B., et al. 2023,Proc. SPIE,12680, 126800T

2023

[7] [7]

2005,ApJ,627, 647

Bailin, J., & Steinmetz, M. 2005,ApJ,627, 647

2005

[8] [8]

M., Cleary, C

Bellovary, J. M., Cleary, C. E., Munshi, F., et al. 2019,MNRAS ,482, 2913

2019