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arxiv: 2604.03389 · v1 · submitted 2026-04-03 · 🌌 astro-ph.GA

Recognition: 2 theorem links

· Lean Theorem

The Role of Baryonic and Dark Matter in Bar Kinematics

Tobias G\'eron , Kai Zhu , Steph Campbell , Chris Lintott , R. J. Smethurst , Behzad Tahmasebzadeh
Authors on Pith no claims yet

Pith reviewed 2026-05-13 18:08 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords galactic barsbar pattern speeddark matterstellar massMaNGA surveyTremaine-Weinberg methodJeans modelsNFW profile
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The pith

Bars rotate more slowly in galaxies with higher stellar and total mass.

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

Simulations predict galactic bars should slow down over time by transferring angular momentum to the outer disc and dark matter halo. This study tests the prediction with direct measurements in 30 MaNGA galaxies, pairing Tremaine-Weinberg bar pattern speeds with dynamical mass estimates from Jeans anisotropic models. The observations show a statistically significant anti-correlation exceeding 4 sigma between bar pattern speed and both stellar mass and total dynamical mass. The trend with dark matter mass is negative but falls short of the same significance threshold, while slower bars also appear in systems with more extended NFW halos of lower central density.

Core claim

The paper reports a statistically significant anti-correlation (>4 sigma) between bar pattern speed and both stellar mass and total dynamical mass in 30 MaNGA galaxies, indicating that the slowest bars reside in the most massive systems. The slope with dark matter mass is negative but reaches only 2.43 sigma. Bars with lower pattern speeds show more extended NFW dark matter profiles with lower central densities, and corotation radius correlates positively with stellar, dark matter, and total mass at >3 sigma significance. No significant relations appear with dark matter fraction or the R parameter.

What carries the argument

Bar pattern speed measured by the Tremaine-Weinberg method on stellar velocity fields, compared to host galaxy stellar and dynamical masses obtained from Jeans anisotropic modeling.

If this is right

  • Slower bars are found in galaxies with higher stellar mass.
  • Total dynamical mass shows a similar anti-correlation with bar pattern speed.
  • Corotation radius increases with stellar mass, dark matter mass, and total dynamical mass.
  • Bars with lower pattern speeds have more extended NFW dark matter profiles with lower central densities.

Where Pith is reading between the lines

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

  • If confirmed in larger samples, bar kinematics could serve as an indirect probe of dark matter halo structure across galaxy populations.
  • Simulations of bar evolution may need to reproduce this specific mass dependence to match the observed angular momentum transfer rates.
  • Future integral-field surveys could test whether the weaker dark matter mass trend strengthens once measurement uncertainties on halo parameters are reduced.

Load-bearing premise

The Tremaine-Weinberg method accurately recovers true bar pattern speeds in the selected MaNGA galaxies without major biases from non-steady-state effects, dust, or other dynamical complications.

What would settle it

A larger sample of galaxies with independently measured bar pattern speeds that shows no anti-correlation with stellar or total dynamical mass would falsify the reported relation.

Figures

Figures reproduced from arXiv: 2604.03389 by Behzad Tahmasebzadeh, Chris Lintott, Kai Zhu, R. J. Smethurst, Steph Campbell, Tobias G\'eron.

Figure 1
Figure 1. Figure 1: An overview of the consistency thresholds on the JAM models used in this work. We compare the JAM model outputs assuming a spherically-aligned velocity ellipsoid (Xsph) and cylindrically-aligned velocity ellipsoid (Xcyl) for the stellar mass (left panel), dark matter mass (second panel), total dynamical mass (third panel), and dark matter fraction (fourth panel). The rightmost panel shows a comparison betw… view at source ↗
Figure 2
Figure 2. Figure 2: A comparison of the bar pattern speed (left panel), corotation radius (middle panel), and R (right panel) for the final sample used in this paper (blue) and all the TW galaxies that pass the quality thresholds (gray) The median values of both samples is denoted by the dashed and dotted lines, respectively. The p-value of an Anderson-Darling test is shown in the top-right corner of each panel, and is > 0.25… view at source ↗
Figure 4
Figure 4. Figure 4: We plot the different bar kinematics parameters (Ωb, left column; RCR, middle column; R, right column) against the different JAM parameters (log (M∗,Re), top row; log (MDM,Re), top middle row; log (MT,Re), bottom middle row; fDM,Re, bottom row). The significance of each correlation was tested with a Spearman test, and its p-value is shown in the top-right corner of each plot. We also fit a line through the… view at source ↗
Figure 5
Figure 5. Figure 5: We show the NFW halo parameters rs and log (ρs) against each other. We additionally color each point based on its bar pattern speed (leftmost panel), corotation radius (middle left panel), R (middle right panel), and total dynamical mass (rightmost panel). The galaxies with the highest values of Ωb tend to have high values of log (ρs) and low values of rs, while this is inverted for the corotation radius. … view at source ↗
read the original abstract

Simulations predict that bars in galaxies should slow down over time. This is often attributed to the exchange of angular momentum between the bar and other regions of the galaxy, such as the outer disc and dark matter halo, which implies that galaxies with a more massive halo or disc should be able to slow down the bar more efficiently. However, observational evidence for this process has been limited. In this work, we provide observational support for the slowing down of bars as predicted by simulations. We combine bar kinematics measurements obtained with the Tremaine-Weinberg method and host galaxy mass estimates derived from Jeans anisotropic models for a sample of 30 galaxies from the MaNGA survey. We find a statistically significant anti-correlation (>4sigma) between the bar pattern speed and both the stellar and total dynamical mass, which suggests that the slowest bars reside in the most massive galaxies. However, while the slope of the best-fit line between the pattern speed and dark matter mass is negative, it is not statistically significant (2.43sigma). We also find that bars with lower pattern speeds have more extended NFW dark matter profiles with lower central densities. Additionally, we find statistically significant correlations (>3sigma) between the corotation radius and the stellar mass, dark matter mass, and total dynamical mass. Finally, we find no significant correlations that involve the dark matter fraction or R, likely due to the inherent challenges associated with measuring these specific parameters accurately.

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

3 major / 2 minor

Summary. The paper analyzes bar pattern speeds measured via the Tremaine-Weinberg method and galaxy masses from Jeans anisotropic models in a sample of 30 MaNGA galaxies. It reports a >4σ anti-correlation between bar pattern speed and both stellar and total dynamical mass (suggesting slower bars in more massive systems), a weaker 2.43σ trend with dark matter mass, correlations (>3σ) between corotation radius and the mass measures, and no significant trends with dark matter fraction or R.

Significance. If robust, the result supplies direct observational evidence supporting simulation predictions that bars lose angular momentum to the disc and halo, thereby slowing over time; the mass-dependent trend and NFW profile correlations would help bridge the gap between theory and observation in bar dynamics.

major comments (3)
  1. [Section 3 (Tremaine-Weinberg measurements)] The central >4σ anti-correlation with stellar mass (and the weaker DM result) rests on TW-derived pattern speeds; the manuscript does not present quantitative tests (e.g., mock IFU data with mass-dependent dust or non-steady flows) to show that any mass-dependent bias in recovered Ω_bar is smaller than the observed trend.
  2. [Section 4.2 (correlation results)] The reported 2.43σ DM-mass correlation versus >4σ stellar-mass correlation is load-bearing for the claim that halo drag is the dominant mechanism; the paper should propagate the full covariance between stellar and DM mass estimates from the JAM modeling into the correlation significance.
  3. [Section 2 (sample selection)] With N=30, the selection function for MaNGA barred galaxies (bar strength, inclination, S/N) must be shown not to correlate with mass; otherwise the anti-correlation could arise from Malmquist-like bias rather than dynamical evolution.
minor comments (2)
  1. [Figure 3] Figure 3 (pattern-speed vs. mass panels) lacks error bars on the x-axis (mass uncertainties) and does not indicate the Spearman or Pearson coefficient with its p-value.
  2. [Abstract] The abstract states significance levels but does not specify the exact correlation coefficient or test; this should be added for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed report. We address each major comment below and will revise the manuscript to strengthen the analysis and robustness of the results.

read point-by-point responses

  1. Referee: [Section 3 (Tremaine-Weinberg measurements)] The central >4σ anti-correlation with stellar mass (and the weaker DM result) rests on TW-derived pattern speeds; the manuscript does not present quantitative tests (e.g., mock IFU data with mass-dependent dust or non-steady flows) to show that any mass-dependent bias in recovered Ω_bar is smaller than the observed trend.

    Authors: We agree that explicit validation against mass-dependent systematics in the TW method would strengthen the central result. The revised manuscript will include quantitative tests using mock IFU data that incorporate mass-dependent dust extinction and non-steady flows, confirming that any resulting bias in recovered Ω_bar remains smaller than the observed >4σ trend. revision: yes

  2. Referee: [Section 4.2 (correlation results)] The reported 2.43σ DM-mass correlation versus >4σ stellar-mass correlation is load-bearing for the claim that halo drag is the dominant mechanism; the paper should propagate the full covariance between stellar and DM mass estimates from the JAM modeling into the correlation significance.

    Authors: We accept that the full covariance between stellar and dark matter mass estimates from the JAM models must be propagated to obtain accurate correlation significances. The revised Section 4.2 will incorporate the complete covariance matrix from the JAM fits, which will refine the reported 2.43σ and >4σ values and provide a more robust basis for interpreting the relative roles of stellar and halo drag. revision: yes

  3. Referee: [Section 2 (sample selection)] With N=30, the selection function for MaNGA barred galaxies (bar strength, inclination, S/N) must be shown not to correlate with mass; otherwise the anti-correlation could arise from Malmquist-like bias rather than dynamical evolution.

    Authors: We agree that verifying the absence of mass-dependent selection effects is essential for a sample of this size. The revised manuscript will include an explicit check demonstrating that bar strength, inclination, and S/N show no significant correlation with galaxy mass within the MaNGA barred-galaxy selection, thereby indicating that the observed anti-correlation is not driven by Malmquist-like bias. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results from independent data and standard methods

full rationale

The paper applies the established Tremaine-Weinberg method to MaNGA IFU observations for bar pattern speeds and uses Jeans anisotropic models for dynamical masses, then reports statistical correlations between these independently measured quantities. No derivation step reduces a claimed prediction to a fitted input by construction, no load-bearing self-citation chain is present, and no ansatz or uniqueness claim is smuggled in via prior author work. The central anti-correlation findings are direct statistical outputs from the survey data rather than tautological re-expressions of the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the validity of the Tremaine-Weinberg method for pattern speed measurement and Jeans anisotropic models for dynamical mass estimates, plus the assumption that NFW profiles describe the dark matter distribution.

axioms (2)
  • domain assumption Tremaine-Weinberg method assumes a steady-state bar pattern speed and integrates line-of-sight velocities correctly.
    Invoked to obtain pattern speeds from MaNGA integral-field data.
  • domain assumption Jeans anisotropic models accurately recover stellar and total dynamical masses from observed kinematics.
    Used to derive host galaxy mass estimates.

pith-pipeline@v0.9.0 · 5578 in / 1283 out tokens · 51871 ms · 2026-05-13T18:08:29.730581+00:00 · methodology

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Works this paper leans on

89 extracted references · 89 canonical work pages

  1. [1]

    Aguerri, J. A. L., M´ endez-Abreu, J., & Corsini, E. M. 2009, A&A, 495, 491, doi: 10.1051/0004-6361:200810931

    work page doi:10.1051/0004-6361:200810931 2009

  2. [2]

    Aguerri, J. A. L., M´ endez-Abreu, J., Falc´ on-Barroso, J., et al. 2015, A&A, 576, A102, doi: 10.1051/0004-6361/201423383 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aab...

    work page doi:10.1051/0004-6361/201423383 2015

  3. [3]

    1992, MNRAS, 259, 345, doi: 10.1093/mnras/259.2.345 11 https://linmix.readthedocs.io/en/latest/index.html

    Athanassoula, E. 1992, MNRAS, 259, 345, doi: 10.1093/mnras/259.2.345 11 https://linmix.readthedocs.io/en/latest/index.html

    work page doi:10.1093/mnras/259.2.345 1992

  4. [4]

    and Copin, Y

    Athanassoula, E. 2003, MNRAS, 341, 1179, doi: 10.1046/j.1365-8711.2003.06473.x

    work page doi:10.1046/j.1365-8711.2003.06473.x 2003

  5. [5]

    Athanassoula, E., Machado, R. E. G., & Rodionov, S. A. 2013, MNRAS, 429, 1949, doi: 10.1093/mnras/sts452

    work page doi:10.1093/mnras/sts452 2013

  6. [6]

    D., Jogee, S., & Marinova, I

    Barazza, F. D., Jogee, S., & Marinova, I. 2008, ApJ, 675, 1194, doi: 10.1086/526510

    work page doi:10.1086/526510 2008

  7. [7]

    2023, ApJ, 953, 173, doi: 10.3847/1538-4357/ace2b9

    Beane, A., Hernquist, L., D’Onghia, E., et al. 2023, ApJ, 953, 173, doi: 10.3847/1538-4357/ace2b9

    work page doi:10.3847/1538-4357/ace2b9 2023

  8. [8]

    1987, Galactic dynamics

    Binney, J., & Tremaine, S. 1987, Galactic dynamics

    work page 1987

  9. [9]

    Overview of the SDSS-IV MaNGA Survey: Mapping Nearby Galaxies at Apache Point Observatory

    Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7, doi: 10.1088/0004-637X/798/1/7

    work page Pith review doi:10.1088/0004-637x/798/1/7 2015

  10. [10]

    J., Verdes-Montenegro, L., Damas-Segovia, A., et al

    Buta, R. J., Verdes-Montenegro, L., Damas-Segovia, A., et al. 2019, MNRAS, 488, 2175, doi: 10.1093/mnras/stz1780

    work page doi:10.1093/mnras/stz1780 2019

  11. [11]

    M., Aguerri, J

    Buttitta, C., Corsini, E. M., Aguerri, J. A. L., et al. 2023, MNRAS, 521, 2227, doi: 10.1093/mnras/stad646 The Role of Baryonic and Dark Matter in Bar Kinematics13

    work page doi:10.1093/mnras/stad646 2023

  12. [12]

    M., 2002, @doi [ ] 10.1046/j.1365-8711.2002.05848.x , http://adsabs.harvard.edu/abs/2002MNRAS.336.1188K 336, 1188

    Cappellari, M. 2002, MNRAS, 333, 400, doi: 10.1046/j.1365-8711.2002.05412.x

    work page doi:10.1046/j.1365-8711.2002.05412.x 2002

  13. [13]

    and Wilkinson, M

    Cappellari, M. 2008, MNRAS, 390, 71, doi: 10.1111/j.1365-2966.2008.13754.x

    work page doi:10.1111/j.1365-2966.2008.13754.x 2008

  14. [14]

    2020, MNRAS, 494, 4819, doi: 10.1093/mnras/staa959 Cervantes Sodi, B

    Cappellari, M. 2020, MNRAS, 494, 4819, doi: 10.1093/mnras/staa959 Cervantes Sodi, B. 2017, ApJ, 835, 80, doi: 10.3847/1538-4357/835/1/80

    work page doi:10.1093/mnras/staa959 2020

  15. [15]

    L., et al

    Cheung, E., Athanassoula, E., Masters, K. L., et al. 2013, ApJ, 779, 162, doi: 10.1088/0004-637X/779/2/162

    work page doi:10.1088/0004-637x/779/2/162 2013

  16. [16]

    1980, A&A, 81, 198

    Contopoulos, G. 1980, A&A, 81, 198

    work page 1980

  17. [17]

    1981, A&A, 102, 265

    Contopoulos, G. 1981, A&A, 102, 265

    work page 1981

  18. [18]

    1997, AJ, 114, 2402, doi: 10.1086/118656

    Courteau, S. 1997, AJ, 114, 2402, doi: 10.1086/118656

    work page doi:10.1086/118656 1997

  19. [19]

    H., Buttitta, C., et al

    Cuomo, V., Lee, Y. H., Buttitta, C., et al. 2021, A&A, 649, A30, doi: 10.1051/0004-6361/202040261

    work page doi:10.1051/0004-6361/202040261 2021

  20. [20]

    A., Corsini, E

    Cuomo, V., Lopez Aguerri, J. A., Corsini, E. M., et al. 2019, A&A, 632, A51, doi: 10.1051/0004-6361/201936415

    work page doi:10.1051/0004-6361/201936415 2019

  21. [21]

    Cuomo, V., Morelli, L., Aguerri, J. A. L., et al. 2024, MNRAS, 527, 11218, doi: 10.1093/mnras/stad3945

    work page doi:10.1093/mnras/stad3945 2024

  22. [22]

    2004, A&A, 416, 515, doi: 10.1051/0004-6361:20031726 de S´ a-Freitas, C., Gadotti, D

    Davoust, E., & Contini, T. 2004, A&A, 416, 515, doi: 10.1051/0004-6361:20031726 de S´ a-Freitas, C., Gadotti, D. A., Fragkoudi, F., et al. 2025, A&A, 698, A5, doi: 10.1051/0004-6361/202453367

    work page doi:10.1051/0004-6361:20031726 2004

  23. [23]

    P., & Sellwood, J

    Debattista, V. P., & Sellwood, J. A. 2000, ApJ, 543, 704, doi: 10.1086/317148

    work page doi:10.1086/317148 2000

  24. [24]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d

    work page doi:10.3847/1538-3881/ab089d 2019

  25. [25]

    A., & Macci` o, A

    Dutton, A. A., & Macci` o, A. V. 2014, MNRAS, 441, 3359, doi: 10.1093/mnras/stu742

    work page doi:10.1093/mnras/stu742 2014

  26. [26]

    1994, A&A, 285, 723

    Emsellem, E., Monnet, G., & Bacon, R. 1994, A&A, 285, 723

    work page 1994

  27. [27]

    2019, MNRAS, 489, 3553, doi: 10.1093/mnras/stz2363

    Erwin, P. 2019, MNRAS, 489, 3553, doi: 10.1093/mnras/stz2363

    work page doi:10.1093/mnras/stz2363 2019

  28. [28]

    B., Frogel, J

    Eskridge, P. B., Frogel, J. A., Pogge, R. W., et al. 2000, AJ, 119, 536, doi: 10.1086/301203

    work page doi:10.1086/301203 2000

  29. [29]

    2015, MNRAS, 454, 3641, doi: 10.1093/mnras/stv2247

    Fanali, R., Dotti, M., Fiacconi, D., & Haardt, F. 2015, MNRAS, 454, 3641, doi: 10.1093/mnras/stv2247

    work page doi:10.1093/mnras/stv2247 2015

  30. [30]

    2016, MNRAS, 462, L41, doi: 10.1093/mnrasl/slw120

    Fragkoudi, F., Athanassoula, E., & Bosma, A. 2016, MNRAS, 462, L41, doi: 10.1093/mnrasl/slw120

    work page doi:10.1093/mnrasl/slw120 2016

  31. [31]

    2022, ApJ, 940, 61, doi: 10.3847/1538-4357/ac9972

    Frankel, N., Pillepich, A., Rix, H.-W., et al. 2022, ApJ, 940, 61, doi: 10.3847/1538-4357/ac9972

    work page doi:10.3847/1538-4357/ac9972 2022

  32. [32]

    2020, MNRAS, 499, 1116, doi: 10.1093/mnras/staa2866

    Fraser-McKelvie, A., Merrifield, M., Arag´ on-Salamanca, A., et al. 2020, MNRAS, 499, 1116, doi: 10.1093/mnras/staa2866

    work page doi:10.1093/mnras/staa2866 2020

  33. [33]

    Gadotti, D. A. 2011, MNRAS, 415, 3308, doi: 10.1111/j.1365-2966.2011.18945.x

    work page doi:10.1111/j.1365-2966.2011.18945.x 2011

  34. [34]

    A., Willett, K

    Galloway, M. A., Willett, K. W., Fortson, L. F., et al. 2015, MNRAS, 448, 3442, doi: 10.1093/mnras/stv235

    work page doi:10.1093/mnras/stv235 2015

  35. [35]

    L., Walmsley, M., Silcock, M

    Garland, I. L., Walmsley, M., Silcock, M. S., et al. 2024, MNRAS, 532, 2320, doi: 10.1093/mnras/stae1620

    work page doi:10.1093/mnras/stae1620 2024

  36. [36]

    2020, MNRAS, 491, 3655, doi: 10.1093/mnras/stz3101

    Garma-Oehmichen, L., Cano-D´ ıaz, M., Hern´ andez-Toledo, H., et al. 2020, MNRAS, 491, 3655, doi: 10.1093/mnras/stz3101

    work page doi:10.1093/mnras/stz3101 2020

  37. [37]

    2022, MNRAS, 517, 5660, doi: 10.1093/mnras/stac3069

    Garma-Oehmichen, L., Hern´ andez-Toledo, H., Aquino-Ort´ ız, E., et al. 2022, MNRAS, 517, 5660, doi: 10.1093/mnras/stac3069

    work page doi:10.1093/mnras/stac3069 2022

  38. [38]

    2019, A&A, 621, L4, doi: 10.1051/0004-6361/201834500 G´ eron, T

    George, K., Joseph, P., Mondal, C., et al. 2019, A&A, 621, L4, doi: 10.1051/0004-6361/201834500 G´ eron, T. 2023,, v1.0 Zenodo, doi: 10.5281/zenodo.7567945 G´ eron, T., Smethurst, R. J., Lintott, C., et al. 2021, MNRAS, 507, 4389, doi: 10.1093/mnras/stab2064 G´ eron, T., Smethurst, R. J., Lintott, C., et al. 2024, ApJ, 973, 129, doi: 10.3847/1538-4357/ad6...

    work page doi:10.1051/0004-6361/201834500 2019

  39. [39]

    P., Kent, B

    Giovanelli, R., Haynes, M. P., Kent, B. R., et al. 2005, AJ, 130, 2598, doi: 10.1086/497431

    work page doi:10.1086/497431 2005

  40. [40]

    2019, MNRAS, 482, 1733, doi: 10.1093/mnras/sty2715

    Guo, R., Mao, S., Athanassoula, E., et al. 2019, MNRAS, 482, 1733, doi: 10.1093/mnras/sty2715

    work page doi:10.1093/mnras/sty2715 2019

  41. [41]

    2024, arXiv e-prints, arXiv:2409.06100

    Guo, Y., Jogee, S., Wise, E., et al. 2024, arXiv e-prints, arXiv:2409.06100. https://arxiv.org/abs/2409.06100

    work page arXiv 2024

  42. [42]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    work page doi:10.1038/s41586-020-2649-2 2020

  43. [43]

    , archivePrefix = "arXiv", eprint =

    Hoyle, B., Masters, K. L., Nichol, R. C., et al. 2011, MNRAS, 415, 3627, doi: 10.1111/j.1365-2966.2011.18979.x

    work page doi:10.1111/j.1365-2966.2011.18979.x 2011

  44. [44]

    K., Combes, F., Garc´ ıa-Burillo, S., et al

    Hunt, L. K., Combes, F., Garc´ ıa-Burillo, S., et al. 2008, A&A, 482, 133, doi: 10.1051/0004-6361:20078874

    work page doi:10.1051/0004-6361:20078874 2008

  45. [45]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    work page doi:10.1109/mcse.2007.55 2007

  46. [46]

    Jeans, J. H. 1922, MNRAS, 82, 122, doi: 10.1093/mnras/82.3.122

    work page doi:10.1093/mnras/82.3.122 1922

  47. [47]

    2025a, A&A, 704, A262, doi: 10.1051/0004-6361/202557236

    Jin, Y., Zhu, L., Tahmasebzadeh, B., et al. 2025a, A&A, 704, A262, doi: 10.1051/0004-6361/202557236

    work page doi:10.1051/0004-6361/202557236

  48. [48]

    2025b, A&A, 700, A249, doi: 10.1051/0004-6361/202555378

    Jin, Y., Zhu, L., Tahmasebzadeh, B., et al. 2025b, A&A, 700, A249, doi: 10.1051/0004-6361/202555378

    work page doi:10.1051/0004-6361/202555378

  49. [49]

    Jogee, S., Scoville, N., & Kenney, J. D. P. 2005, ApJ, 630, 837, doi: 10.1086/432106

    work page doi:10.1086/432106 2005

  50. [50]

    A., & Mandel, E

    Joye, W. A., & Mandel, E. 2003, in Astronomical Society of the Pacific Conference Series, Vol. 295, Astronomical Data Analysis Software and Systems XII, ed. H. E

    work page 2003

  51. [51]

    K., & Das, M

    Kataria, S. K., & Das, M. 2019, ApJ, 886, 43, doi: 10.3847/1538-4357/ab48f7

    work page doi:10.3847/1538-4357/ab48f7 2019

  52. [52]

    Kelly, B. C. 2007, ApJ, 665, 1489, doi: 10.1086/519947

    work page doi:10.1086/519947 2007

  53. [53]

    J., Lintott, C

    Kruk, S. J., Lintott, C. J., Bamford, S. P., et al. 2018, MNRAS, 473, 4731, doi: 10.1093/mnras/stx2605 14G ´eron et al

    work page doi:10.1093/mnras/stx2605 2018

  54. [54]

    2012 , month = apr, journal =

    Lablanche, P.-Y., Cappellari, M., Emsellem, E., et al. 2012, MNRAS, 424, 1495, doi: 10.1111/j.1365-2966.2012.21343.x Le Conte, Z. A., Gadotti, D. A., Ferreira, L., et al. 2024, MNRAS, 530, 1984, doi: 10.1093/mnras/stae921

    work page doi:10.1111/j.1365-2966.2012.21343.x 2012

  55. [55]

    2023, MNRAS, 526, 1972, doi: 10.1093/mnras/stad2799

    Li, X., Shlosman, I., Heller, C., & Pfenniger, D. 2023, MNRAS, 526, 1972, doi: 10.1093/mnras/stad2799

    work page doi:10.1093/mnras/stad2799 2023

  56. [56]

    2024, MNRAS, 527, 11026, doi: 10.1093/mnras/stad3907

    Li, X., Shlosman, I., Pfenniger, D., & Heller, C. 2024, MNRAS, 527, 11026, doi: 10.1093/mnras/stad3907

    work page doi:10.1093/mnras/stad3907 2024

  57. [57]

    2023, MNRAS, 526, 1022, doi: 10.1093/mnras/stad2732

    Lu, S., Zhu, K., Cappellari, M., et al. 2023, MNRAS, 526, 1022, doi: 10.1093/mnras/stad2732

    work page doi:10.1093/mnras/stad2732 2023

  58. [58]

    Lynden-Bell, D., & Kalnajs, A. J. 1972, MNRAS, 157, 1, doi: 10.1093/mnras/157.1.1

    work page doi:10.1093/mnras/157.1.1 1972

  59. [59]

    2007, ApJ, 659, 1176, doi: 10.1086/512355

    Marinova, I., & Jogee, S. 2007, ApJ, 659, 1176, doi: 10.1086/512355

    work page doi:10.1086/512355 2007

  60. [60]

    2010, Monthly Notices of the Royal Astronomical Society, 408, 1181, doi: 10.1111/j.1365-2966.2010.17197.x

    Masters, K. L., Nichol, R. C., Hoyle, B., et al. 2011, MNRAS, 411, 2026, doi: 10.1111/j.1365-2966.2010.17834.x

    work page doi:10.1111/j.1365-2966.2010.17834.x 2011

  61. [61]

    2012 , month = apr, journal =

    Masters, K. L., Nichol, R. C., Haynes, M. P., et al. 2012, MNRAS, 424, 2180, doi: 10.1111/j.1365-2966.2012.21377.x

    work page doi:10.1111/j.1365-2966.2012.21377.x 2012

  62. [62]

    L., G´ eron, T., D’Onghia, E., et al

    McClure, R. L., G´ eron, T., D’Onghia, E., et al. 2025, arXiv e-prints, arXiv:2506.09150, doi: 10.48550/arXiv.2506.09150

    work page doi:10.48550/arxiv.2506.09150 2025

  63. [63]

    Blok, W. J. G. 2000, ApJL, 533, L99, doi: 10.1086/312628 Men´ endez-Delmestre, K., Sheth, K., Schinnerer, E., Jarrett, T. H., & Scoville, N. Z. 2007, ApJ, 657, 790, doi: 10.1086/511025

    work page doi:10.1086/312628 2000

  64. [64]

    Monthly Notices of the Royal Astronomical Society , volume =

    Moster, B. P., Naab, T., & White, S. D. M. 2013, MNRAS, 428, 3121, doi: 10.1093/mnras/sts261

    work page doi:10.1093/mnras/sts261 2013

  65. [65]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., & White, S. D. M. 1996, ApJ, 462, 563, doi: 10.1086/177173

    work page doi:10.1086/177173 1996

  66. [66]

    2025, Research Notes of the American Astronomical Society, 9, 236, doi: 10.3847/2515-5172/ae0305 Planck Collaboration, Aghanim, N., Akrami, Y., et al

    Pearlstein, T., Masters, K., & G´ eron, T. 2025, Research Notes of the American Astronomical Society, 9, 236, doi: 10.3847/2515-5172/ae0305 Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6, doi: 10.1051/0004-6361/201833910

    work page doi:10.3847/2515-5172/ae0305 2025

  67. [67]

    J., & Lintott, C

    Puczek, N., G´ eron, T., Smethurst, R. J., & Lintott, C. J. 2025, arXiv e-prints, arXiv:2511.02054, doi: 10.48550/arXiv.2511.02054

    work page doi:10.48550/arxiv.2511.02054 2025

  68. [68]

    and Wilkinson, M

    Rautiainen, P., Salo, H., & Laurikainen, E. 2008, MNRAS, 388, 1803, doi: 10.1111/j.1365-2966.2008.13522.x

    work page doi:10.1111/j.1365-2966.2008.13522.x 2008

  69. [69]

    2021, MNRAS, 508, 926, doi: 10.1093/mnras/stab2553

    Roshan, M., Ghafourian, N., Kashfi, T., et al. 2021, MNRAS, 508, 926, doi: 10.1093/mnras/stab2553

    work page doi:10.1093/mnras/stab2553 2021

  70. [70]

    Salpeter, E. E. 1955, ApJ, 121, 161, doi: 10.1086/145971

    work page doi:10.1086/145971 1955

  71. [71]

    F., Capozziello, S., & Dainotti, M

    Scott, N., Cappellari, M., Davies, R. L., et al. 2009, MNRAS, 398, 1835, doi: 10.1111/j.1365-2966.2009.15275.x

    work page doi:10.1111/j.1365-2966.2009.15275.x 2009

  72. [72]

    L., et al

    Scott, N., Cappellari, M., Davies, R. L., et al. 2013, MNRAS, 432, 1894, doi: 10.1093/mnras/sts422

    work page doi:10.1093/mnras/sts422 2013

  73. [73]

    Sellwood, J. A. 1981, A&A, 99, 362

    work page 1981

  74. [74]

    Sellwood, J. A. 2008, ApJ, 679, 379, doi: 10.1086/586882

    work page doi:10.1086/586882 2008

  75. [75]

    2024, A&A, 692, A159, doi: 10.1051/0004-6361/202451521

    Semczuk, M., Dehnen, W., Sch¨ onrich, R., & Athanassoula, E. 2024, A&A, 692, A159, doi: 10.1051/0004-6361/202451521

    work page doi:10.1051/0004-6361/202451521 2024

  76. [76]

    Teuben, P. J. 2005, ApJ, 632, 217, doi: 10.1086/432409

    work page doi:10.1086/432409 2005

  77. [77]

    M., Elmegreen, B

    Sheth, K., Elmegreen, D. M., Elmegreen, B. G., et al. 2008, ApJ, 675, 1141, doi: 10.1086/524980

    work page doi:10.1086/524980 2008

  78. [78]

    2012 , month = apr, journal =

    Skibba, R. A., Masters, K. L., Nichol, R. C., et al. 2012, MNRAS, 423, 1485, doi: 10.1111/j.1365-2966.2012.20972.x

    work page doi:10.1111/j.1365-2966.2012.20972.x 2012

  79. [79]

    A., Matsuda, T., & Fujimoto, M

    Sorensen, S. A., Matsuda, T., & Fujimoto, M. 1976, Ap&SS, 43, 491, doi: 10.1007/BF00640025

    work page doi:10.1007/bf00640025 1976

  80. [80]

    2017, MNRAS, 465, 3729, doi: 10.1093/mnras/stw2934

    Spinoso, D., Bonoli, S., Dotti, M., et al. 2017, MNRAS, 465, 3729, doi: 10.1093/mnras/stw2934

    work page doi:10.1093/mnras/stw2934 2017

Showing first 80 references.