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arxiv: 2606.11315 · v1 · pith:OHGVLRPKnew · submitted 2026-06-09 · 🌌 astro-ph.GA

Spiral arms across cosmic time: JWST measurements of the pitch angles of spiral galaxies at z<3.5

Vicki Kuhn , Yicheng Guo , Sophie Rentschler , Maxmillian Castillo , Gourab Nandi , Ellie Dugdale , Tsinat Mitiku This is my paper

Pith reviewed 2026-06-27 12:26 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords spiral galaxiespitch angleJWSTredshift evolutiondensity wave theorygalaxy structurecosmic timeCEERS JADES
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The pith

Spiral galaxy arms show a transition at redshift 1 from locally driven structures to ones regulated by global gravitational potential.

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

The paper measures pitch angles in 593 massive spiral galaxies observed with JWST up to redshift 3.5. It finds no overall change in average pitch angle with time, yet a clear shift in how pitch angle relates to galaxy properties. Above redshift 1.25, pitch angle shows no correlation with stellar mass, bulge mass, disk mass, or star formation rate. Below that redshift, pitch angle becomes negatively correlated with mass measures and positively correlated with specific star formation rate at the lowest redshifts. These patterns point to a change in the dominant mechanism shaping spiral arms around z=1.

Core claim

The analysis of pitch angles reveals no significant redshift evolution across the full sample, with only the most massive galaxies showing slight winding up over time. At z greater than 1.25, pitch angle is uncorrelated with stellar mass, bulge mass, disk mass, and specific star formation rate. At z less than 1.25, statistically significant correlations appear: negative with stellar, bulge, and disk mass, and positive with sSFR below z=0.75. Pitch angle shows no dependence on tidal strength from companions. The results indicate a transition epoch near z=1, above which spiral structures are primarily locally driven and below which they are regulated by global gravitational potential in line w

What carries the argument

Pitch angle measurements from SpArcFiRe applied to galaxies identified by a fine-tuned Zoobot model, used to test correlations with global galaxy properties as a function of redshift.

If this is right

  • Spiral structure formation mechanisms differ before and after z approximately 1.
  • Density wave theory applies only to spiral arms at redshifts below 1.25.
  • Local processes dominate spiral arm formation in galaxies at z greater than 1.25.
  • Tidal interactions with companions do not influence pitch angles at any redshift in this sample.
  • The most massive galaxies exhibit gradual tightening of spiral arms over cosmic time.

Where Pith is reading between the lines

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

  • Models of early galaxy disk dynamics may need separate prescriptions for arm formation above and below redshift 1.
  • Future high-resolution observations at z greater than 2 could test whether local instabilities alone can sustain measurable spiral arms without global potential wells.
  • The lack of tidal correlation suggests companion-driven triggering is not the main driver even at earlier epochs where mergers were more frequent.
  • If the transition holds, simulations should reproduce a redshift-dependent switch in the dominant source of non-axisymmetric structure.

Load-bearing premise

The identification of spiral galaxies and measurement of their pitch angles remains free of systematic biases that change with redshift in the JWST images, and the sample represents the massive spiral population at each epoch.

What would settle it

A reanalysis of the same JWST fields that finds pitch angles at z greater than 1.25 to be systematically underestimated or overestimated relative to lower redshifts, or a larger sample showing the same mass and sSFR correlations already present above z=1.25.

Figures

Figures reproduced from arXiv: 2606.11315 by Ellie Dugdale, Gourab Nandi, Maxmillian Castillo, Sophie Rentschler, Tsinat Mitiku, Vicki Kuhn, Yicheng Guo.

Figure 1
Figure 1. Figure 1: Top row: Our sample in the SFR–stellar mass diagram at different redshift bins. Spiral galaxies are shown in purple squares and non-spiral galaxies are shown in orange circles. Middle row: Similar to the top panel, sSFR–stellar mass diagram. Bottom row: The half-light radius–stellar mass diagram. Spiral galaxies tend to have higher SFRs, sSFRs, and sizes compared to non-spiral galaxies. analysis (e.g., S. … view at source ↗
Figure 2
Figure 2. Figure 2: Example SpArcFiRe outputs. Top row: The original image that was presented. Middle row: The cen￾tered and de-projected image. Bottom row: The middle image with final arcs superimposed. (a)-(d) Spiral galaxies at z = 0.284, 1.098, 2.033, 3.225, respectively. (e) A bulge being improperly fit as an arc. (f) A spiral galaxy that has been zoomed in too close. (g) A pair of galaxies merging. (h) A non-spiral bein… view at source ↗
Figure 3
Figure 3. Figure 3: A comparison between our SpArcFiRe measure￾ments and those of I. V. Chugunov et al. (2025) who used a photometric decomposition method. The mean offset is −0.57◦ and scatter of σ = 8.31◦ between our two measure￾ments. smaller mass bins show a very similar relation to the overall trend (with 495 galaxies falling within these masses). The largest mass bin (98 galaxies) shows the largest increase with Pearson… view at source ↗
Figure 4
Figure 4. Figure 4: Pitch angle as a function of redshift. The black diamonds and line represent the mean and standard deviation of the total sample. Values are colored by their stellar mass: log(M∗/M⊙): 10-10.5 (blue), log(M∗/M⊙): 10.5-11.0 (orange), log(M∗/M⊙): 11.0-12.0 (purple). The shaded regions show a 1σ error on the mean. The solid lines show the best fit line for each mass bin. The best-fit line, Pearson and Spearman… view at source ↗
Figure 5
Figure 5. Figure 5: (a) shows the distribution of pitch angles across our entire sample. (b)-(f) shows the Pringle-Dobbs test (dis￾tribution of cot ϕ). 3.3. Correlations between pitch angle and internal properties [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: First row: pitch angle as a function of total stellar mass. Black diamonds and dashed lines show mean and standard deviations. The shaded area shows the 1σ error on the mean. Best-fit line, Pearson, and Spearman correlation values, along with their p-values, are shown in the top right. Second row: pitch angle as a function of bulge mass. Third row: pitch angle as a function of disk mass. Fourth row: pitch … view at source ↗
Figure 7
Figure 7. Figure 7: Pitch angle as a function of the log of the tidal strength (P) exerted by a neighboring galaxy. We compared our results with S. H. Oh et al. (2015) (lime dashed line). Our P values are much lower than those in S. H. Oh et al. (2015) and we have shaded the region that falls outside their values. 4.1. Differences in pitch angle values Pitch angle values for nearby galaxies differ by several degrees. S.-Y. Yu… view at source ↗
Figure 8
Figure 8. Figure 8: Results of our arm classification. The number of flocculent galaxies (orange) decreases with cosmic time whereas multiple-armed galaxies (purple) slightly increase. Blue bars represent the category where the classifiers could not see a clear arm class and green bars represent the galaxies where there was no arm class majority. We found that multiple-armed galaxies remain fairly steady at 1.25 ≤ z < 3.5 and… view at source ↗
read the original abstract

The properties of spiral galaxies in the early universe remain poorly studied and, as such, little is known about their nature and evolution. We use JWST data to measure the pitch angles of spiral galaxies across cosmic time. Our sample consists of 593 spiral galaxies with stellar masses ($M_*$) greater than $10^{10} M_\odot$ up to $z \sim 3.5$, drawn from the CEERS and JADES surveys. Spiral galaxies are identified by fine-tuning a Zoobot deep-learning model. We use SpArcFiRe to identify spiral arms and measure their pitch angles. We find no significant redshift evolution in the average pitch angle across the full sample. However, in the most massive systems (log$(M_*/M_\odot)=11-12$), spiral arms slightly wind up with time. We show that at $z>1.25$, pitch angle does not correlate with some key internal galaxy properties (stellar mass, bulge mass, disk mass, specific star formation rate [sSFR]). In contrast, at $z<1.25$, pitch angle shows a weak but statistically significant negative correlation with stellar mass, bulge mass, and disk mass, and a positive correlation with sSFR at $z<0.75$. We also find no dependence of pitch angle on the tidal strength applied by nearby companions. These results indicate a transition epoch at $z\sim1$: above this redshift, spiral structures appear to be primarily locally driven and not correlated with global galaxy properties; and below this redshift, spiral arms are regulated by global gravitational potential, consistent with the predictions of the density wave theory.

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

1 major / 2 minor

Summary. The paper measures pitch angles of spiral arms in 593 massive (M*>10^10 Msun) galaxies at z<3.5 drawn from JWST CEERS and JADES fields. Spirals are selected via fine-tuned Zoobot and arms traced with SpArcFiRe. It reports no overall redshift evolution in mean pitch angle (with mild winding in the highest-mass bin), absence of correlations between pitch angle and M*, bulge mass, disk mass or sSFR at z>1.25, and the emergence of weak but significant correlations (negative with mass terms, positive with sSFR below z~0.75) at lower redshift. These are interpreted as evidence for a transition epoch near z~1 from locally driven to globally regulated (density-wave) spiral structure.

Significance. If the differential correlation results are robust to measurement systematics, the work supplies the first statistical constraints on spiral-arm pitch-angle behavior across most of cosmic time and offers an observational test of the redshift at which global gravitational potential begins to dominate arm morphology, with direct implications for distinguishing local versus density-wave formation channels in galaxy-evolution models.

major comments (1)
  1. [Abstract] Abstract (and the transition-epoch interpretation): the reported absence of pitch-angle correlations with M*, bulge mass, disk mass and sSFR at z>1.25 is presented as physical evidence for a change in arm-driving mechanism. However, no stratified validation, mock recovery tests, or human-labeled subsets binned by redshift are described to demonstrate that Zoobot selection completeness and SpArcFiRe pitch-angle scatter remain constant across the z=0–3.5 range. Because JWST angular resolution, surface-brightness sensitivity and rest-frame bandpass all degrade with redshift, any z-dependent drop in arm-detection fidelity would preferentially suppress correlations in the high-z bin while preserving them at low z, rendering the transition claim load-bearing on an untested assumption.
minor comments (2)
  1. [Abstract] Abstract states statistical results (correlations, transition epoch) but supplies no error bars, sample sizes per redshift bin, or completeness corrections; these quantitative details are required to assess the significance of the reported weak correlations.
  2. The claim of “no dependence of pitch angle on the tidal strength applied by nearby companions” is stated without reference to the tidal-strength estimator or the companion catalog used; a brief methods paragraph or table would clarify this null result.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful review and constructive feedback. We address the major comment on the abstract and transition-epoch interpretation below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and the transition-epoch interpretation): the reported absence of pitch-angle correlations with M*, bulge mass, disk mass and sSFR at z>1.25 is presented as physical evidence for a change in arm-driving mechanism. However, no stratified validation, mock recovery tests, or human-labeled subsets binned by redshift are described to demonstrate that Zoobot selection completeness and SpArcFiRe pitch-angle scatter remain constant across the z=0–3.5 range. Because JWST angular resolution, surface-brightness sensitivity and rest-frame bandpass all degrade with redshift, any z-dependent drop in arm-detection fidelity would preferentially suppress correlations in the high-z bin while preserving them at low z, rendering the transition claim load-bearing on an untested assumption.

    Authors: We agree that the manuscript does not describe explicit stratified validation, mock recovery tests, or human-labeled subsets binned by redshift to confirm that Zoobot completeness and SpArcFiRe scatter are constant with redshift. This is a valid concern, as observational effects could in principle introduce z-dependent biases. To strengthen the transition-epoch claim, the revised manuscript will add a dedicated subsection with mock recovery tests (degrading low-z images to high-z conditions and re-measuring) and an assessment of arm-detection fraction versus redshift. These will quantify whether the reported absence of correlations above z=1.25 could be driven by systematics. revision: yes

Circularity Check

0 steps flagged

No circularity: purely observational measurements and binned correlations

full rationale

The paper reports direct measurements of pitch angles from JWST imaging using fine-tuned Zoobot for galaxy selection and SpArcFiRe for arm tracing, then computes statistical correlations (or their absence) between pitch angle and galaxy properties (M*, bulge mass, disk mass, sSFR) in redshift-binned subsamples. No equations, derivations, or first-principles predictions are presented; the transition-epoch interpretation follows from the observed pattern of correlations changing across z~1.25. No self-citations, fitted inputs renamed as predictions, or ansatzes appear in the provided text. The analysis is self-contained against external benchmarks of the measurement pipeline and sample selection.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on the accuracy of the machine-learning galaxy classifier and the arm-measurement algorithm when applied to JWST data at high redshift; no free parameters are introduced in the abstract, and no new physical entities are postulated.

axioms (2)
  • domain assumption The fine-tuned Zoobot model reliably identifies spiral galaxies in JWST images across 0<z<3.5
    Sample construction and all subsequent measurements rest on this identification step.
  • domain assumption SpArcFiRe returns unbiased pitch-angle measurements for the identified spirals in the CEERS and JADES fields
    All reported averages, correlations, and the transition claim are derived from these measurements.

pith-pipeline@v0.9.1-grok · 5865 in / 1416 out tokens · 29739 ms · 2026-06-27T12:26:47.894908+00:00 · methodology

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Works this paper leans on

85 extracted references · 83 canonical work pages · 3 internal anchors

  1. [1]

    Athanassoula, E., Romero-G´ omez, M., & Masdemont, J. J. 2009, MNRAS, 394, 67, doi: 10.1111/j.1365-2966.2008.14273.x

    work page doi:10.1111/j.1365-2966.2008.14273.x 2009

  2. [2]

    , keywords =

    Bagley, M. B., Finkelstein, S. L., Koekemoer, A. M., et al. 2023, ApJL, 946, L12, doi: 10.3847/2041-8213/acbb08

    work page doi:10.3847/2041-8213/acbb08 2023

  3. [3]

    1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

    work page doi:10.1051/aas:1996164 1996

  4. [4]

    A., Elmegreen, B

    Bittner, A., Gadotti, D. A., Elmegreen, B. G., et al. 2017, MNRAS, 471, 1070, doi: 10.1093/mnras/stx1646

    work page doi:10.1093/mnras/stx1646 2017

  5. [5]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503, doi: 10.1086/591786

    work page internal anchor Pith review doi:10.1086/591786 2008

  6. [6]

    C., Bianchi, L., et al

    Calzetti, D., Kennicutt, Jr., R. C., Bianchi, L., et al. 2005, ApJ, 633, 871, doi: 10.1086/466518

    work page doi:10.1086/466518 2005

  7. [7]

    V., Marchuk, A

    Chugunov, I. V., Marchuk, A. A., & Mosenkov, A. V. 2025, PASA, 42, e029, doi: 10.1017/pasa.2025.6

    work page doi:10.1017/pasa.2025.6 2025

  8. [8]

    T., et al

    Cibinel, A., Daddi, E., Sargent, M. T., et al. 2019, MNRAS, 485, 5631, doi: 10.1093/mnras/stz690

    work page doi:10.1093/mnras/stz690 2019

  9. [9]

    Conroy, C., & Gunn, J. E. 2010,, Astrophysics Source Code Library, record ascl:1010.043 http://ascl.net/1010.043 13

    2010

  10. [10]

    The Astrophysical Journal , author =

    Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486, doi: 10.1088/0004-637X/699/1/486

    work page internal anchor Pith review doi:10.1088/0004-637x/699/1/486 2009

  11. [11]

    G., Guo, Y., et al

    Costantin, L., P´ erez-Gonz´ alez, P. G., Guo, Y., et al. 2023, Nature, 623, 499, doi: 10.1038/s41586-023-06636-x

    work page doi:10.1038/s41586-023-06636-x 2023

  12. [12]

    L., Berrier, J

    Davis, B. L., Berrier, J. C., Shields, D. W., et al. 2012, ApJS, 199, 33, doi: 10.1088/0067-0049/199/2/33

    work page doi:10.1088/0067-0049/199/2/33 2012

  13. [13]

    L., Graham, A

    Davis, B. L., Graham, A. W., & Cameron, E. 2018, ApJ, 869, 113, doi: 10.3847/1538-4357/aae820

    work page doi:10.3847/1538-4357/aae820 2018

  14. [14]

    L., Graham, A

    Davis, B. L., Graham, A. W., & Cameron, E. 2019, ApJ, 873, 85, doi: 10.3847/1538-4357/aaf3b8

    work page doi:10.3847/1538-4357/aaf3b8 2019

  15. [15]

    L., Kennefick, D., Kennefick, J., et al

    Davis, B. L., Kennefick, D., Kennefick, J., et al. 2015, ApJL, 802, L13, doi: 10.1088/2041-8205/802/1/L13

    work page doi:10.1088/2041-8205/802/1/l13 2015

  16. [16]

    R., & Hayes, W

    Davis, D. R., & Hayes, W. B. 2014, ApJ, 790, 87, doi: 10.1088/0004-637X/790/2/87 D´ ıaz-Garc´ ıa, S., Salo, H., Knapen, J. H., &

    work page doi:10.1088/0004-637x/790/2/87 2014

  17. [17]

    2019, A&A, 631, A94, doi: 10.1051/0004-6361/201936000

    Herrera-Endoqui, M. 2019, A&A, 631, A94, doi: 10.1051/0004-6361/201936000

    work page doi:10.1051/0004-6361/201936000 2019

  18. [18]

    2014, PASA, 31, e035, doi: 10.1017/pasa.2014.31 D’Onghia, E., Vogelsberger, M., & Hernquist, L

    Dobbs, C., & Baba, J. 2014, PASA, 31, e035, doi: 10.1017/pasa.2014.31 D’Onghia, E., Vogelsberger, M., & Hernquist, L. 2013, ApJ, 766, 34, doi: 10.1088/0004-637X/766/1/34

    work page doi:10.1017/pasa.2014.31 2014

  19. [19]

    Overview of the JWST Advanced Deep Extragalactic Survey (JADES)

    Eisenstein, D. J., Willott, C., Alberts, S., et al. 2023a, arXiv e-prints, arXiv:2306.02465, doi: 10.48550/arXiv.2306.02465

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2306.02465

  20. [20]

    arXiv e-prints , keywords =

    Eisenstein, D. J., Johnson, B. D., Robertson, B., et al. 2023b, arXiv e-prints, arXiv:2310.12340, doi: 10.48550/arXiv.2310.12340

    work page doi:10.48550/arxiv.2310.12340

  21. [21]

    Elmegreen, B. G. 2011, in EAS Publications Series, Vol. 51, EAS Publications Series, ed. C. Charbonnel & T. Montmerle (EDP), 19–30, doi: 10.1051/eas/1151002

    work page doi:10.1051/eas/1151002 2011

  22. [22]

    G., & Elmegreen, D

    Elmegreen, B. G., & Elmegreen, D. M. 1985, ApJ, 288, 438, doi: 10.1086/162810

    work page doi:10.1086/162810 1985

  23. [23]

    M., & Elmegreen, B

    Elmegreen, D. M., & Elmegreen, B. G. 1982, MNRAS, 201, 1021, doi: 10.1093/mnras/201.4.1021

    work page doi:10.1093/mnras/201.4.1021 1982

  24. [24]

    M., & Elmegreen, B

    Elmegreen, D. M., & Elmegreen, B. G. 2014, ApJ, 781, 11, doi: 10.1088/0004-637X/781/1/11 Espejo Salcedo, J. M., Pastras, S., V´ acha, J., et al. 2025, A&A, 700, A42, doi: 10.1051/0004-6361/202554725

    work page doi:10.1088/0004-637x/781/1/11 2014

  25. [25]

    J., Faber, S

    Fang, J. J., Faber, S. M., Koo, D. C., et al. 2018, ApJ, 858, 100, doi: 10.3847/1538-4357/aabcba

    work page doi:10.3847/1538-4357/aabcba 2018

  26. [26]

    J., Sazonova, E., et al

    Ferreira, L., Conselice, C. J., Sazonova, E., et al. 2023, ApJ, 955, 94, doi: 10.3847/1538-4357/acec76

    work page doi:10.3847/1538-4357/acec76 2023

  27. [27]

    2023, STScI/MAST, doi: 10.17909/Z7P0-8481

    Finkelstein, S., Bagley, M., & Yang, G. 2023, STScI/MAST, doi: 10.17909/Z7P0-8481

    work page doi:10.17909/z7p0-8481 2023

  28. [28]

    L., Bagley, M

    Finkelstein, S. L., Bagley, M. B., Arrabal Haro, P., et al. 2025, ApJL, 983, L4, doi: 10.3847/2041-8213/adbbd3

    work page doi:10.3847/2041-8213/adbbd3 2025

  29. [29]

    2000, AJ, 120, 2206, doi: 10.1086/316803

    Fontana, A., D’Odorico, S., Poli, F., et al. 2000, AJ, 120, 2206, doi: 10.1086/316803

    work page doi:10.1086/316803 2000

  30. [30]

    S., Baba, J., Saitoh, T

    Fujii, M. S., Baba, J., Saitoh, T. R., et al. 2011, ApJ, 730, 109, doi: 10.1088/0004-637X/730/2/109

    work page doi:10.1088/0004-637x/730/2/109 2011

  31. [31]

    2025, A&A, 699, A343, doi: 10.1051/0004-6361/202555504

    Genin, A., Shuntov, M., Brammer, G., et al. 2025, A&A, 699, A343, doi: 10.1051/0004-6361/202555504

    work page doi:10.1051/0004-6361/202555504 2025

  32. [32]

    M.,¨Ubler, H., et al

    Genzel, R., F¨ orster Schreiber, N. M.,¨Ubler, H., et al. 2017, Nature, 543, 397, doi: 10.1038/nature21685

    work page doi:10.1038/nature21685 2017

  33. [33]

    H., ¨Ubler, H., et al

    Genzel, R., Price, S. H., ¨Ubler, H., et al. 2020, ApJ, 902, 98, doi: 10.3847/1538-4357/abb0ea

    work page doi:10.3847/1538-4357/abb0ea 2020

  34. [34]

    1965, MNRAS, 130, 125, doi: 10.1093/mnras/130.2.125

    Goldreich, P., & Lynden-Bell, D. 1965, MNRAS, 130, 125, doi: 10.1093/mnras/130.2.125

    work page doi:10.1093/mnras/130.2.125 1965

  35. [35]

    Grand, R. J. J., Kawata, D., & Cropper, M. 2013, A&A, 553, A77, doi: 10.1051/0004-6361/201321308

    work page doi:10.1051/0004-6361/201321308 2013

  36. [36]

    Koekemoer, A. M. 2012, ApJ, 757, 120, doi: 10.1088/0004-637X/757/2/120

    work page doi:10.1088/0004-637x/757/2/120 2012

  37. [37]

    C., Bell, E

    Guo, Y., Ferguson, H. C., Bell, E. F., et al. 2015, ApJ, 800, 39, doi: 10.1088/0004-637X/800/1/39

    work page doi:10.1088/0004-637x/800/1/39 2015

  38. [38]

    F., et al

    Guo, Y., Rafelski, M., Bell, E. F., et al. 2018, ApJ, 853, 108, doi: 10.3847/1538-4357/aaa018

    work page doi:10.3847/1538-4357/aaa018 2018

  39. [39]

    L., et al

    Guo, Y., Jogee, S., Finkelstein, S. L., et al. 2023, ApJL, 945, L10, doi: 10.3847/2041-8213/acacfb

    work page doi:10.3847/2041-8213/acacfb 2023

  40. [40]

    E., Bamford, S

    Hart, R. E., Bamford, S. P., Hayes, W. B., et al. 2017, MNRAS, 472, 2263, doi: 10.1093/mnras/stx2137

    work page doi:10.1093/mnras/stx2137 2017

  41. [41]

    Kruit, P. C. 2005, A&A, 444, 101, doi: 10.1051/0004-6361:20053012

    work page doi:10.1051/0004-6361:20053012 2005

  42. [42]

    Hubble, E. P. 1926, ApJ, 64, 321, doi: 10.1086/143018

    work page doi:10.1086/143018 1926

  43. [43]

    G., Angeloudi, E., et al

    Huertas-Company, M., Iyer, K. G., Angeloudi, E., et al. 2024, A&A, 685, A48, doi: 10.1051/0004-6361/202346800

    work page doi:10.1051/0004-6361/202346800 2024

  44. [44]

    H., & Toomre, A

    Julian, W. H., & Toomre, A. 1966, ApJ, 146, 810, doi: 10.1086/148957

    work page doi:10.1086/148957 1966

  45. [45]

    S., Silverman, J

    Kalita, B. S., Silverman, J. D., Daddi, E., et al. 2024, ApJ, 960, 25, doi: 10.3847/1538-4357/acfee4

    work page doi:10.3847/1538-4357/acfee4 2024

  46. [46]

    S., Rose, C., Vanderhoof, B

    Kartaltepe, J. S., Rose, C., Vanderhoof, B. N., et al. 2023, ApJL, 946, L15, doi: 10.3847/2041-8213/acad01

    work page doi:10.3847/2041-8213/acad01 2023

  47. [47]

    A., Weiner, B

    Kassin, S. A., Weiner, B. J., Faber, S. M., et al. 2012, ApJ, 758, 106, doi: 10.1088/0004-637X/758/2/106

    work page doi:10.1088/0004-637x/758/2/106 2012

  48. [48]

    , keywords =

    Kendall, S., Kennicutt, R. C., & Clarke, C. 2011, MNRAS, 414, 538, doi: 10.1111/j.1365-2966.2011.18422.x

    work page doi:10.1111/j.1365-2966.2011.18422.x 2011

  49. [49]

    2024, ApJL, 968, L15, doi: 10.3847/2041-8213/ad43eb

    Kuhn, V., Guo, Y., Martin, A., et al. 2024, ApJL, 968, L15, doi: 10.3847/2041-8213/ad43eb

    work page doi:10.3847/2041-8213/ad43eb 2024

  50. [50]

    R., Shapley, A

    Law, D. R., Shapley, A. E., Steidel, C. C., et al. 2012, Nature, 487, 338, doi: 10.1038/nature11256 Le Conte, Z. A., Gadotti, D. A., Ferreira, L., et al. 2024, MNRAS, 530, 1984, doi: 10.1093/mnras/stae921

    work page doi:10.1038/nature11256 2012

  51. [51]

    C., & Shu, F

    Lin, C. C., & Shu, F. H. 1964, ApJ, 140, 646, doi: 10.1086/147955

    work page doi:10.1086/147955 1964

  52. [52]

    C., & Shu, F

    Lin, C. C., & Shu, F. H. 1966, Proceedings of the National Academy of Science, 55, 229, doi: 10.1073/pnas.55.2.229

    work page doi:10.1073/pnas.55.2.229 1966

  53. [53]

    L., Krawczyk, C., et al

    Lingard, T., Masters, K. L., Krawczyk, C., et al. 2021, MNRAS, 504, 3364, doi: 10.1093/mnras/stab1072 14

    work page doi:10.1093/mnras/stab1072 2021

  54. [54]

    F., & Quataert, E

    Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166, doi: 10.1111/j.1365-2966.2010.17432.x

    work page doi:10.1111/j.1365-2966.2010.17432.x 2011

  55. [55]

    2022, arXiv e-prints, arXiv:2201.03545, doi: 10.48550/arXiv.2201.03545

    Liu, Z., Mao, H., Wu, C.-Y., et al. 2022, arXiv e-prints, arXiv:2201.03545, doi: 10.48550/arXiv.2201.03545

    work page doi:10.48550/arxiv.2201.03545 2022

  56. [56]

    2023, ApJ, 955, 106, doi: 10.3847/1538-4357/aced3e

    Martin, A., Guo, Y., Wang, X., et al. 2023, ApJ, 955, 106, doi: 10.3847/1538-4357/aced3e

    work page doi:10.3847/1538-4357/aced3e 2023

  57. [57]

    2024, A&A, 691, A240, doi: 10.1051/0004-6361/202451409

    Merlin, E., Santini, P., Paris, D., et al. 2024, A&A, 691, A240, doi: 10.1051/0004-6361/202451409

    work page doi:10.1051/0004-6361/202451409 2024

  58. [58]

    2014, ApJ, 787, 174, doi: 10.1088/0004-637X/787/2/174

    Michikoshi, S., & Kokubo, E. 2014, ApJ, 787, 174, doi: 10.1088/0004-637X/787/2/174

    work page doi:10.1088/0004-637x/787/2/174 2014

  59. [59]

    2024, ApJL, 976, L27, doi: 10.3847/2041-8213/ad7b17

    Nelson, E., Brammer, G., Gim´ enez-Arteaga, C., et al. 2024, ApJL, 976, L27, doi: 10.3847/2041-8213/ad7b17

    work page doi:10.3847/2041-8213/ad7b17 2024

  60. [60]

    H., Kim, W.-T., & Lee, H

    Oh, S. H., Kim, W.-T., & Lee, H. M. 2015, ApJ, 807, 73, doi: 10.1088/0004-637X/807/1/73

    work page doi:10.1088/0004-637x/807/1/73 2015

  61. [61]

    , keywords =

    Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196, doi: 10.1093/mnras/stz2338

    work page doi:10.1093/mnras/stz2338 2019

  62. [62]

    E., & Dobbs, C

    Pringle, J. E., & Dobbs, C. L. 2019, MNRAS, 490, 1470, doi: 10.1093/mnras/stz2694

    work page doi:10.1093/mnras/stz2694 2019

  63. [63]

    A., & Mosenkov, A

    Usachev, P. A., & Mosenkov, A. V. 2022, Astronomy Letters, 48, 644, doi: 10.1134/S1063773722110111

    work page doi:10.1134/s1063773722110111 2022

  64. [64]

    A., & Mosenkov, A

    Usachev, P. A., & Mosenkov, A. V. 2023, A&A, 680, L14, doi: 10.1051/0004-6361/202348449

    work page doi:10.1051/0004-6361/202348449 2023

  65. [65]

    2023, STScI/MAST, doi: 10.17909/8TDJ-8N28

    Rieke, M., Robertson, B., Tacchella, S., et al. 2023, STScI/MAST, doi: 10.17909/8TDJ-8N28

    work page doi:10.17909/8tdj-8n28 2023

  66. [66]

    J., Robertson, B., Tacchella, S., et al

    Rieke, M. J., Robertson, B., Tacchella, S., et al. 2023, ApJS, 269, 16, doi: 10.3847/1538-4365/acf44d

    work page doi:10.3847/1538-4365/acf44d 2023

  67. [67]

    W., Roberts, M

    Roberts, Jr., W. W., Roberts, M. S., & Shu, F. H. 1975, ApJ, 196, 381, doi: 10.1086/153421

    work page doi:10.1086/153421 1975

  68. [68]

    1961, The Hubble Atlas of Galaxies

    Sandage, A. 1961, The Hubble Atlas of Galaxies

    1961

  69. [69]

    S., & Reshetnikov, V

    Savchenko, S. S., & Reshetnikov, V. P. 2013, MNRAS, 436, 1074, doi: 10.1093/mnras/stt1627

    work page doi:10.1093/mnras/stt1627 2013

  70. [70]

    A., & Carlberg, R

    Sellwood, J. A., & Carlberg, R. G. 1984, ApJ, 282, 61, doi: 10.1086/162176

    work page doi:10.1086/162176 1984

  71. [71]

    Shannon, C. E. 1948, Bell Labs Technical Journal, 27, 379, doi: 10.1002/j.1538-7305.1948.tb01338.x

    work page doi:10.1002/j.1538-7305.1948.tb01338.x 1948

  72. [72]

    2022, Galaxies, 10, 100, doi: 10.3390/galaxies10050100

    Shields, D., Boe, B., Pfountz, C., et al. 2022, Galaxies, 10, 100, doi: 10.3390/galaxies10050100

    work page doi:10.3390/galaxies10050100 2022

  73. [73]

    J., Giroux, M

    Smith, B. J., Giroux, M. L., & Struck, C. 2022, AJ, 164, 146, doi: 10.3847/1538-3881/ac88c5

    work page doi:10.3847/1538-3881/ac88c5 2022

  74. [74]

    L., Gillman, S., Cortese, L., et al

    Tiley, A. L., Gillman, S., Cortese, L., et al. 2021, MNRAS, 506, 323, doi: 10.1093/mnras/stab1692

    work page doi:10.1093/mnras/stab1692 2021

  75. [75]

    1972, title Galactic Bridges and Tails , , 178, 623, 10.1086/151823

    Toomre, A., & Toomre, J. 1972, ApJ, 178, 623, doi: 10.1086/151823

    work page doi:10.1086/151823 1972

  76. [76]

    2021, Science, 372, 1201, doi: 10.1126/science.abe9680

    Tsukui, T., & Iguchi, S. 2021, Science, 372, 1201, doi: 10.1126/science.abe9680

    work page doi:10.1126/science.abe9680 2021

  77. [77]

    2023, The Journal of Open Source Software, 8, 5312, doi: 10.21105/joss.05312

    Walmsley, M., Allen, C., Aussel, B., et al. 2023, The Journal of Open Source Software, 8, 5312, doi: 10.21105/joss.05312

    work page doi:10.21105/joss.05312 2023

  78. [78]

    2024, AJ, 168, 264, doi: 10.3847/1538-3881/ad8632

    Wei, J., Xu, Y., Lin, Z., et al. 2024, AJ, 168, 264, doi: 10.3847/1538-3881/ad8632

    work page doi:10.3847/1538-3881/ad8632 2024

  79. [79]

    W., Lintott, C

    Willett, K. W., Lintott, C. J., Bamford, S. P., et al. 2013, MNRAS, 435, 2835, doi: 10.1093/mnras/stt1458

    work page doi:10.1093/mnras/stt1458 2013

  80. [80]

    2023, ApJL, 942, L1, doi: 10.3847/2041-8213/aca652

    Wu, Y., Cai, Z., Sun, F., et al. 2023, ApJL, 942, L1, doi: 10.3847/2041-8213/aca652

    work page doi:10.3847/2041-8213/aca652 2023

Showing first 80 references.