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The intricate link between galaxy dynamics and intrinsic shape (or why so-called prolate rotation is a misnomer)

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper claims that the assumed galaxy shape–rotation relation fails in Illustris, making 'prolate rotation' a misnomer.

desk verdict A clear conference summary of an already-published Illustris result; the plotted trend is plausible, but the paper's strong conclusion about 'little constraining power' is not backed by quantitative analysis. read the letter →

arxiv 1908.08648 v1 pith:6J7ZKM3M submitted 2019-08-23 astro-ph.GA

Caroline Foster , Robert Bassett This is my paper · ORCID
classification astro-ph.GA
keywords galaxies:kinematicsanddynamicsstructurestatisticsfundamentalparametersintrinsicshapeskinematicmisalignmentprolaterotationIllustris
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 tests a widely used assumption in galaxy-shape recovery: that a galaxy's intrinsic shape is tightly related to the angle between its rotation axis and its morphological axes, the intrinsic kinematic misalignment $\Psi_{\rm int}$. Using 978 galaxies from the Illustris cosmological simulation, the authors measure ellipsoid-equivalent shapes and angular momentum vectors directly in three dimensions. They find that the assumed relationship does not hold: most oblate and triaxial galaxies are aligned ($\Psi_{\rm int}\simeq 0$), whereas prolate and spherical galaxies scatter widely in $\Psi_{\rm int}$, with no preference for the $\Psi_{\rm int}\simeq 90^\circ$ configuration commonly called 'prolate rotation'. If real galaxies behave like Illustris, then published intrinsic-shape distributions inferred from integral-field spectroscopy using this relation are unreliable, and the phrase 'prolate rotation' should be abandoned.

What carries the argument

The central object is the intrinsic kinematic misalignment angle $\Psi_{\rm int}$, defined as the angle between the galaxy's angular momentum vector and the major axis of its ellipsoid-equivalent shape, with $\Psi_{\rm int}=90^\circ$ corresponding to rotation around the major axis. The shape itself is measured with an iterative reduced inertia tensor calculation on stellar particles inside the half-mass radius, giving axis ratios $p=b/a$ and $q=c/a$; galaxies are sorted into spherical, oblate, prolate, and triaxial classes from these ratios, and the triaxiality $T=(1-p^2)/(1-q^2)$ is computed. The argument is a direct comparison: the measured $T$–$\Psi_{\rm int}$ scatter in Illustris is checked against the analytic curve that earlier work derived from Stäckel-potential models.

What would settle it

One could falsify the central claim by showing that real IFS galaxies satisfy the $\Psi_{\rm int}$–shape relation: for example, if a large observed sample of galaxies classified as prolate by an independent method shows projected major-axis rotation near $\Psi_{\rm int}\simeq 90^\circ$ as predicted by the analytic relation, the Illustris-based conclusion would fail. A second check would be measuring $\Psi_{\rm int}$ distributions in galaxies from another independent cosmological simulation: if that simulation reproduces the analytic relation, the result is simulation-specific rather than general.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that the analytic relationship between triaxiality $T$ and intrinsic kinematic misalignment $\Psi_{\rm int}$ suggested in earlier theoretical work is not reproduced in Illustris. For oblate and triaxial galaxies the angular momentum vector almost always lies close to the morphological minor axis, giving $\Psi_{\rm int}\simeq 0^\circ$; for prolate and spherical galaxies $\Psi_{\rm int}$ spans the full range from $0^\circ$ to $90^\circ$. In particular, prolate galaxies do not preferentially rotate around their major axis, so the term 'prolate rotation' misdescribes their kinematics. Consequently, $\Psi_{\rm int}$ carries very little information about intrinsic shape, and methods that use this angle to constrain the distribution of galaxy intrinsic shapes are not on solid ground.

Load-bearing premise

The argument depends on Illustris being a faithful stand-in for real galaxies: if the simulated population's angular momentum content or shape distribution differs from the observed Universe, the conclusion that observational shape-recovery is biased does not necessarily follow.

Editorial extensions

If this is right

  • Published intrinsic shape distributions inferred from IFS surveys such as ATLAS3D, SAMI, MANGA, and MASSIVE that rely on the $\Psi_{\rm int}$–shape relation may be systematically biased, because a key prior in the inference is invalid.
  • Kinematic maps alone cannot break the shape degeneracy: $\Psi_{\rm int}$ should be treated as a weak or uninformative constraint rather than a sharp predictor.
  • Prolate galaxies should not be assumed to rotate around their projected major axis; classification schemes that equate 'prolate rotation' with a particular $\Psi_{\rm int}$ value need revision.
  • Forward-modeling simulated galaxies through observational selection effects would be a safer route to infer intrinsic shape distributions than the analytic relation.

Reading between the lines

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

  • If the Illustris result is representative, earlier observational shape-recovery results that used the $\Psi_{\rm int}$ relation likely underestimate the fraction of misaligned prolate galaxies; re-analyzing existing IFS data with simulation-calibrated priors would test this.
  • The same test could be run in other independent cosmological simulations to see whether the breakdown of the $\Psi_{\rm int}$–shape relation is a generic prediction of galaxy formation physics or specific to Illustris.
  • A useful observable extension would be to compare the projected distribution of kinematic misalignment angles predicted by simulating Illustris snapshots with observed IFS samples; a mismatch would help refine the feedback or angular momentum implementations.
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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 978 galaxies from the Illustris cosmological simulation at z=0 to measure intrinsic ellipsoidal axis ratios (p, q) and the intrinsic kinematic misalignment angle Psi_int between the stellar rotation vector and the morphological major axis. The authors compare the joint distribution of triaxiality and Psi_int with the analytical relation suggested by Weijmans et al. (2014). They report that most oblate and triaxial Illustris galaxies have Psi_int near 0 degrees, while prolate and spherical galaxies scatter broadly in Psi_int, with no preference for Psi_int near 90 degrees. From this they conclude that Psi_int holds little constraining power for recovering galaxy intrinsic shape distributions, and that the term 'prolate rotation' is a misnomer.

Significance. The question addressed is timely and important: several large IFS surveys (ATLAS3D, SAMI, MANGA, MASSIVE) have used the assumed relationship between kinematics and intrinsic shape to statistically recover shape distributions, so a demonstration that this relationship fails would have direct practical impact. The paper's strengths are that it uses a large, well-defined simulated sample with a clear stellar-particle selection threshold, an iterative reduced-inertia-tensor shape measurement, and a direct visual comparison to an external analytical relation. However, as written the paper provides only a scatter plot as evidence; there is no quantitative test of constraining power, no uncertainty estimate, and no validation of the simulation against observed misalignment distributions. If the central claim were supported by quantitative inference tests, the result would be significant for the interpretation of IFS shape-recovery studies.

major comments (4)
  1. [§3, Fig. 1 (right panel)] The conclusion that 'Psi_int holds little constraining power when inferring the distribution of galaxy intrinsic shapes' is not established by the scatter plot alone. A variable with a noisy or non-monotonic joint distribution can still be informative; for example, the strong clustering of oblate and triaxial galaxies at Psi_int near 0 degrees is itself a constraint that separates them from prolate and spherical galaxies. The paper should provide a quantitative measure of constraining power, such as mutual information, classification accuracy, or a likelihood-based comparison of P(shape | Psi_int), or a mock shape-recovery experiment that measures how well the true shape distribution is recovered with and without the assumed relation.
  2. [§3] The statement that the failure of the Weijmans et al. (2014) relation 'affects our ability to recover accurate intrinsic shape distributions' is an inference that goes beyond the figure. Standard forward-modeling methods, such as those used by Foster et al. (2017), marginalize over the full joint distribution of intrinsic shape and misalignment; a broken one-to-one relation does not automatically imply a biased recovered shape distribution. The authors should demonstrate the claimed bias explicitly, for instance by applying a shape-recovery method to simulated galaxies with known intrinsic shapes and comparing the inferred distribution with and without the assumed relation.
  3. [§2.2, Eq. (2.4)] The text states that Psi_int is 'the angle between the short axis of the equivalent ellipsoid and the stellar angular rotation vector', but Eq. (2.4) defines it using the angle between the rotation vector and the major axis e1. This is a direct inconsistency in the definition of the central quantity of the paper. Please reconcile the text and equation, and also explain how the major axis is determined robustly for spherical galaxies, whose major-axis direction is stochastic, since those galaxies contribute to the scatter shown in Figure 1.
  4. [§2–§3] The paper draws observational implications from Illustris without validating that the simulation reproduces the observed distribution of kinematic misalignments. The conclusion that real shape-recovery methods are biased depends on Illustris being a faithful proxy for galaxy angular-momentum content and shapes, especially at the low-mass, prolate end. Please include a comparison of the projected kinematic misalignment distribution of the simulated sample with observed IFS samples (e.g., ATLAS3D, SAMI, or MANGA), or otherwise justify that the simulated dynamics are representative, before generalizing to observational surveys.
minor comments (5)
  1. [Abstract] The phrase 'Many recent integral integral field spectroscopy' contains a duplicated word 'integral'; please correct.
  2. [Eq. (2.3)] The notation 'Ln ⃗ vn' appears to be a typesetting error; it should likely be the luminosity L_n times the velocity vector \vec{v}_n. Please clarify the vector notation in this equation.
  3. [Fig. 1] The right-hand panel would benefit from error bars or at least a statement of the typical measurement uncertainty on Psi_int and triaxiality, since the visual scatter is compared qualitatively with the Weijmans et al. relation.
  4. [References] The reference to Mendez-Abreu (2016) is malformed ('ASSL, 15, ASSL..418'); please provide the full bibliographic entry.
  5. [§3] The functional form of the Weijmans et al. (2014) relation that is drawn as a red dashed line is not specified in the text; please state the equation or describe the relation quantitatively so that the comparison in Figure 1 is reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical comparison against an external analytical relation, with only a non-load-bearing self-citation.

full rationale

This is an empirical comparison, not a derivation. The paper measures p, q, T, and Ψ_int directly from Illustris simulation particles using Eqs. (2.1)–(2.4), then compares these measurements to the relationship from Weijmans et al. (2014), which is an external, independent source based on Stäckel-potential models. No parameter is fitted to the target conclusion; the conclusion that prolate galaxies scatter in Ψ_int follows from the plotted joint distribution. The definition of Ψ_int relative to the major axis is a stated modeling choice (Section 2.2), not an input that encodes the result. The only self-reference is to Bassett & Foster (2019), the original paper containing the same Illustris analysis; that citation is provenance for this proceedings summary and is not load-bearing in the argument. The claim that Ψ_int holds 'little constraining power' is an extrapolation from a scatter plot and may be under-supported—that is a correctness or inference-strength concern, not circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the realism of Illustris, the single-ellipsoid approximation, and the appropriateness of the Weijmans et al. baseline. The only hand-chosen numeric input is the shape classification thresholds; no new physical entities are introduced.

free parameters (1)
  • Shape classification thresholds = p-q = 0.2; p = 0.8
    Adopted from Li et al. (2018) to divide galaxies into spherical, oblate, prolate, and triaxial classes. The claim that prolate galaxies show a wide Psi_int range depends on which galaxies are classified as prolate.
assumptions (4)
  • domain assumption Illustris simulations approximate real galaxy dynamics closely enough to draw conclusions about observed galaxies.
    Section 2 uses Illustris-1 and Section 3 generalizes the failure of the relation to observational shape recovery, which only follows if the simulation is a faithful proxy.
  • domain assumption Galaxy stellar distributions can be approximated by a single ellipsoid-equivalent shape.
    Section 2.1 explicitly ignores multiple components and fits one ellipsoid; the kinematic misalignment is measured relative to this ellipsoid.
  • domain assumption The Weijmans et al. (2014) relation, derived for Stäckel-potential elliptical galaxies, is the appropriate baseline for IFS galaxy samples.
    Section 3 contrasts Illustris with this relation; if the relation was never intended for all galaxy types, the failure is less of a surprise.
  • standard math Eigenvalue decomposition of the reduced inertia tensor recovers the intrinsic axes.
    Section 2.1 uses Iij eigen-decomposition, a standard method from Allgood et al. (2006).

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Cite this review

Pith. "Pith review of The intricate link between galaxy dynamics and intrinsic shape (or why so-called prolate rotation is a misnomer)." pith.science (2026). https://pith.science/paper/6J7ZKM3M

@misc{pith2026190808648,
  author       = {Pith},
  title        = {Pith review of: The intricate link between galaxy dynamics and intrinsic shape (or why so-called prolate rotation is a misnomer)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6J7ZKM3M}},
  note         = {Machine review of arXiv:1908.08648}
}
read the original abstract

Many recent integral integral field spectroscopy (IFS) survey teams have used stellar kinematic maps combined with imaging to statistically infer the underlying distributions of galaxy intrinsic shapes. With now several IFS samples at our disposal, the method, which was originally proposed by M. Franx and collaborators in 1991, is gaining in popularity, having been so far applied to ATLAS3D, SAMI, MANGA and MASSIVE. We present results showing that a commonly assumed relationship between dynamical and intrinsic shape alignment does not hold in Illustris, affecting our ability to recover accurate intrinsic shape distributions. A further implication is that so-called "prolate rotation", where the bulk of stars in prolate galaxies are thought to rotate around the projected major axis, is a misnomer.

Figures

Figures reproduced from arXiv: 1908.08648 by the authors.

Figure 1
Figure 1. LHS: Distribution of intrinsic axis ratios (p = b/a, q = c/a) for Illustris galaxies. Dashed lines delineate spherical, oblate, triaxial, and prolate as per Li et al. (2018). RHS: The distribution of triaxiality (T) vs intrinsic kinematic misalignment (Ψint). Plotted symbols and colours indicate shape subclasses as labelled. The red dashed line represents the relationship suggested in Weijmans et al. (2014) [PITH_F… view at source ↗

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Works this paper leans on

91 extracted references · 75 canonical work pages · cited by 1 Pith paper

  1. [1]

    A., Primack J

    Allgood B., Flores R. A., Primack J. R., Kravtsov A. V., Wechsler R. H., Faltenbacher A., Bullock J. S., 2006, MNRAS, 367, 1781

    2006

  2. [2]

    T., Hunter C., 1994, MNRAS, 271, 924

    Arnold R., de Zeeuw P. T., Hunter C., 1994, MNRAS, 271, 924

    1994

  3. [3]

    Astropy Collaboration, et al., 2013, A&A, 558, A33

    2013

  4. [4]

    S., 2000, AJ, 120, 110

    Bak J., Statler T. S., 2000, AJ, 120, 110

    2000

  5. [5]

    Bassett R., Bekki K., Cortese L., Couch W., 2017, MNRAS, 471, 1892

    2017

  6. [6]

    Bassett R., Foster C., 2019, MNRAS, 487, 2354

    2019

  7. [7]

    Benacchio L., Galletta G., 1980, MNRAS, 193, 885

    1980

  8. [8]

    Binney J., 1978, ComAp, 8, 27

    1978

Show all 91 references
  1. [9]

    Binney J., de Vaucouleurs G., 1981, MNRAS, 194, 679

    1981

  2. [10]

    Binney J., 1985, MNRAS, 212, 767

    1985

  3. [11]

    J., et al., 2019, MNRAS, 483, 458

    Bryant J. J., et al., 2019, MNRAS, 483, 458

    2019

  4. [12]

    N., 2012, MNRAS, 425, 44

    Cameron E., Pettitt A. N., 2012, MNRAS, 425, 44

    2012

  5. [13]

    Cappellari M., et al., 2011, MNRAS, 416, 1680

    2011

  6. [14]

    Contopoulos G., 1956, ZA, 39, 126

    1956

  7. [15]

    M., et al., 2012, MNRAS, 421, 872

    Croom S. M., et al., 2012, MNRAS, 421, 872

    2012

  8. [16]

    de Zeeuw T., 1985, MNRAS, 216, 273

    1985

  9. [17]

    de Zeeuw T., Franx M., 1989, ApJ, 343, 617

    1989

  10. [18]

    L., 2018, MNRAS, 476, 3661

    Dom \' nguez S \'a nchez H., Huertas-Company M., Bernardi M., Tuccillo D., Fischer J. L., 2018, MNRAS, 476, 3661

    2018

  11. [19]

    L., 2015, ApJ, 813, 10

    Ebrov \'a I., okas E. L., 2015, ApJ, 813, 10

    2015

  12. [20]

    L., 2017, ApJ, 850, 144

    Ebrov \'a I., okas E. L., 2017, ApJ, 850, 144

    2017

  13. [21]

    Emsellem E., Krajnovic D., Sarzi M., 2014, MNRAS, 445, L79

    2014

  14. [22]

    Emsellem E., et al., 2011, MNRAS, 414, 888

    2011

  15. [23]

    Ene I., et al., 2018, MNRAS, 479, 2810

    2018

  16. [24]

    Fasano G., et al., 2010, MNRAS, 404, 1490

    2010

  17. [25]

    Fasano G., Vio R., 1991, MNRAS, 249, 629

    1991

  18. [26]

    P., 2008, MNRAS, 384, 449

    Feroz F., Hobson M. P., 2008, MNRAS, 384, 449

    2008

  19. [27]

    Foster C., et al., 2017, MNRAS, 472, 966

    2017

  20. [28]

    Foster C., et al., 2016, MNRAS, 457, 147

    2016

  21. [29]

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

    1991

  22. [30]

    Garrison-Kimmel S., et al., 2018, MNRAS, 481, 4133

    2018

  23. [31]

    Genel S., , 2014, MNRAS, 445, 175

    2014

  24. [32]

    W., Roberts M

    Goad J. W., Roberts M. S., 1981, ApJ, 250, 79

    1981

  25. [33]

    J., et al., 2014, arXiv, arXiv:1406.2661

    Goodfellow I. J., et al., 2014, arXiv, arXiv:1406.2661

    2014 arXiv

  26. [34]

    W., et al., 2018, MNRAS, 475, 716

    Green A. W., et al., 2018, MNRAS, 475, 716

    2018

  27. [35]

    E., Power C., Robotham A

    Harborne K. E., Power C., Robotham A. S. G., Cortese L., Taranu D. S., 2019, MNRAS, 483, 249

    2019

  28. [36]

    P., van der Wel A., Rix H.-W., Franx M., 2012, ApJ, 749, 96

    Holden B. P., van der Wel A., Rix H.-W., Franx M., 2012, ApJ, 749, 96

    2012

  29. [37]

    T., 1992, ApJ, 389, 79

    Hunter C., de Zeeuw P. T., 1992, ApJ, 389, 79

    1992

  30. [38]

    Jacobs C., Glazebrook K., Collett T., More A., McCarthy C., 2017, MNRAS, 471, 167

    2017

  31. [39]

    Jennings E., Madigan M., 2017, A&C, 19, 16

    2017

  32. [40]

    Jesseit R., Cappellari M., Naab T., Emsellem E., Burkert A., 2009, MNRAS, 397, 1202

    2009

  33. [41]

    D., Karachentseva V

    Karachentsev I. D., Karachentseva V. E., Kudrya Y. N., Sharina M. E., Parnovskij S. L., 1999, BSAO, 47, 5

    1999

  34. [42]

    K., 2007, ApJ, 670, 1048

    Kimm T., Yi S. K., 2007, ApJ, 670, 1048

    2007

  35. [43]

    T., Copin Y., 2006, MNRAS, 366, 787

    Krajnovi \'c D., Cappellari M., de Zeeuw P. T., Copin Y., 2006, MNRAS, 366, 787

    2006

  36. [44]

    J., Emsellem E., 2016, MNRAS, 455, 3680

    Li H., Li R., Mao S., Xu D., Long R. J., Emsellem E., 2016, MNRAS, 455, 3680

    2016

  37. [45]

    T., Emsellem E., Long R

    Li H., Mao S., Cappellari M., Graham M. T., Emsellem E., Long R. J., 2018, ApJL, 863, L19

    2018

  38. [46]

    Li H., Mao S., Emsellem E., Xu D., Springel V., Krajnovi \'c D., 2018, MNRAS, 473, 1489

    2018

  39. [47]

    Lintott C., , 2011, MNRAS, 410, 166

    2011

  40. [48]

    J., et al., 2008, MNRAS, 389, 1179

    Lintott C. J., et al., 2008, MNRAS, 389, 1179

    2008

  41. [49]

    K., Wong O

    Lukic V., Br \"u ggen M., Banfield J. K., Wong O. I., Rudnick L., Norris R. P., Simmons B., 2018, MNRAS, 476, 246

    2018

  42. [50]

    E., McConnell N., Janish R., Blakeslee J

    Ma C.-P., Greene J. E., McConnell N., Janish R., Blakeslee J. P., Thomas J., Murphy J. D., 2014, ApJ, 795, 158

    2014

  43. [51]

    Marinacci F., Pakmor R., Springel V., 2014, MNRAS, 437, 1750

    2014

  44. [52]

    E., Romanowsky A

    Moody C. E., Romanowsky A. J., Cox T. J., Novak G. S., Primack J. R., 2014, MNRAS, 444, 1475

    2014

  45. [53]

    M \'e ndez-Abreu J., 2016, ASSL, 15, ASSL..418

    2016

  46. [54]

    Naab T., Burkert A., 2003, ApJ, 597, 893

    2003

  47. [55]

    Naab T., et al., 2014, MNRAS, 444, 3357

    2014

  48. [56]

    Nelson D., , 2015, A&C, 13, 12

    2015

  49. [57]

    D., Strauss M

    Padilla N. D., Strauss M. A., 2008, MNRAS, 388, 1321

    2008

  50. [58]

    Pourrahmani M., Nayyeri H., Cooray A., 2018, ApJ, 856, 68

    2018

  51. [59]

    T., Harrison I., Abdalla F

    Rivi M., Lochner M., Balan S. T., Harrison I., Abdalla F. B., 2019, MNRAS, 482, 1096

    2019

  52. [60]

    Robotham A. S. G., Davies L. J. M., Driver S. P., Koushan S., Taranu D. S., Casura S., Liske J., 2018, MNRAS, 476, 3137

    2018

  53. [61]

    D., 2013, MNRAS, 434, 2153

    Rodr \' guez S., Padilla N. D., 2013, MNRAS, 434, 2153

    2013

  54. [62]

    D., Garc \' a Lambas D., 2016, MNRAS, 456, 571

    Rodr \' guez S., Padilla N. D., Garc \' a Lambas D., 2016, MNRAS, 456, 571

    2016

  55. [63]

    S., Lauer T

    Ryden B. S., Lauer T. R., Postman M., 1993, ApJ, 410, 515

    1993

  56. [64]

    S., 1996, ApJ, 461, 146

    Ryden B. S., 1996, ApJ, 461, 146

    1996

  57. [65]

    S., 2006, ApJ, 641, 773

    Ryden B. S., 2006, ApJ, 641, 773

    2006

  58. [66]

    C., Stokes N

    Sandage A., Freeman K. C., Stokes N. R., 1970, ApJ, 160, 831

    1970

  59. [67]

    Schaller M., et al., 2015, MNRAS, 452, 343

    2015

  60. [68]

    Scott N., et al., 2018, MNRAS, 481, 2299

    2018

  61. [69]

    F., et al., 2006, AJ, 131, 1163

    Skrutskie M. F., et al., 2006, AJ, 131, 1163

    2006

  62. [70]

    F., et al., 2015, MNRAS, 454, 1886

    Snyder G. F., et al., 2015, MNRAS, 454, 1886

    2015

  63. [71]

    Springel V., 2010, MNRAS, 401, 791

    2010

  64. [72]

    S., 1994, ApJ, 425, 458

    Statler T. S., 1994, ApJ, 425, 458

    1994

  65. [73]

    S., Fry A

    Statler T. S., Fry A. M., 1994, ApJ, 425, 481

    1994

  66. [74]

    S., 1994, ApJ, 425, 500

    Statler T. S., 1994, ApJ, 425, 500

    1994

  67. [75]

    S., et al., 2013, MNRAS, 436, 625

    Stinson G. S., et al., 2013, MNRAS, 436, 625

    2013

  68. [76]

    S \'a nchez-Janssen R., M \'e ndez-Abreu J., Aguerri J. A. L., 2010, MNRAS, 406, L65

    2010

  69. [77]

    S \'a nchez-Janssen R., , 2016, ApJ, 820, 69

    2016

  70. [78]

    S., Dubinski J., Yee H

    Taranu D. S., Dubinski J., Yee H. K. C., 2013, ApJ, 778, 61

    2013

  71. [79]

    D., Dopita M

    Thomas A. D., Dopita M. A., Kewley L. J., Groves B. A., Sutherland R. S., Hopkins A. M., Blanc G. A., 2018, ApJ, 856, 89

    2018

  72. [80]

    Tsatsi A., Lyubenova M., van de Ven G., Chang J., Aguerri J. A. L., Falc \'o n-Barroso J., Macci \`o A. V., 2017, A&A, 606, A62

    2017

  73. [81]

    van de Sande J., , 2018, NatAs, 2, 483

    2018

  74. [82]

    A., Ryden B

    Vincent R. A., Ryden B. S., 2005, ApJ, 623, 137

    2005

  75. [83]

    Vogelsberger M., , 2014, Natur, 509, 177

    2014

  76. [84]

    Vogelsberger M., , 2014, MNRAS, 444, 1518

    2014

  77. [85]

    Wang Y., Mao S., Li H., Xu D., Chen X., Springel V., 2019, MNRAS, 483, 3048

    2019

  78. [86]

    Weijmans A.-M., , 2014, MNRAS, 444, 3340

    2014

  79. [87]

    F., Boada S., 2019, MNRAS, 484, 4683

    Wu J. F., Boada S., 2019, MNRAS, 484, 4683

    2019

  80. [88]

    Xu D., et al., 2017, MNRAS, 469, 1824

    2017

  81. [89]

    G., et al., 2000, AJ, 120, 1579

    York D. G., et al., 2000, AJ, 120, 1579

    2000

  82. [90]

    M., Williams L

    Young A. M., Williams L. L. R., Hjorth J., 2018, JCAP, 2018, 033

    2018

  83. [91]

    Zhang M., Zheng Y., 2018, arXiv, arXiv:1811.06229

    2018 arXiv

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