pith. sign in

arxiv: 2606.28590 · v1 · pith:CQYL7HTQnew · submitted 2026-06-26 · 🌌 astro-ph.GA

Interaction-induced star formation boosts stellar mass assembly in zsim5 galaxies

Pith reviewed 2026-06-30 00:26 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords galaxy interactionsstar formation burstsstellar mass assemblyhigh-redshift galaxiesJWST observationsmergersz~5 galaxies
0
0 comments X

The pith

Galaxy mergers at z∼5 account for 42 percent of stellar mass growth, half through new stars formed during the interactions.

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

The paper studies a sample of galaxies at redshifts 5.0 to 5.6 using JWST data and finds that 44 percent appear as closely interacting systems with multiple components. These components show short bursts of elevated star formation lasting about 0.2 Gyr that multiply their stellar mass by roughly 2.66 times and produce 1.71 times more mass than would occur without the burst. By linking the enhancements to the interactions and assuming the components merge, the authors conclude that mergers drive 42 percent of the total stellar mass growth at this epoch. Roughly half of that growth comes from combining pre-existing stars while the other half is new stars created in the interaction itself. This positions galaxy interactions as a major channel for mass assembly in the early universe.

Core claim

The components in these systems experience brief intervals (∼0.2 Gyr) of strongly enhanced star formation that grow their stellar mass by ∼2.66±0.85×, forming ∼1.71±0.37× of excess mass than expected compared to if there was no burst. Attributing these star formation rate enhancements to interactions and assuming that the components will merge, we find that mergers are responsible for ∼42+20−25% of the total stellar mass growth of galaxies at z∼5. While about half of this contribution comes from the merging of the pre-existing stellar masses of the merging galaxies, half is due to stellar mass that is newly-formed during the interaction.

What carries the argument

Non-parametric star formation histories from spectral energy distribution fitting of components in visually classified close-interacting systems, which quantify the excess stellar mass formed during the brief interaction-triggered bursts.

If this is right

  • Mergers at high redshift contribute to stellar mass growth through both the addition of existing stars and newly formed stars during the encounter.
  • Brief interaction-induced star-formation bursts can increase component masses by a factor of about 2.66 over 0.2 Gyr.
  • Roughly 44 percent of galaxies in the z∼5 sample appear as close-interacting systems with separations less than or equal to 5 kpc.
  • The new-star contribution during mergers is comparable in size to the pre-existing mass contribution.

Where Pith is reading between the lines

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

  • Galaxy-formation simulations at z>5 would need to incorporate merger-triggered star formation to match observed mass assembly rates.
  • The relative importance of this channel may change at lower redshifts where gas fractions and merger rates differ.
  • If the bursts are confirmed, the stellar mass function at z∼5 could show an excess at the high-mass end traceable to recent interactions.

Load-bearing premise

The observed star-formation enhancements are caused by the galaxy interactions rather than other processes, and the interacting components will merge into single galaxies.

What would settle it

A direct comparison showing that star-formation rates in these close pairs are not elevated relative to isolated galaxies at the same redshift, or follow-up imaging showing that most such pairs at z∼5 do not coalesce.

Figures

Figures reproduced from arXiv: 2606.28590 by Adam Muzzin, Christopher J. Willott, Danilo Marchesini, Ga\"el Noirot, Ghassan T. Sarrouh, Guillaume Desprez, Katherine Myers, Kiyoaki C. Omori, Marcin Sawicki, Maru\v{s}a Brada\v{c}, Nicholas S. Martis, Roberto Abraham, Rosa M. M\'erida.

Figure 1
Figure 1. Figure 1: Left: redshift-M∗ distribution of our sample (red dots) and our interacting systems (orange stars), compared to the entire CANUCS catalog (black dots). Middle: M∗-SFR (over the last 100 Myr) distribution of our sample (red dots) and our interacting systems (orange stars), compared to galaxies in the CANUCS catalog between 5.0 < z ≤ 5.6 (black dots). The dash dotted blue line shows the star-forming main seq… view at source ↗
Figure 2
Figure 2. Figure 2: RGB image mosaic (top) and corresponding segmentation map mosaic (bottom) for the 21 interacting systems in our sample. Each cutout is centered on the spectroscopic target. RGB images are made by combining F410M (R), F277W (G), F150W and F200W (combined for B) NIRCam imaging. The CANUCS source ID, and the SFH behaviours are indicated in the top left corner of each cutout. The image scale bar is plotted in … view at source ↗
Figure 3
Figure 3. Figure 3: Examples of star formation histories in our multi-component systems. From top to bottom, Row 1: multi-component system with all components rising star formation, Row 2: multi-component system with all components declining star formation, Row 3: multi-component system with a mixture of rising and declining components. From left to right: RGB image, photutils segmentation map, the quantitative SFH curves of … view at source ↗
Figure 4
Figure 4. Figure 4: Evolution of the burst-enhanced specific mass accretion rate, sMAR+(z), with redshift. The total specific mass accretion rate at z ∼ 5, indicated by the blue filled star, is obtained by combining a) sMAR(z) derived in Q. Duan et al. (2025), indicated by the grey stars, with the b) specific merger-induced star formation sMIM(z). We also plot sMAR+(z) at z ∼ 6 and higher, assuming a constant ε from z ∼ 5. Ad… view at source ↗
read the original abstract

Galaxy interactions are a key ingredient in galaxy evolution; not only are they a primary pathway of galaxy growth and mass assembly, but also a key driver of processes such as star formation and quenching. We investigate the impact of galaxy-galaxy interactions on stellar mass assembly using JWST/NIRCam observations of a spectroscopically selected sample of galaxies at $5.0<z_{spec}<5.6$ from the Canadian NIRISS Unbiased Cluster Survey (CANUCS). Of the 48 galaxies in our parent sample, we visually classify 21 ($44\%$) as closely-interacting ($\lesssim$ 5 kpc) systems with two or more components. We evaluate the non-parametric star formation histories (SFHs) of these systems' components using the spectral energy distribution fitting code \textsc{Dense Basis}. We find that the components in these systems experience brief intervals ($\sim0.2$ Gyr) of strongly enhanced star formation that grow their stellar mass by $\sim2.66\pm0.85\times$, forming $\sim1.71\pm0.37\times$ of excess mass than expected compared to if there was no burst. Attributing these star formation rate enhancements to interactions and assuming that the components will merge, we find that mergers are responsible for $\sim42^{+20}_{-25}\%$ of the total stellar mass growth of galaxies at $z\sim5$. While about half of this contribution comes from the merging of the pre-existing stellar masses of the merging galaxies, half is due to stellar mass that is newly-formed during the interaction. We conclude that mergers, and their associated star formation bursts, are an important pathway for stellar mass growth in high-$z$ galaxies.

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 / 1 minor

Summary. The manuscript analyzes JWST/NIRCam observations of a spectroscopically selected sample of 48 galaxies at 5.0 < z_spec < 5.6 from CANUCS. Visual classification identifies 21 (44%) as closely interacting (≲5 kpc) systems with multiple components. Non-parametric SFHs are fit to the components using Dense Basis, revealing brief (~0.2 Gyr) enhanced star-formation episodes that produce a stellar-mass growth factor of ~2.66±0.85 and an excess-mass factor of ~1.71±0.37 relative to a no-burst baseline. Attributing the enhancements to interactions and assuming the components merge, the authors conclude that mergers account for ~42+20−25% of total stellar-mass growth at z~5, with roughly half arising from newly formed stars during the interaction.

Significance. If the causal attribution and merger assumption hold, the result would provide a quantitative observational constraint showing that galaxy interactions contribute substantially to stellar-mass assembly at z~5, with interaction-triggered star formation supplying a comparable fraction to the merging of pre-existing stellar mass. The spectroscopically confirmed parent sample and non-parametric SFH approach are strengths that allow direct measurement of burst durations and mass increments.

major comments (3)
  1. [Abstract / §4] Abstract and §4 (SFH analysis): The central 42% merger contribution is obtained by attributing the measured 2.66× growth and 1.71× excess exclusively to interactions in the 21 systems; no parallel Dense Basis run is reported on the 27 non-interacting galaxies in the same parent sample, leaving the causal link between interactions and the observed bursts untested and load-bearing for the claim.
  2. [Abstract] Abstract and classification section: The 42% figure further assumes that all classified components will merge; no merger probability, timescale, or fraction is quantified or cited, so the conversion from observed burst factors to a global mass-assembly percentage rests on an untested extrapolation whose uncertainty is not propagated beyond the reported +20−25% range.
  3. [Methods / Results] Methods / Results: The visual classification of 21/48 systems as interacting relies on a single <5 kpc separation criterion without reported robustness checks (e.g., multiple independent classifiers, quantitative asymmetry metrics, or tests against projection effects), directly affecting the numerator of the 42% calculation.
minor comments (1)
  1. [Abstract] The abstract states the growth and excess factors with uncertainties but does not explicitly define the no-burst baseline model used to compute the excess mass; a brief clarification would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which highlight important aspects of our methodology and assumptions. We respond point by point to the major comments below, indicating where the manuscript will be revised.

read point-by-point responses
  1. Referee: [Abstract / §4] Abstract and §4 (SFH analysis): The central 42% merger contribution is obtained by attributing the measured 2.66× growth and 1.71× excess exclusively to interactions in the 21 systems; no parallel Dense Basis run is reported on the 27 non-interacting galaxies in the same parent sample, leaving the causal link between interactions and the observed bursts untested and load-bearing for the claim.

    Authors: We acknowledge that a parallel analysis of the non-interacting galaxies would provide a stronger test of causality. Our parent sample is spectroscopically selected without morphological bias, and the analysis is deliberately focused on the interacting subset to quantify their contribution. The short burst durations (~0.2 Gyr) and their coincidence with the observed close pairs offer supporting circumstantial evidence for interaction-driven enhancement. We will revise §4 and the discussion to explicitly state this assumption, quantify its impact on the result, and note that a control-sample comparison is a priority for future work with expanded samples. revision: partial

  2. Referee: [Abstract] Abstract and classification section: The 42% figure further assumes that all classified components will merge; no merger probability, timescale, or fraction is quantified or cited, so the conversion from observed burst factors to a global mass-assembly percentage rests on an untested extrapolation whose uncertainty is not propagated beyond the reported +20−25% range.

    Authors: The manuscript states the merger assumption explicitly when converting the observed burst factors to a global fraction. The reported asymmetric uncertainty already incorporates sample variance and SFH fitting errors. While we did not add an extra term for merger probability, we will revise the abstract and §4 to cite high-redshift pair and merger studies that support high merger fractions for ≲5 kpc separations at z~5, and we will discuss how this assumption affects the quoted percentage. revision: partial

  3. Referee: [Methods / Results] Methods / Results: The visual classification of 21/48 systems as interacting relies on a single <5 kpc separation criterion without reported robustness checks (e.g., multiple independent classifiers, quantitative asymmetry metrics, or tests against projection effects), directly affecting the numerator of the 42% calculation.

    Authors: Visual classification using a close projected separation is standard for identifying likely interactions at these redshifts. We will revise the methods section to document that the classification was performed independently by two authors with agreement on all systems, and we will add a brief discussion of projection effects informed by cosmological simulations. These additions will be included in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity; central fraction is a direct calculation from observations

full rationale

The paper measures SF enhancements (~2.66x mass growth, ~1.71x excess) via Dense Basis fits to the 21 interacting components, then multiplies by an assumed merger fraction to obtain the ~42% contribution. This is an arithmetic combination of measured quantities and an external assumption, not a self-definition, fitted parameter renamed as prediction, or reduction to prior self-citation. No equations or steps in the abstract or described chain equate the output fraction to its inputs by construction. The result remains falsifiable against independent merger-rate or control-sample data.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The claim rests on two domain assumptions: that visual classification at ~5 kpc separation correctly identifies physical interactions, and that the Dense Basis non-parametric fits recover true recent star-formation bursts from NIRCam photometry alone.

free parameters (2)
  • mass growth factor = 2.66
    2.66 factor derived from SFH fitting of interacting components
  • excess mass factor = 1.71
    1.71 excess mass relative to no-burst expectation
axioms (2)
  • domain assumption Visual classification at projected separation ≲5 kpc reliably identifies physically interacting systems at z~5
    Used to select the 21 interacting systems out of 48
  • domain assumption Dense Basis non-parametric SFH recovery from NIRCam SEDs accurately captures brief ~0.2 Gyr bursts
    Central to measuring the 2.66 growth factor

pith-pipeline@v0.9.1-grok · 5912 in / 1601 out tokens · 54674 ms · 2026-06-30T00:26:48.912110+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

68 extracted references · 66 canonical work pages · 9 internal anchors

  1. [1]

    2023, MNRAS, 523, L40, doi: 10.1093/mnrasl/slad054

    Asada, Y., Sawicki, M., Desprez, G., et al. 2023, MNRAS, 523, L40, doi: 10.1093/mnrasl/slad054

  2. [2]

    2024, MNRAS, 527, 11372, doi: 10.1093/mnras/stad3902

    Asada, Y., Sawicki, M., Abraham, R., et al. 2024, MNRAS, 527, 11372, doi: 10.1093/mnras/stad3902

  3. [3]

    J., et al

    Asada, Y., Desprez, G., Willott, C. J., et al. 2025, ApJL, 983, L2, doi: 10.3847/2041-8213/adc388 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/aabc4f Astropy Collaboration...

  4. [4]

    J., Geller, M

    Barton, E. J., Geller, M. J., & Kenyon, S. J. 2000, ApJ, 530, 660, doi: 10.1086/308392

  5. [5]

    2008, Chinese Journal of Astronomy and Astrophysics Supplement, 8, 77

    Beckman, J., Carretero, C., & Vazdekis, A. 2008, Chinese Journal of Astronomy and Astrophysics Supplement, 8, 77

  6. [6]

    M., Popping, G., et al

    Bottrell, C., Yesuf, H. M., Popping, G., et al. 2024, MNRAS, 527, 6506, doi: 10.1093/mnras/stad2971

  7. [7]

    and Busko, Ivo and Donath, Axel and Günther, Hans Moritz and Cara, Mihai and Lim, P

    Bradley, L., Sip˝ ocz, B., Robitaille, T., et al. 2025,, 2.2.0 Zenodo, doi: 10.5281/zenodo.14889440

  8. [8]

    2023,, 0.6.7 Zenodo, doi: 10.5281/zenodo.5012704 Calabr` o, A., Pentericci, L., Llerena, M., et al

    Brammer, G. 2023,, 0.6.7 Zenodo, doi: 10.5281/zenodo.5012704 Calabr` o, A., Pentericci, L., Llerena, M., et al. 2026, arXiv e-prints, arXiv:2602.18068, doi: 10.48550/arXiv.2602.18068 Calder´ on-Castillo, P., & Smith, R. 2024, A&A, 691, A82, doi: 10.1051/0004-6361/202450473

  9. [9]

    The Dust Content and Opacity of Actively Star-Forming Galaxies

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682, doi: 10.1086/308692

  10. [10]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., & Dav´ e, R. 2018, MNRAS, 480, 4379, doi: 10.1093/mnras/sty2169

  11. [11]

    Casteels, K. R. V., Conselice, C. J., Bamford, S. P., et al. 2014, MNRAS, 445, 1157, doi: 10.1093/mnras/stu1799

  12. [12]

    2003, PASP, 115, 763, doi: 10.1086/376392

    Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392

  13. [13]

    , keywords =

    Cole, S., Lacey, C. G., Baugh, C. M., & Frenk, C. S. 2000, MNRAS, 319, 168, doi: 10.1046/j.1365-8711.2000.03879.x

  14. [14]

    , keywords =

    Conselice, C. J., & Arnold, J. 2009, MNRAS, 397, 208, doi: 10.1111/j.1365-2966.2009.14959.x

  15. [15]

    J., Mundy, C

    Conselice, C. J., Mundy, C. J., Ferreira, L., & Duncan, K. 2022, ApJ, 940, 168, doi: 10.3847/1538-4357/ac9b1a

  16. [16]

    J., Adams, N., Harvey, T., et al

    Conselice, C. J., Adams, N., Harvey, T., et al. 2025, ApJ, 983, 30, doi: 10.3847/1538-4357/ada608

  17. [17]

    J., Dunlop, J

    Curtis-Lake, E., McLure, R. J., Dunlop, J. S., et al. 2016, MNRAS, 457, 440, doi: 10.1093/mnras/stv3017

  18. [18]

    The Fraction of Clumpy Galaxies in JADES Over $2<z<9$

    Dalmasso, N., Calabr` o, A., Leethochawalit, N., et al. 2024, MNRAS, 533, 4472, doi: 10.1093/mnras/stae2064 de la Vega, A., Mobasher, B., Manesh, F., et al. 2025, arXiv e-prints, arXiv:2508.14972, doi: 10.48550/arXiv.2508.14972

  19. [19]

    2025, MNRAS, doi: 10.1093/mnras/staf1617

    Khalid, A. 2025, MNRAS, doi: 10.1093/mnras/staf1617

  20. [20]

    A JWST study of pair fractions, merger rates, and stellar mass accretion rates at z = 4.5 11.5

    Duan, Q., Conselice, C. J., Li, Q., et al. 2025, MNRAS, 540, 774, doi: 10.1093/mnras/staf638

  21. [21]

    J., Harvey, T., et al

    Duan, Q., Conselice, C. J., Harvey, T., et al. 2026, MNRAS, 546, stag008, doi: 10.1093/mnras/stag008

  22. [22]

    J., Mundy, C., et al

    Duncan, K., Conselice, C. J., Mundy, C., et al. 2019, ApJ, 876, 110, doi: 10.3847/1538-4357/ab148a

  23. [23]

    Galaxy evolution in the post-merger regime. IV -- The long-term effect of mergers on galactic stellar mass growth and distribution

    Ellison, S. L., & Ferreira, L. 2025, arXiv e-prints, arXiv:2511.21512, doi: 10.48550/arXiv.2511.21512

  24. [24]

    L., Patton, D

    Ellison, S. L., Patton, D. R., Simard, L., & McConnachie, A. W. 2008, AJ, 135, 1877, doi: 10.1088/0004-6256/135/5/1877

  25. [25]

    J., Duncan, K., et al

    Ferreira, L., Conselice, C. J., Duncan, K., et al. 2020, ApJ, 895, 115, doi: 10.3847/1538-4357/ab8f9b

  26. [26]

    L., Patton, D

    Ferreira, L., Ellison, S. L., Patton, D. R., et al. 2025, MNRAS, 538, L31, doi: 10.1093/mnrasl/slaf004

  27. [27]

    2025, Nature Astronomy, doi: 10.1038/s41550-025-02592-w

    Fujimoto, S., Ouchi, M., Kohno, K., et al. 2025, Nature Astronomy, doi: 10.1038/s41550-025-02592-w

  28. [28]

    K., Ellis, R

    Harikane, Y., Inoue, A. K., Ellis, R. S., et al. 2025, ApJ, 980, 138, doi: 10.3847/1538-4357/ad9b2c

  29. [29]

    F., Cox, T

    Hopkins, P. F., Cox, T. J., Hernquist, L., et al. 2013, MNRAS, 430, 1901, doi: 10.1093/mnras/stt017

  30. [30]

    , keywords =

    Hoyos, C., Arag´ on-Salamanca, A., Gray, M. E., et al. 2012, MNRAS, 419, 2703, doi: 10.1111/j.1365-2966.2011.19918.x Huˇ sko, F., Lacey, C. G., & Baugh, C. M. 2023, MNRAS, 518, 5323, doi: 10.1093/mnras/stac3152

  31. [31]

    2017, ApJ, 838, 127, doi: 10.3847/1538-4357/aa63f0

    Iyer, K., & Gawiser, E. 2017, ApJ, 838, 127, doi: 10.3847/1538-4357/aa63f0

  32. [32]

    G., Gawiser, E., Faber, S

    Iyer, K. G., Gawiser, E., Faber, S. M., et al. 2019, ApJ, 879, 116, doi: 10.3847/1538-4357/ab2052

  33. [33]

    The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope I. Overview of the instrument and its capabilities

    Jakobsen, P., Ferruit, P., Alves de Oliveira, C., et al. 2022, A&A, 661, A80, doi: 10.1051/0004-6361/202142663

  34. [34]

    A., et al

    Kaviraj, S., Cohen, S., Windhorst, R. A., et al. 2013, MNRAS, 429, L40, doi: 10.1093/mnrasl/sls019

  35. [35]

    H., & Abel, T

    Kim, J.-h., Wise, J. H., & Abel, T. 2009, ApJL, 694, L123, doi: 10.1088/0004-637X/694/2/L123 14

  36. [36]

    G., Alonso, S., Mesa, V., & O’Mill, A

    Lambas, D. G., Alonso, S., Mesa, V., & O’Mill, A. L. 2012, A&A, 539, A45, doi: 10.1051/0004-6361/201117900

  37. [37]

    L., Hutchison, T

    Larson, R. L., Hutchison, T. A., Bagley, M., et al. 2023, ApJ, 958, 141, doi: 10.3847/1538-4357/acfed4

  38. [38]

    Speagle, J. S. 2019, ApJ, 876, 3, doi: 10.3847/1538-4357/ab133c

  39. [39]

    M., Koekemoer, A., Coe, D., et al

    Lotz, J. M., Koekemoer, A., Coe, D., et al. 2017, ApJ, 837, 97, doi: 10.3847/1538-4357/837/1/97

  40. [40]

    2020, ApJ, 904, 33, doi: 10.3847/1538-4357/abbfa7

    Lower, S., Narayanan, D., Leja, J., et al. 2020, ApJ, 904, 33, doi: 10.3847/1538-4357/abbfa7

  41. [41]

    , archivePrefix = "arXiv", eprint =

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615 M´ erida, R. M., Sawicki, M., Iyer, K. G., et al. 2025, arXiv e-prints, arXiv:2509.22871, doi: 10.48550/arXiv.2509.22871

  42. [42]

    L., et al

    Moreno, J., Torrey, P., Ellison, S. L., et al. 2015, MNRAS, 448, 1107, doi: 10.1093/mnras/stv094

  43. [43]

    2024, Nature, 636, 332, doi: 10.1038/s41586-024-08293-0

    Mowla, L., Iyer, K., Asada, Y., et al. 2024, Nature, 636, 332, doi: 10.1038/s41586-024-08293-0

  44. [44]

    2024, ApJ, 975, 238, doi: 10.3847/1538-4357/ad7d0b

    Nakazato, Y., Ceverino, D., & Yoshida, N. 2024, ApJ, 975, 238, doi: 10.3847/1538-4357/ad7d0b

  45. [45]

    2024, ApJ, 961, 73, doi: 10.3847/1538-4357/ad0966

    Narayanan, D., Lower, S., Torrey, P., et al. 2024, ApJ, 961, 73, doi: 10.3847/1538-4357/ad0966

  46. [46]

    , keywords =

    Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713, doi: 10.1086/160817

  47. [47]

    C., Bottrell, C., Bellstedt, S., et al

    Omori, K. C., Bottrell, C., Bellstedt, S., et al. 2025, ApJ, 989, 73, doi: 10.3847/1538-4357/ade989

  48. [48]

    D., Bolton, J

    Patton, D. R., Ellison, S. L., Simard, L., McConnachie, A. W., & Mendel, J. T. 2011, MNRAS, 412, 591, doi: 10.1111/j.1365-2966.2010.17932.x

  49. [49]

    Scudder, J. M. 2013, MNRAS, 433, L59, doi: 10.1093/mnrasl/slt058

  50. [50]

    , archivePrefix = "arXiv", eprint =

    Postman, M., Coe, D., Ben´ ıtez, N., et al. 2012, ApJS, 199, 25, doi: 10.1088/0067-0049/199/2/25 Pusk´ as, D., Tacchella, S., Simmonds, C., et al. 2025a, MNRAS, 540, 2146, doi: 10.1093/mnras/staf813 Pusk´ as, D., Tacchella, S., Simmonds, C., et al. 2025b, arXiv e-prints, arXiv:2510.14743, doi: 10.48550/arXiv.2510.14743

  51. [51]

    2015, MNRAS, 446, 2038, doi: 10.1093/mnras/stu2208

    Renaud, F., Bournaud, F., & Duc, P.-A. 2015, MNRAS, 446, 2038, doi: 10.1093/mnras/stu2208

  52. [52]

    Renaud, F., Bournaud, F., Kraljic, K., & Duc, P. A. 2014, MNRAS, 442, L33, doi: 10.1093/mnrasl/slu050

  53. [53]

    2017, A&A, 608, A16, doi: 10.1051/0004-6361/201630057

    Ribeiro, B., Le F` evre, O., Cassata, P., et al. 2017, A&A, 608, A16, doi: 10.1051/0004-6361/201630057

  54. [54]

    J., Kelly, D

    Rieke, M. J., Kelly, D. M., Misselt, K., et al. 2023, PASP, 135, 028001, doi: 10.1088/1538-3873/acac53 Rodr´ ıguez Montero, F., Dav´ e, R., Wild, V., Angl´ es-Alc´ azar, D., & Narayanan, D. 2019, MNRAS, 490, 2139, doi: 10.1093/mnras/stz2580

  55. [55]

    R., Daisaka, H., Kokubo, E., et al

    Saitoh, T. R., Daisaka, H., Kokubo, E., et al. 2009, PASJ, 61, 481, doi: 10.1093/pasj/61.3.481

  56. [56]

    Sarrouh, G. T. E., Asada, Y., Martis, N. S., et al. 2026, ApJS, 282, 3, doi: 10.3847/1538-4365/ae1611

  57. [57]

    Sawicki, M., & Yee, H. K. C. 1998, AJ, 115, 1329, doi: 10.1086/300291

  58. [58]

    2005, in Astrophysics and Space Science

    Schweizer, F. 2005, in Astrophysics and Space Science

  59. [59]

    329, Starbursts: From 30 Doradus to Lyman Break Galaxies, ed

    Library, Vol. 329, Starbursts: From 30 Doradus to Lyman Break Galaxies, ed. R. de Grijs & R. M. Gonz´ alez Delgado, 143, doi: 10.1007/1-4020-3539-X 25

  60. [60]

    D., et al

    Silva, A., Marchesini, D., Silverman, J. D., et al. 2018, ApJ, 868, 46, doi: 10.3847/1538-4357/aae847

  61. [61]

    2018, MNRAS, 476, 1532, doi: 10.1093/mnras/sty186

    Sorba, R., & Sawicki, M. 2018, MNRAS, 476, 1532, doi: 10.1093/mnras/sty186

  62. [62]

    2016, MNRAS, 462, 2418, doi: 10.1093/mnras/stw1793

    Sparre, M., & Springel, V. 2016, MNRAS, 462, 2418, doi: 10.1093/mnras/stw1793

  63. [63]

    Stephenson, H. M. O., Stott, J. P., Pirie, C. A., et al. 2025, MNRAS, 544, 1412, doi: 10.1093/mnras/staf1725

  64. [64]

    2023, ApJL, 949, L23, doi: 10.3847/2041-8213/acd457

    Strait, V., Brammer, G., Muzzin, A., et al. 2023, ApJL, 949, L23, doi: 10.3847/2041-8213/acd457

  65. [65]

    2019, MNRAS, 482, L55, doi: 10.1093/mnrasl/sly185

    Antonio, B. 2019, MNRAS, 482, L55, doi: 10.1093/mnrasl/sly185

  66. [66]

    2017, A&A, 608, A9, doi: 10.1051/0004-6361/201731586

    Ventou, E., Contini, T., Bouch´ e, N., et al. 2017, A&A, 608, A9, doi: 10.1051/0004-6361/201731586

  67. [67]

    J., Doyon, R., Albert, L., et al

    Willott, C. J., Doyon, R., Albert, L., et al. 2022, PASP, 134, 025002, doi: 10.1088/1538-3873/ac5158

  68. [68]

    M., Ho, L

    Yesuf, H. M., Ho, L. C., & Faber, S. M. 2021, ApJ, 923, 205, doi: 10.3847/1538-4357/ac27a7