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What Determines the Sizes of Bars in Spiral Galaxies?

T0 review · 2 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Bar size in spiral galaxies is bimodal in stellar mass and is set mostly by the disc scale length, with an extra mass dependence only in the most massive galaxies.

desk verdict New bimodal bar–mass scaling from S4G with a real but covariance-vulnerable residual mass term; worth refereeing, but the high-mass claim needs a correlated-error test. read the letter →

arxiv 1908.08423 v2 pith:A45JIJFM submitted 2019-08-22 astro-ph.GA

Peter Erwin (1) ((1) Max-Planck-Institut fuer extraterrestrische Physik , Garching , Germany) This is my paper · ORCID
classification astro-ph.GA
keywords galaxybarsbarsizesspiralgalaxiesstellarmassdiscscalelengthscalingrelationsdetectabilityS4G
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 argues that bar size in spiral galaxies is not a single smooth function of stellar mass. It is nearly constant below about $10^{10.1}\,M_\odot$ ($a \propto M_\star^{0.1}$) and steepens sharply above it ($a \propto M_\star^{0.6}$). Bar size correlates most tightly with the exponential disc scale length ($a \propto h^{0.8}$), and the correlation with stellar mass at low masses is just a side effect of bigger galaxies having bigger discs. Above the break mass, however, stellar mass matters in its own right: at fixed galaxy size, the more massive galaxy has the longer bar. Because bar size controls whether distant surveys can detect bars at all, this bimodal scaling affects measurements of bar frequency at high redshift as well as theoretical models of bar growth.

What carries the argument

The load-bearing element is a broken-linear (bimodal) power-law fit in the $\log a_{\rm vis}$--$\log M_\star$ plane, extended to multi-variable fits of $\log a_{\rm vis}$ against $\log M_\star$ plus $\log R_e$ or $\log h$ (Eqns. 1 and 4). The break mass $M_{\rm brk}$ is a free parameter, so the bimodality is not imposed by hand; it emerges from the data and is checked by bootstrap resampling, Akaike information criterion comparisons, and bootstrap estimates of prediction error. Residual plots of the size-only fits against stellar mass give the decisive visual evidence that an extra mass term appears only above the break.

What would settle it

Re-fit the bar-size relations using independent size and mass estimates for the same galaxies (for example, effective radii from optical imaging or masses from dynamics rather than photometry) and check whether the residual slope of bar size versus stellar mass at fixed size remains about $0.35$--$0.47$ for $\log (M_\star/M_\odot) > 10.1$; if it drops to zero, the extra mass dependence is a correlated-error artifact.

Watch

Extended reading notes

Core claim

The central discovery, stated on the paper's own terms, is that the bar-size--stellar-mass relation in local disc galaxies is bimodal, with a break at $\log (M_\star/M_\odot) \simeq 10.1$--$10.2$: below the break $a_{\rm vis} \propto M_\star^{0.1}$, above it $a_{\rm vis} \propto M_\star^{0.6}$. Bar size is an even stronger function of galaxy size, $a_{\rm vis} \propto R_e^{0.45}$ and $a_{\rm vis} \propto h^{0.8}$, and the multi-variable fits of Table 4 show that once both size and mass are included, the residual mass slope below the break is consistent with zero while above the break it remains significant ($\beta_2 \simeq 0.35$--$0.47$). The author reads this as evidence that disc scale length is the primary structural determinant of bar size, that a separate mass-driven growth channel operates only in galaxies above roughly $10^{10.1}\,M_\odot$, and that the classical correlations with gas fraction and Hubble type are not independent of mass and size.

Load-bearing premise

The analysis treats stellar mass, half-light radius, and disc scale length as error-free predictors, even though all three are measured from the same near-infrared images of the same galaxies; if correlated measurement errors generate the residual high-mass dependence of bar size on mass, the most novel conclusion would not survive.

Editorial extensions

If this is right

  • Bar-detectability corrections for large or high-redshift surveys cannot assume a single bar-size--mass scaling; the break at $\log (M_\star/M_\odot) \simeq 10.1$ means low-mass and high-mass bars fade out at different rates with distance.
  • Disc scale length, not total stellar mass, is the primary structural control on bar size, so models of bar formation and growth should aim to reproduce $a \propto h^{0.8}$.
  • Above the break, a more massive galaxy of the same size will host a longer bar, pointing to a mass-dependent growth process that operates only in the most massive discs.
  • The absence of any residual bar-size dependence on present-day gas fraction or Hubble type means those classical correlations are not separate physical drivers.
  • Barred galaxies are more extended than unbarred galaxies of the same mass, and larger bars mark larger discs, so bar presence and bar size can improve predictions of galaxy size from stellar mass.

Reading between the lines

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

  • If the bimodal relation is real, then measurements of bar fraction versus redshift need a mass-dependent visibility correction that itself may evolve with the break mass; a constant-size detection threshold will bias high-redshift samples toward the most massive bars.
  • The $a \propto h^{0.8}$ scaling invites a direct test in simulations: bar length should be a predictable multiple of the initial disc scale length, and mergers or disc heating that change $h$ should change bar size in the same proportion.
  • A concrete check that goes beyond the paper's fits is to redo the multi-variable fits with independent sizes and masses (for example, optical effective radii and dynamical masses); this would separate a physical mass effect from correlated photometric errors.
  • The null gas-fraction result does not necessarily mean gas never slows bar growth; it may mean present-day atomic gas fraction is a poor proxy for the gas content at the epoch of bar formation, a distinction simulations with time-evolving gas fractions could settle.
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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

2 major / 4 minor

Summary. The paper presents an analysis of bar sizes (deprojected semi-major axes) in the S4G sample of nearby spiral galaxies, using volume- and mass-limited subsamples to measure how bar size depends on stellar mass, galaxy half-light radius, disc scale length, gas fraction, and Hubble type. The central claims are: (i) the bar-size--stellar-mass relation is bimodal, being nearly flat (slope ~0.1) below log M* ~ 10.1 and steep (slope ~0.6) above; (ii) bar size correlates more strongly with disc scale length (slope ~0.8) and half-light radius (slope ~0.45) than with stellar mass; (iii) after accounting for galaxy size, an additional residual dependence on stellar mass persists only for galaxies above the break mass, with slopes 0.35 (with h) and 0.47 (with Re) from the multivariate fits in Eq. (4) and Table 4; (iv) bar size shows no residual dependence on gas mass fraction or Hubble type after mass/size corrections; and (v) galaxy size can be modeled as a function of stellar mass, bar presence, and bar size. The analysis uses careful sample selection, bootstrap uncertainties, AIC comparison, and out-of-sample prediction errors, and the code and data are publicly available.

Significance. If the central claims hold, this is a valuable empirical contribution to understanding bar formation and growth: it identifies disc scale length as the dominant predictor of bar size and reveals a high-mass residual mass dependence that is not explained by galaxy size alone. The bimodality of the bar-size--mass relation, if real, is a new scaling relation that any model of bar evolution must reproduce, and the observed absence of a present-day gas-fraction dependence constrains theoretical expectations. The paper is careful in its sample selection (volume/mass-limited, inclination cuts), uses bootstrap resampling for parameter uncertainties, compares models with AIC and out-of-sample prediction errors, and ships reproducible code and data. These are genuine strengths. However, the novelty and importance of the result rest heavily on the high-mass residual mass dependence, which is the most vulnerable to systematic uncertainties in the measured predictors.

major comments (2)
  1. [Section 3.3, Eq. (4), Table 4] The central new result -- that bar size depends on stellar mass even after controlling for Re or h at log M* > 10.1 (beta2 = 0.35 +/- 0.06 with h, 0.47 +/- 0.09 with Re) -- is not robustly established because the predictor variables M*, Re, and h are all measured from the same S4G 3.6 micron images (Muse et al. 2015; Salo et al. 2015) and are treated as error-free. Correlated measurement errors among M*, Re, and h (e.g., a fluctuation that raises both the inferred stellar mass and the inferred disc scale length) can generate a spurious positive residual slope of bar size versus M* after removing h or Re. The 2000 bootstrap resamples resample galaxies, not measurement-noise realizations, so they cannot detect this effect. I request an explicit test: either propagate the published uncertainties on M*, Re, and h (including their covariance), or run a Monte Carlo simulation that adds correlated noise to these predictors and show that the recovered beta2 remains consistent with the reported values. Until this is done, the high-mass residual mass dependence should be treated as tentative rather than established, and this caveat should be stated in the abstract and summary.
  2. [Section 3.1, Eq. (1), Table 2] The bimodality of the bar-size--stellar-mass relation is quantified with a broken-linear fit that has an abrupt change in slope, and the model comparison is only between a single straight line and this broken linear form. A smooth transition (e.g., a bent power law with a finite transition width, or a low-order spline) might fit the data as well, which would change the physical interpretation from a distinct break to a gradual steepening. Moreover, the reported break mass (log M* ~ 10.1-10.2) is close to the break seen in the size--mass relation itself (Appendix A), so part of the apparent bimodality could be a projection of that underlying relation. The multivariate fits in Section 3.3 partially address this concern, but the paper should either test alternative functional forms or explicitly justify why an abrupt broken line is the physically preferred model.
minor comments (4)
  1. [Section 2.1.1] The assumption of a constant 10% fractional uncertainty on bar sizes is used to compute all AIC values. Since the same uncertainty is used for all fits, it does not affect relative AIC comparisons among the models, but the absolute AIC values are not meaningful if the true uncertainty differs substantially. It would be useful to state this explicitly and perhaps quote qualitative conclusions based on AIC deltas rather than absolute values.
  2. [Section 4.1] The statement that 'there is very little correlation' (Spearman r = 0.08, P = 0.11) is slightly undercut by the weak turn-up for very gas-rich galaxies that is mentioned in the same paragraph; the paper correctly notes this is in the opposite sense from the models, but the wording should be tightened to avoid appearing inconsistent.
  3. [Section 5, Eq. (5-6)] In the fits of galaxy size versus stellar mass and bar size, the binary variable B is defined as 0 for unbarred and 1 for barred galaxies. The model is fitted to all galaxies in the Main Spiral Sample, but for unbarred galaxies the bar size is undefined; it would be helpful to clarify that the product B * log(avis) is set to zero for unbarred galaxies, as the mathematical notation in Eq. (5-6) alone does not make this explicit.
  4. [Throughout] The paper is generally well written, but there are minor typos and formatting issues (e.g., 'Insitut' in the author affiliation, and some figure captions that use 'fi' ligatures). These do not affect the science.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central scaling relations are empirical fits to independent S4G catalog data, not derivations that reduce to their own inputs.

full rationale

The paper's central results are empirical scaling relations fitted to published S4G catalog data, with stellar masses from Muñoz-Mateos et al. (2015), half-light radii and disc scale lengths from Salo et al. (2015), and bar sizes from Herrera-Endoqui et al. (2015). The bimodal slope values, break mass, and residual mass dependence are least-squares outputs, not inputs: the broken-linear relation in Eqn. 1 is motivated by LOESS and residual inspection and then fitted to the data, and the multi-variable model in Eqn. 4 is assessed against single-variable models using AIC and bootstrap validation. Bar size is not defined in terms of stellar mass or galaxy size, and the paper does not fit a parameter to one subset of data and then present a closely related quantity as an independent prediction. The bootstrap MSE computation is an honest internal predictive check, not a hidden reuse of fitted values. Self-citations to Paper I (Erwin 2018) are used for sample definitions, distance limits, and contextual arguments about bar detectability; they do not carry the scaling-law claims, which are re-derived from the catalog data and are consistent with prior independent work. Section 5's use of bar size to predict galaxy size is explicitly described by the paper as "a trivial inversion" of the empirical bar-size–galaxy-size relation, so it is not presented as an independent first-principles derivation and does not constitute circular reasoning. The possible correlation of measurement errors among M*, Re, h, and a_bar is a legitimate statistical robustness concern, but it is an error-propagation and correctness issue rather than a circularity of the derivation.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The work is a fitting analysis of published photometric and structural catalogs. The fitted slopes and break mass are the empirical findings themselves, not hidden inputs. Invented entities: none. The key unstated premise is that measurement errors in the predictor variables do not create the residual mass trend.

free parameters (4)
  • Slope of bar size vs stellar mass, low-mass branch (beta_1) = 0.10 +/- 0.04 (Parent Spiral)
    Fitted broken-linear slope for log a vs log M* below the break; it is the measured result, not an ad hoc input.
  • Slope of bar size vs stellar mass, high-mass branch (beta_2) = 0.59 +/- 0.08 (Parent Spiral)
    Fitted broken-linear slope above the break; drives the central bimodality claim.
  • Break mass (log M_brk/M_sun) = 10.16 +/- 0.07 (Parent Spiral)
    Fitted break position separating the two regimes; critical for the bimodality claim.
  • Slope of bar size vs disc scale length (beta_h) = 0.76 +/- 0.06
    Fitted slope for the strongest single-predictor relation.
assumptions (5)
  • domain assumption The S4G Parent Spiral Sample (D<=30 Mpc, log M*=9-11) is representative of local spiral galaxies after excluding S0s and inclined systems.
    The analysis relies on this subsample to avoid incompleteness; Section 2.
  • domain assumption Deprojected 'visual' bar semi-major axes from Herrera-Endoqui et al. (2015) are reliable measures of bar size; a constant 10% fractional uncertainty applies.
    Section 2.1.1; the assumed uncertainty anchors all AIC and prediction-error comparisons.
  • domain assumption Present-day atomic gas mass fraction is a meaningful (if imperfect) tracer of the gas content relevant to bar formation; molecular gas corrections would not change the null result.
    Section 4.1; the paper argues molecular gas would strengthen the disagreement with theory.
  • ad hoc to paper Measurement errors in M*, Re, and h are negligible relative to the trends, and correlations among these errors do not generate the residual mass dependence.
    Section 3.3 uses M*, Re, h from the same S4G imaging without propagating uncertainties; this is the weakest load-bearing premise.
  • ad hoc to paper The broken-linear functional form adequately captures the true bar-size-mass relation; the break is not an artifact of the fitting function.
    Section 3.1; the bimodality is modeled as two linear segments joined at a break.

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Cite this review

Pith. "Pith review of What Determines the Sizes of Bars in Spiral Galaxies?." pith.science (2026). https://pith.science/paper/A45JIJFM

@misc{pith2026190808423,
  author       = {Pith},
  title        = {Pith review of: What Determines the Sizes of Bars in Spiral Galaxies?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A45JIJFM}},
  note         = {Machine review of arXiv:1908.08423}
}
abstract

I use volume- and mass-limited subsamples and recently published data from the Spitzer Survey of Stellar Structure in Galaxies (S4G) to investigate how the size of bars depends on galaxy properties. The known correlation between bar semi-major-axis $a$ and galaxy stellar mass (or luminosity) is actually *bimodal*: for $\log M_{\star} < 10.1$, bar size is almost independent of stellar mass ($a \propto M_{\star}^{0.1}$), while it is a strong function for higher masses ($a \propto M_{\star}^{0.6}$). Bar size is a slightly stronger function of galaxy half-light radius $r_{e}$ and (especially) exponential disc scale length $h$ ($a \propto h^{0.8}$). Correlations between stellar mass and galaxy size can explain the bar-size--$M_{\star}$ correlation -- but only for galaxies with $\log M_{\star} < 10.1$; at higher masses, there is an extra dependence of bar size on $M_{\star}$ itself. Despite theoretical arguments that the presence of gas can affect bar growth, there is no evidence for any residual dependence of bar size on (present-day) gas mass fraction. The traditional dependence of bar size on Hubble type (longer bars in early-type discs) can be explained as a side-effect of stellar-mass--Hubble-type correlations. Finally, I show that galaxy size ($r_{e}$ or $h$) can be modeled as a function of stellar mass and both bar presence and bar size: barred galaxies tend to be more extended than unbarred galaxies of the same mass, with larger bars correlated with larger sizes.

Figures

Figures reproduced from arXiv: 1908.08423 by the authors.

Figure 1
Figure 1. Bar semi-major axis versus stellar mass for S4G spiral galaxies with D 6 30 Mpc. Top: data with LOESS fit (black line); boxes indicate binned mean values, with error bars showing standard deviations. Bottom: data with linear fit (thin, short-dashed blue line) and broken linear fit (thick, long-dashed red line); in both cases, the fit is to the Parent Spiral Sample (log (M?/M ) = 9–11), indicated by the darker data p… view at source ↗
Figure 2
Figure 2. As for the bottom panel in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Fits of bar size versus Re (left) and disc scale length h (right), along with residuals from each fit plotted against stellar mass (bottom two panels). Upper left: Bar size versus Re, with fit (dashed red line). Dark grey circles are galaxies in the Main Barred Spiral Sample, while light grey circles are additional barred S4G galaxies with masses below or above the log (M?/M ) = 9–11 limits of the Main Spiral Sample… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Bar size as a function of gas mass fraction for the Parent Barred Spiral Sample (grey and black points) and the Main Barred Spiral Sample (black points). The red line indicates a LOESS fits to the individual points in the Parent Barred Spiral Sample. This trend does re…
Figure 5
Figure 5. Figure 5: Residuals from the fit of bar size as a function of both stellar mass and Re (left panel) or stellar mass and disc scale length h (right panel), plotted against gas mass fraction. Red lines indicate LOESS fits to the residual points. The absence of any strong trends in…
Figure 6
Figure 6. Figure 6: Bar size as a function of Hubble type T for S4G spiral galaxies with D 6 30 Mpc; galaxies in the Parent Spiral Sam￾ple are indicated by darker data points. The red line indicates a LOESS fit to the latter data points. 6.2 Which Way(s) Does the Arrow of Causality Point?…
Figure 7
Figure 7. Figure 7: Residuals from the fit of bar size as a function of both stellar mass and Re (left panel) or stellar mass and disc scale length h (right panel), plotted against Hubble type T. Red lines indicate LOESS fits to the residual points. 1 10 Bar size avis [kpc] 1.0 0.5 0.0 0.…
Figure 8
Figure 8. Figure 8: Residuals of the galaxy-size–stellar-mass fit for Re (left) and for disc scale length h (right) as a function of bar size. (See Appendix A for details of these fits.) The dashed lines are simple linear fits to the residual data points. In both cases, there is a correla…
Figure 9
Figure 9. Figure 9: Galaxy size Re (left) or disc scale length h (right) as a function of stellar mass for S4G galaxies. Filled symbols are barred galaxies and open symbols are unbarred. The three lines represent LOESS fits to just the barred galaxies (blue line), just the unbarred galaxi…

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Works this paper leans on

53 extracted references · 44 canonical work pages

  1. [1]

    Aguerri J. A. L., M \'e ndez-Abreu J., Corsini E. M., 2009, , 495, 491

    work page 2009

  2. [2]

    G., et al., 2017, , 469, 1054

    Algorry D. G., et al., 2017, , 469, 1054

    work page 2017

  3. [3]

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

    2013

  4. [4]

    Athanassoula E., 2002, ApJL, 569, L83

    work page 2002

  5. [5]

    Athanassoula E., 2003, MNRAS, 341, 1179

    work page 2003

  6. [6]

    Athanassoula E., Machado R. E. G., Rodionov S. A., 2013, , 429, 1949

    work page 2013

  7. [7]

    H., Shlosman I., Fricke K

    Berentzen I., Heller C. H., Shlosman I., Fricke K. J., 1998, MNRAS, 300, 49

    work page 1998

  8. [8]

    H., 2007, ApJ, 666, 189

    Berentzen I., Shlosman I., Martinez-Valpuesta I., Heller C. H., 2007, ApJ, 666, 189

    work page 2007

Show all 53 references
  1. [9]

    Boselli A., Cortese L., Boquien M., Boissier S., Catinella B., Lagos C., Saintonge A., 2014, , 564, A66

    2014

  2. [10]

    Bournaud F., Combes F., Semelin B., 2005, , 364, L18

    2005

  3. [11]

    S., Valluri M., Shen J., Debattista V

    Brown J. S., Valluri M., Shen J., Debattista V. P., 2013, , 778, 151

    2013

  4. [12]

    J., et al., 2015, ApJS, 217, 32

    Buta R. J., et al., 2015, ApJS, 217, 32

    2015

  5. [13]

    Cappellari M., 2014, CAP\_LOESS\_1D & CAP\_LOESS\_2D: Recover mean trends from noisy data , Astrophysics Source Code Library

    2014

  6. [14]

    Cappellari M., et al., 2013, , 432, 1862

    2013

  7. [15]

    P., Sellwood J

    Debattista V. P., Sellwood J. A., 1998, , 493, L5

    1998

  8. [16]

    P., Sellwood J

    Debattista V. P., Sellwood J. A., 2000, , 543, 704

    2000

  9. [17]

    P., Mayer L., Carollo C

    Debattista V. P., Mayer L., Carollo C. M., Moore B., Wadsley J., Quinn T., 2006, ApJ, 645, 209

    2006

  10. [18]

    D \' az-Garc \' a S., Salo H., Laurikainen E., Herrera-Endoqui M., 2016a, , 587, A160

  11. [19]

    D \' az-Garc \' a S., Salo H., Laurikainen E., 2016b, , 596, A84

  12. [20]

    Dubinski J., Berentzen I., Shlosman I., 2009, , 697, 293

    2009

  13. [21]

    G., Elmegreen D

    Elmegreen B. G., Elmegreen D. M., 1985, , 288, 438

    1985

  14. [22]

    Erwin P., 2005, MNRAS, 364, 283

    2005

  15. [23]

    Erwin P., 2018, , 474, 5372

    2018

  16. [24]

    Gavazzi G., et al., 2015, , 580, A116

    2015

  17. [25]

    Springer Series in Statistics, New York: Springer

    Hastie T., Tibshirani R., Friedman J., 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Ed. . Springer Series in Statistics, New York: Springer

    2009

  18. [26]

    D., 1992, , 400, 80

    Hernquist L., Weinberg M. D., 1992, , 400, 80

    1992

  19. [27]

    Herrera-Endoqui M., D \' az-Garc \' a S., Laurikainen E., Salo H., 2015, A&A, 582, A86

    2015

  20. [28]

    Hohl F., 1971, , 168, 343

    1971

  21. [29]

    Holley-Bockelmann K., Weinberg M., Katz N., 2005, , 363, 991

    2005

  22. [30]

    Hoyle B., et al., 2011, , 415, 3627

    2011

  23. [31]

    Jones E., Oliphant T., Peterson P., et al., 2001--, SciPy : Open source scientific tools for Python , http://www.scipy.org/

    2001

  24. [32]

    D., Combes F., 2018, , 609, A60

    Khoperskov S., Haywood M., Di Matteo P., Lehnert M. D., Combes F., 2018, , 609, A60

    2018

  25. [33]

    H., Erroz-Ferrer S., Roa J., Bakos J., Cisternas M., Leaman R., Szymanek N., 2014, , 569, A91

    Knapen J. H., Erroz-Ferrer S., Roa J., Bakos J., Cisternas M., Leaman R., Szymanek N., 2014, , 569, A91

    2014

  26. [34]

    Kormendy J., 1979, ApJ, 227, 714

    1979

  27. [35]

    Kraljic K., Bournaud F., Martig M., 2012, ApJ, 757, 60

    2012

  28. [36]

    H., Peletier R

    Laine S., Shlosman I., Knapen J. H., Peletier R. F., 2002, , 567, 97

    2002

  29. [37]

    Lange R., et al., 2015, , 447, 2603

    2015

  30. [38]

    H., 2007, , 381, 401

    Laurikainen E., Salo H., Buta R., Knapen J. H., 2007, , 381, 401

    2007

  31. [39]

    Marinova I., Jogee S., 2007, , 659, 1176

    2007

  32. [40]

    Martin P., 1995, , 109, 2428

    1995

  33. [41]

    H., Scoville N

    Men \'e ndez-Delmestre K., Sheth K., Schinnerer E., Jarrett T. H., Scoville N. Z., 2007, ApJ, 657, 790

    2007

  34. [42]

    C., et al., 2015, , 219, 3

    Mu \ n oz-Mateos J. C., et al., 2015, , 219, 3

    2015

  35. [43]

    Salo H., et al., 2015, , 219, 4

    2015

  36. [44]

    A., 2013, , 432, L56

    S \'a nchez-Janssen R., Gadotti D. A., 2013, , 432, L56

    2013

  37. [45]

    J., White S

    Shen S., Mo H. J., White S. D. M., Blanton M. R., Kauffmann G., Voges W., Brinkmann J., Csabai I., 2003, , 343, 978

    2003

  38. [46]

    Sheth K., et al., 2010, , 122, 1397

    2010

  39. [47]

    M., Elmegreen B

    Sheth K., Melbourne J., Elmegreen D. M., Elmegreen B. G., Athanassoula E., Abraham R. G., Weiner B. J., 2012, , 758, 136

    2012

  40. [48]

    Spinoso D., Bonoli S., Dotti M., Mayer L., Madau P., Bellovary J., 2017, , 465, 3729

    2017

  41. [49]

    Valenzuela O., Klypin A., 2003, , 345, 406

    2003

  42. [50]

    Villa-Vargas J., Shlosman I., Heller C., 2010, , 719, 1470

    2010

  43. [51]

    Wegg C., Gerhard O., Portail M., 2015, , 450, 4050

    2015

  44. [52]

    D., Katz N., 2002, , 580, 627

    Weinberg M. D., Katz N., 2002, , 580, 627

    2002

  45. [53]

    D., Katz N., 2007, , 375, 460

    Weinberg M. D., Katz N., 2007, , 375, 460

    2007

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