Pith. sign in

REVIEW 1 major objections 5 minor 1 cited by

The impact of spiral arms on the star formation life cycle

T0 review · 1 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Spiral arms do not change how long molecular clouds live, across 22 nearby spiral galaxies.

desk verdict Solid environment-split extension of the Heisenberg method; the cloud-lifetime null result is robust, and the higher inter-arm SFE is plausible but needs a leakage test. read the letter →

arxiv 2505.10908 v1 pith:3LOVKPR6 submitted 2025-05-16 astro-ph.GA

Andrea Romanelli , Mélanie Chevance , J. M. Diederik Kruijssen , Lise Ramambason , Miguel Querejeta , Mederic Boquien , Daniel A. Dale , Jakob den Brok
show 13 more authors
Simon C. O. Glover Kathryn Grasha Annie Hughes Jaeyeon Kim Steven Longmore Sharon E. Meidt José Eduardo Mendez-Delgado Lukas Neumann Jérôme Pety Eva Schinnerer Rowan Smith Jiayi Sun Thomas G. Williams
This is my paper · ORCID
classification astro-ph.GA
keywords galaxyevolutionspiralarmsgiantmolecularcloudsstarformationefficiencycloudlifetimestellarfeedbackinter-armregionsnearbygalaxies
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 asks whether spiral arms actively trigger star formation by changing how long molecular clouds live or how efficiently they turn gas into stars. Using a statistical technique that reads the cloud-to-star timeline from the small-scale spatial decorrelation of CO and H-alpha emission, the authors compare cloud lifetimes, feedback timescales, region spacing, and integrated star formation efficiency in arm and inter-arm regions of 22 nearby spiral galaxies. They find that cloud lifetimes are statistically indistinguishable between the two environments (Anderson–Darling test $p=0.52$; Wilcoxon test $p=0.85$), and that feedback timescales are very similar as well. Inter-arm regions show a slightly higher integrated star formation efficiency (median 3.8 percent versus 2.9 percent; $p=0.012$), while star-forming regions are more tightly packed in arms. If right, the result removes a dominant triggering role for spiral arms in the local cloud-to-star cycle and shifts the emphasis to gas accumulation rather than direct stimulation.

What carries the argument

The load-bearing object is the statistical method of the “Uncertainty Principle for Star Formation”, which translates the observed small-scale scatter of the gas-to-star flux ratio into the durations of the successive phases of the cloud-to-star cycle. Emission peaks in CO(2-1) and H-alpha maps are located, apertures of increasing size are placed around them, and the measured decorrelation between molecular gas and star formation tracers is fitted with a function of three parameters: the cloud lifetime (the CO-visible phase), the feedback timescale (the phase where both tracers overlap), and the characteristic separation length between independent regions. The absolute calibration comes from the known H-alpha-emitting lifetime of a young stellar population, adjusted for metallicity. The comparison between environments rests on morphological masks that split each galaxy into spiral-arm and inter-arm zones; cloud lifetimes, feedback timescales, separations, and efficiencies are then compared with Anderson–Darling and Wilcoxon signed-rank tests.

What would settle it

Measure the inter-arm star formation efficiency after removing diffuse ionised gas or using an extinction-insensitive tracer such as radio free-free emission, and check whether the inter-arm SFE excess (3.8 versus 2.9 percent) persists; if the excess disappears, the paper's only statistically clear environmental difference collapses, while the cloud-lifetime equality would remain standing.

Watch

Extended reading notes

Core claim

The central claim is that the duration of the molecular-cloud phase of the star formation life cycle is independent of spiral structure: molecular clouds live for the same amount of time whether they sit in a spiral arm or in an inter-arm region, with lifetimes of roughly 5 to 40 Myr in both. The feedback timescale, during which CO and H-alpha emission overlap before stellar feedback disperses the cloud, is also similar, though its median is slightly higher in arms. The paper's main environmental differences are spatial: independent star-forming regions are separated by about 100 pc less in arms than between them, and the integrated star formation efficiency per cloud is modestly higher in inter-arm regions (median 3.8 percent versus 2.9 percent). Together these results are read as evidence that spiral arms gather molecular gas and raise the density of star-forming sites without changing the per-cloud pace of the gas-to-star cycle, so they are unlikely to be the trigger of star formation.

Load-bearing premise

The central comparison assumes the arm and inter-arm masks cleanly separate the two environments; in particular, the higher inter-arm star formation efficiency assumes that ionising photons from arm star-forming regions do not leak into inter-arm regions and inflate the measured star formation rate, an effect the paper notes could be present because the photon mean free path is comparable to or larger than the mask width.

Editorial extensions

If this is right

  • Spiral arms function as gas collectors rather than star formation triggers: they concentrate clouds and regions but do not alter the lifetime of the cloud phase.
  • Cloud lifetimes of roughly 5–40 Myr in both environments imply that cloud destruction is fast everywhere, consistent with feedback-regulated cycling rather than dynamical triggering by arms.
  • The shorter separation between independent regions in arms (about 100 pc) means a higher surface density of star-forming sites, which can explain why arms look like preferred star formation sites without any per-cloud efficiency boost.
  • A higher integrated SFE in inter-arm regions, if real, means integrated and instantaneous measures of star formation efficiency need not agree; per-event efficiency and depletion time are distinct diagnostics.
  • Models of galaxy evolution that assume arm passage triggers star formation would need revision, at least for the local, moderate-density galaxy population studied here.

Reading between the lines

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

  • An implicit test: the same analysis applied to flocculent or weakly armed galaxies should show the same cloud lifetimes if spiral structure is truly incidental to the cloud-to-star cycle; a difference there would hint that arm passage matters only in strongly sheared systems.
  • The leakage of ionising photons from arm regions into inter-arm masks is the main threat to the SFE contrast; using a recombination line insensitive to diffuse leakage, or masking diffuse ionised gas before measuring the SFR, would settle whether inter-arm gas really is more efficient.
  • Because the arm SFE is lower even though arm clouds are more massive and more tightly packed, this suggests the efficiency difference is set by local cloud-scale conditions (density contrast, confinement, feedback) rather than by large-scale dynamics — a distinction that could be tested against cloud virial parameters in the same galaxies.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 5 minor

Summary. This paper applies the Heisenberg statistical framework (Kruijssen et al. 2018) to 22 PHANGS spiral galaxies to measure the cloud lifetime (tCO), feedback timescale (tfb), region separation length (lambda), and integrated star formation efficiency (epsilon_SF) separately in spiral arms and inter-arm regions. The central finding is that the distributions of tCO are statistically indistinguishable between the two environments (Anderson-Darling p=0.52, Wilcoxon p=0.85), which the authors interpret as evidence that spiral arms do not dominate the triggering of star formation. Secondary results are that tfb shows similar distributions by the AD test but a marginally significant median difference by the Wilcoxon test (p=0.06), lambda is smaller in spiral arms (significant in both tests), and epsilon_SF is higher in inter-arm regions (medians 3.8% versus 2.9%; AD p=0.012). The paper explicitly acknowledges that ionizing photon leakage from arm HII regions into inter-arm masks could bias the epsilon_SF result but does not quantify the effect.

Significance. If the null result on cloud lifetime holds, it provides a strong, quantitative constraint on spiral-arm triggering models at cloud scale, using a homogeneous ALMA+Halpha sample and a publicly available statistical method. The paper's differential arm/inter-arm design avoids circularity in the environmental comparison, and the tCO result is insensitive to SFR calibration because it is anchored to a stellar-population reference timescale rather than to the SFR maps. The secondary SFE contrast, however, is the only statistically clear environmental difference and is vulnerable to systematic cross-contamination between masks; its robustness is therefore load-bearing for the paper's overall claim that spiral arms only accumulate gas without changing the cloud-to-star cycle. The paper would be strengthened by quantitative leakage bounds and a sensitivity analysis of the mask choice.

major comments (1)
  1. [Section 3, paragraph on reference timescale] The statement that the Halpha reference timescale tstar,ref varies only between 4.30 and 4.59 Myr across the sample and is similar in arms and inter-arms is reassuring, but the CO luminosity-weighted averaging of tstar,ref per environment could in principle introduce a small systematic offset if the CO-bright regions in arms have systematically different metallicities than those in inter-arms. The paper reports the ranges but does not give the per-environment medians or the maximum arm/inter-arm difference per galaxy. Please report the distribution of tstar,ref separately for the two environments and, if the difference is indeed negligible as stated, this will fully close the concern. If a small offset exists, its effect on tCO and epsilon_SF should be propagated into the error bars.
minor comments (5)
  1. [Table 1] The Anderson-Darling test statistic reported for tCO is -0.17, which is negative; the k-sample AD statistic is normally non-negative. Please clarify whether this is a standardized statistic or whether a different formulation (e.g., the Tk statistic) is used, and consider reporting only the p-values to avoid confusion.
  2. [Section 3 and Figure 3 caption] The caption of Figure 3 refers to the Python module 'statmodels'; the correct package name is 'statsmodels'. Please correct the typo.
  3. [Table A.1, notes] The note defining the upper-limit criterion uses lambda/lap,min < 1.5, but lap,min is not defined in the text. Please define lap,min explicitly (presumably the spatial resolution of the coarser map) when this criterion is introduced in Section 3.
  4. [Section 4.3] The discussion of lambda correctly interprets the shorter separation in spiral arms as a consequence of higher peak density, but the statement that 'this result would be strengthened even further by obtaining exact measurements instead of upper limits' is vague; please indicate how many of the 22 galaxies have upper limits on lambda and whether the AD p-value changes when those galaxies are removed.
  5. [Section 2.2] The description of the CO(2-1) moment-0 maps uses the term 'broad masking scheme' without a reference; please add the appropriate citation for the masking scheme (e.g., Leroy et al. 2021a) or define it in one sentence.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the arm versus inter-arm comparison is differential, externally calibrated, and does not reduce any measured quantity to a fitted input.

full rationale

The derivation chain is self-contained with respect to the paper's central claim. Cloud lifetimes are obtained by applying the publicly available Heisenberg code (Kruijssen et al. 2018) to CO and Halpha maps; the code fits relative phase durations and converts them to absolute timescales using an external stellar-population reference timescale (Haydon et al. 2020b), which is a population-synthesis input rather than a fitted result of this paper. The arm versus inter-arm comparison is differential: environmental masks are morphological (Querejeta et al. 2021), and no parameter is fitted to the arm/inter-arm difference and then reported as a prediction. The main null result (similar tCO distributions; AD p=0.52, Wilcoxon p=0.85) follows directly from the measured values and is insensitive to the SFR map choice. The integrated SFE in Eq. 5 is tCO divided by tcomp_dep, with tCO identical between environments and tcomp_dep computed from observed compact CO surface densities and the global SFR maps; the higher inter-arm SFE is a direct arithmetic consequence of those inputs, not a hidden fitted quantity. The paper explicitly flags in Sect. 5 that ionizing-photon leakage from arm HII regions into inter-arm masks could overestimate the inter-arm SFE, so this is an acknowledged systematic uncertainty rather than a circular step. Same-group citations occur (e.g., Heisenberg method papers and the Kruijssen et al. 2024 multi-scale analysis in footnote 3), but the absolute lifetimes agree with independent measurements (e.g., Kawamura et al. 2009; Grasha et al. 2019) and the central null result does not rest on a self-citation chain. No circular reduction was found.

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

The central quantitative outputs rest on the Heisenberg model with three fitted parameters per region, external calibration inputs (reference Halpha timescale, alpha_CO, R21), and the assumption that the arm/inter-arm masks are unbiased. No new physical entities are introduced. The only hand-tuned parameter unique to this paper is the per-galaxy mask widening kernel radius.

free parameters (4)
  • Cloud lifetime tCO (per region) = Table A.1, 5-40 Myr across environments
    Output of the Heisenberg fit; central to the null result.
  • Feedback timescale tfb (per region) = Table A.1, 1-10 Myr; upper limits for starred galaxies
    Output of the Heisenberg fit; compared between environments.
  • Region separation length lambda (per region) = Table A.1, roughly 90-400 pc
    Output of the Heisenberg fit; significantly different between environments.
  • Spiral arm mask widening kernel radius = Not reported; set per galaxy by visual inspection
    Controls the arm/inter-arm classification; all derived quantities depend on it (Section 2.4).
assumptions (5)
  • domain assumption The Heisenberg model of independent star-forming regions captures the CO-Halpha decorrelation as a two-timescale life cycle.
    All timescales tCO, tfb and lambda are inferred by fitting this model to the observed flux ratio versus aperture (Section 3). If the model is wrong, the reported lifetimes are not physical.
  • domain assumption The reference Halpha timescale t_star_ref is about 4.3 Myr and follows the metallicity scaling of Eq. 4.
    Converts the Heisenberg relative timescales to absolute Myr (Section 3).
  • domain assumption The CO-to-H2 factor alpha_CO and the CO(2-1)/CO(1-0) ratio R21 are the same in arms and inter-arms.
    Any variation would change the depletion time and hence epsilon_SF (Section 4.4); the authors note there is no consensus.
  • domain assumption Halpha emission is not significantly extincted internally for most of the sample (Sigma_mol below about 20 M_sun pc^-2).
    Used to justify using Halpha as SFR tracer without internal extinction correction (Sections 2.3 and 5).
  • domain assumption The environmental masks correctly separate spiral arms from inter-arm regions after the visual widening step.
    All measurements are made on masked subregions (Section 2.4).

how reviews work

0 comments
Cite this review

Pith. "Pith review of The impact of spiral arms on the star formation life cycle." pith.science (2026). https://pith.science/paper/3LOVKPR6

@misc{pith2026250510908,
  author       = {Pith},
  title        = {Pith review of: The impact of spiral arms on the star formation life cycle},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3LOVKPR6}},
  note         = {Machine review of arXiv:2505.10908}
}
read the original abstract

The matter cycle between gas clouds and stars in galaxies plays a crucial role in regulating galaxy evolution through feedback mechanisms. In turn, the local and global galactic environments shape the interstellar medium and provide the initial conditions for star formation, potentially affecting the properties of this small-scale matter cycle. In particular, spiral arms have been proposed to play a pivotal role in the star formation life cycle, by enhancing the gas density and triggering star formation. However, their exact role is still debated. In this paper, we investigate the role of spiral arms in the giant molecular cloud evolutionary life cycle and on the star formation process in a sample of 22 nearby spiral galaxies from the PHANGS survey. We measure the cloud lifetime, the feedback timescale, the typical distance between independent regions and the star formation efficiency in spiral arms and inter-arm regions separately. We find that the distributions of the cloud lifetime as well as the feedback timescale are similar in both environments. This result suggests that spiral arms are unlikely to play a dominant role in triggering star formation. By contrast, the star formation efficiency appears to be slightly higher in inter-arm regions compared to spiral arms.

Figures

Figures reproduced from arXiv: 2505.10908 by the authors.

Figure 2
Figure 2. Molecular gas surface density versus SFR surface density aver￾aged over the area enclosed by arm and inter-arm regions. Dashed lines mark the position of the median of each sample. 2.5.3. αCO conversion factor maps To convert the observed CO flux to a gas mass, we adopt a metallicity-dependent αCO conversion factor, following Sun et al. (2020): αCO = 4.35 × Z −1.6 M⊙(K km s−1 pc2 ) −1 , (1) where Z = (O/H) is the ox… view at source ↗
Figure 3
Figure 3. Cumulative distribution function of the cloud lifetime (top left), feedback timescale (top right), region separation length (bottom left), and SFE (bottom right). Each panel shows the c.d.f. for the parameters calculated in spiral arms (orange) and inter-arm regions (blue), as well as for the full galaxies (red, Kim et al. 2022) as a thick solid line, and the smoothed c.d.f as a shaded line. In cases where only an u… view at source ↗
Figure 4
Figure 4. Cloud lifetime (top left), feedback timescale (top right), region separation length (bottom left), and integrated SFE (bottom right) measured through our statistical analysis. For each parameter, we report the values calculated in the spiral arms on the x-axis and in the inter-arm regions on the y-axis for each galaxy. The grey line shows the one-to-one relation. Each data point is colour-coded according to the aver… view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Scatter plot of the feedback timescale as a function of the sur￾face density contrast between the CO emission peaks and the galactic average (εCO; see text). Spiral arms are represented in orange, inter-arm regions in blue. The solid lines represent the linear models f…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Unveiling the Milky Way with a Gaia DR3 census of OB-type stars within 2 kpc. I. Tracing local Galactic structure, massive star-forming regions and core-collapse supernova progenitors

    astro-ph.GA 2026-07 conditional novelty 4.0 of 10

    A Gaia DR3-based census of 105,971 OB stars within 2 kpc maps local Galactic structure and identifies over 4,200 core-collapse supernova or black hole progenitor candidates.

Reference graph

Works this paper leans on

103 extracted references · 42 canonical work pages · cited by 1 Pith paper

  1. [1]

    Anderson, T. W. & Darling, D. A. 1954, Journal of the American Statistical As- sociation, 49, 765

    1954

  2. [2]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481

    2009

  3. [3]

    2022, A&A, 659, A26

    Belfiore, F., Santoro, F., Groves, B., et al. 2022, A&A, 659, A26

    2022

  4. [4]

    2008, AJ, 136, 2846

    Bigiel, F., Leroy, A., Walter, F., et al. 2008, AJ, 136, 2846

    2008

  5. [5]

    1990, in Astronomical Society of the Pacific Conference Series, V ol

    Blitz, L. 1990, in Astronomical Society of the Pacific Conference Series, V ol. 12, The Evolution of the Interstellar Medium, ed. L. Blitz, 273–289

    1990

  6. [6]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151 Cedrés, B., Cepa, J., Bongiovanni, Á., et al. 2013, A&A, 560, A59

    2004

  7. [7]

    & Beckman, J

    Cepa, J. & Beckman, J. E. 1990, ApJ, 349, 497

    1990

  8. [8]

    J., et al

    Chen, Q.-H., Grasha, K., Battisti, A. J., et al. 2024, MNRAS, 534, 883

    2024

Show all 103 references
  1. [9]

    Chevance, M., Kruijssen, J. M. D., Hygate, A. P. S., et al. 2020, MNRAS, 493, 2872

    2020

  2. [10]

    Chevance, M., Kruijssen, J. M. D., Krumholz, M. R., et al. 2022, MNRAS, 509, 272

    2022

  3. [11]

    Chevance, M., Kruijssen, J. M. D., & Longmore, S. N. 2025, arXiv e-prints, arXiv:2501.13160

    2025 arXiv

  4. [12]

    R., McLeod, A

    Chevance, M., Krumholz, M. R., McLeod, A. F., et al. 2023, in Astronomical Society of the Pacific Conference Series, V ol. 534, Protostars and Planets VII, ed. S. Inutsuka, Y . Aikawa, T. Muto, K. Tomida, & M. Tamura, 1

    2023

  5. [13]

    E., Schinnerer, E., et al

    Colombo, D., Meidt, S. E., Schinnerer, E., et al. 2014, ApJ, 784, 4

    2014

  6. [14]

    2017, A&A, 601, A146

    Corbelli, E., Braine, J., Bandiera, R., et al. 2017, A&A, 601, A146

    2017

  7. [15]

    A., Young, L

    Davis, T. A., Young, L. M., Crocker, A. F., et al. 2014, MNRAS, 444, 3427 den Brok, J. S., Bigiel, F., Sliwa, K., et al. 2022, A&A, 662, A89 den Brok, J. S., Chatzigiannakis, D., Bigiel, F., et al. 2021, MNRAS, 504, 3221

    2014

  8. [16]

    & Baba, J

    Dobbs, C. & Baba, J. 2014, PASA, 31, e035

    2014

  9. [17]

    Dobbs, C. L. 2011, in EAS Publications Series, V ol. 52, EAS Publications Series, ed. M. Röllig, R. Simon, V . Ossenkopf, & J. Stutzki, 87–93 Donovan Meyer, J., Koda, J., Momose, R., et al. 2013, ApJ, 772, 107

    2011

  10. [18]

    & Solomon, P

    Downes, D. & Solomon, P. M. 1998, ApJ, 507, 615 Article number, page 10 of 12 A. Romanelli et al.: The impact of spiral arms on the star formation life cycle

    1998

  11. [19]

    2009, ApJ, 697, 1870

    Egusa, F., Kohno, K., Sofue, Y ., Nakanishi, H., & Komugi, S. 2009, ApJ, 697, 1870

    2009

  12. [20]

    2004, PASJ, 56, L45

    Egusa, F., Sofue, Y ., & Nakanishi, H. 2004, PASJ, 56, L45

    2004

  13. [21]

    Elmegreen, B. G. & Elmegreen, D. M. 1986, ApJ, 311, 554

    1986

  14. [22]

    2022, A&A, 659, A191

    Emsellem, E., Schinnerer, E., Santoro, F., et al. 2022, A&A, 659, A191

    2022

  15. [23]

    L., Rosolowsky, E., & Blitz, L

    Engargiola, G., Plambeck, R. L., Rosolowsky, E., & Blitz, L. 2003, ApJS, 149, 343

    2003

  16. [24]

    Y ., & Kravtsov, A

    Feldmann, R., Gnedin, N. Y ., & Kravtsov, A. V . 2011, ApJ, 732, 115

    2011

  17. [25]

    Fitzpatrick, E. L. 1999, PASP, 111, 63

    1999

  18. [26]

    W., Walter, F., & Leroy, A

    Foyle, K., Rix, H. W., Walter, F., & Leroy, A. K. 2010, ApJ, 725, 534

    2010

  19. [27]

    2014, ApJ, 780, 36

    Fukui, Y ., Ohama, A., Hanaoka, N., et al. 2014, ApJ, 780, 36

    2014

  20. [28]

    1993, A&A, 274, 123

    Garcia-Burillo, S., Guelin, M., & Cernicharo, J. 1993, A&A, 274, 123

    1993

  21. [29]

    Gensior, J., Kruijssen, J. M. D., & Keller, B. W. 2020, MNRAS, 495, 199

    2020

  22. [30]

    2019, MNRAS, 483, 4707

    Grasha, K., Calzetti, D., Adamo, A., et al. 2019, MNRAS, 483, 4707

    2019

  23. [31]

    L., Quillen, A

    Henry, A. L., Quillen, A. C., & Gutermuth, R. 2003, AJ, 126, 2831

    2003

  24. [32]

    2018, PASJ, 70, 73

    Hirota, A., Egusa, F., Baba, J., et al. 2018, PASJ, 70, 73

    2018

  25. [33]

    F., Garcia-Burillo, S., & Stutzki, J

    Hitschfeld, M., Kramer, C., Schuster, K. F., Garcia-Burillo, S., & Stutzki, J. 2009, A&A, 495, 795

    2009

  26. [34]

    E., Colombo, D., et al

    Hughes, A., Meidt, S. E., Colombo, D., et al. 2013, ApJ, 779, 46

    2013

  27. [35]

    Hygate, A. P. S., Kruijssen, J. M. D., Chevance, M., et al. 2019, MNRAS, 488, 2800

    2019

  28. [36]

    Jeffreson, S. M. R. & Kruijssen, J. M. D. 2018, MNRAS, 476, 3688

    2018

  29. [37]

    2009, ApJS, 184, 1

    Kawamura, A., Mizuno, Y ., Minamidani, T., et al. 2009, ApJS, 184, 1

    2009

  30. [38]

    1998, ApJ, 498, 541

    Kennicutt, Robert C., J. 1998, ApJ, 498, 541

    1998

  31. [39]

    Kennicutt, R. C. & Evans, N. J. 2012, ARA&A, 50, 531

    2012

  32. [40]

    Kim, J., Chevance, M., Kruijssen, J. M. D., et al. 2023, ApJ, 944, L20

    2023

  33. [41]

    Kim, J., Chevance, M., Kruijssen, J. M. D., et al. 2022, MNRAS, 516, 3006

    2022

  34. [42]

    Kim, J., Chevance, M., Kruijssen, J. M. D., et al. 2021, MNRAS, 504, 487

    2021

  35. [43]

    Knapen, J. H. & Beckman, J. E. 1996, MNRAS, 283, 251

    1996

  36. [44]

    2013, in Astronomical Society of the Pacific Conference Series, V ol

    Koda, J. 2013, in Astronomical Society of the Pacific Conference Series, V ol. 476, New Trends in Radio Astronomy in the ALMA Era: The 30th Anniver- sary of Nobeyama Radio Observatory, ed. R. Kawabe, N. Kuno, & S. Ya- mamoto, 49

    2013

  37. [45]

    2012, ApJ, 761, 41

    Koda, J., Scoville, N., Hasegawa, T., et al. 2012, ApJ, 761, 41

    2012

  38. [46]

    2009, ApJ, 700, L132

    Koda, J., Scoville, N., Sawada, T., et al. 2009, ApJ, 700, L132

    2009

  39. [47]

    A., Schinnerer, E., et al

    Kreckel, K., Blanc, G. A., Schinnerer, E., et al. 2016, ApJ, 827, 103

    2016

  40. [48]

    Kruijssen, J. M. D. 2014, Classical and Quantum Gravity, 31, 244006

    2014

  41. [49]

    Kruijssen, J. M. D., Chevance, M., Longmore, S. N., et al. 2024, OJA submitted, arXiv:2404.14495

    2024

  42. [50]

    Kruijssen, J. M. D. & Longmore, S. N. 2014, MNRAS, 439, 3239

    2014

  43. [51]

    Kruijssen, J. M. D., Schruba, A., Chevance, M., et al. 2019, Nature, 569, 519

    2019

  44. [52]

    Kruijssen, J. M. D., Schruba, A., Hygate, A. P. S., et al. 2018, MNRAS, 479, 1866

    2018

  45. [53]

    Krumholz, M. R. & McKee, C. F. 2005, ApJ, 630, 250

    2005

  46. [54]

    2007, PASJ, 59, 117

    Kuno, N., Sato, N., Nakanishi, H., et al. 2007, PASJ, 59, 117

    2007

  47. [55]

    E., Rosolowsky, E., et al

    Lang, P., Meidt, S. E., Rosolowsky, E., et al. 2020, ApJ, 897, 122

    2020

  48. [56]

    K., Sandstrom, K

    Leroy, A. K., Sandstrom, K. M., Lang, D., et al. 2019, ApJS, 244, 24

    2019

  49. [57]

    K., Schinnerer, E., Hughes, A., et al

    Leroy, A. K., Schinnerer, E., Hughes, A., et al. 2017, ApJ, 846, 71

    2017

  50. [58]

    K., Sun, J., Meidt, S., et al

    Leroy, A. K., Sun, J., Meidt, S., et al. 2025, arXiv e-prints, arXiv:2502.04481

    2025

  51. [59]

    K., Walter, F., Sandstrom, K., et al

    Leroy, A. K., Walter, F., Sandstrom, K., et al. 2013, AJ, 146, 19

    2013

  52. [60]

    2021, MNRAS, 505, 4048

    Liu, L., Bureau, M., Blitz, L., et al. 2021, MNRAS, 505, 4048

    2021

  53. [61]

    Lord, S. D. & Young, J. S. 1990, ApJ, 356, 135

    1990

  54. [62]

    2022, MNRAS, 514, 5035

    Lu, A., Boyce, H., Haggard, D., et al. 2022, MNRAS, 514, 5035

    2022

  55. [63]

    2009, ApJ, 707, 250

    Martig, M., Bournaud, F., Teyssier, R., & Dekel, A. 2009, ApJ, 707, 250

    2009

  56. [64]

    Mathis, J. S. 1986, ApJ, 301, 423

    1986

  57. [65]

    E., Hughes, A., Dobbs, C

    Meidt, S. E., Hughes, A., Dobbs, C. L., et al. 2015, ApJ, 806, 72

    2015

  58. [66]

    E., Leroy, A

    Meidt, S. E., Leroy, A. K., Querejeta, M., et al. 2021, ApJ, 913, 113

    2021

  59. [67]

    E., Leroy, A

    Meidt, S. E., Leroy, A. K., Rosolowsky, E., et al. 2018, ApJ, 854, 100

    2018

  60. [68]

    E., Schinnerer, E., García-Burillo, S., et al

    Meidt, S. E., Schinnerer, E., García-Burillo, S., et al. 2013, ApJ, 779, 45

    2013

  61. [69]

    & Sofue, Y

    Nakanishi, H. & Sofue, Y . 2003, PASJ, 55, 191

    2003

  62. [70]

    2010, ApJ, 722, L127

    Onodera, S., Kuno, N., Tosaki, T., et al. 2010, ApJ, 722, L127

    2010

  63. [71]

    2012, ApJ, 759, L27

    Padoan, P., Haugbølle, T., & Nordlund, Å. 2012, ApJ, 759, L27

    2012

  64. [72]

    Pilyugin, L. S. & Grebel, E. K. 2016, MNRAS, 457, 3678

    2016

  65. [73]

    S., Grebel, E

    Pilyugin, L. S., Grebel, E. K., Zinchenko, I. A., & Kniazev, A. Y . 2014, AJ, 148, 134

    2014

  66. [74]

    K., Meidt, S

    Querejeta, M., Leroy, A. K., Meidt, S. E., et al. 2024, A&A, 687, A293

    2024

  67. [75]

    2021, A&A, 656, A133

    Querejeta, M., Schinnerer, E., Meidt, S., et al. 2021, A&A, 656, A133

    2021

  68. [76]

    2019, A&A, 625, A19

    Querejeta, M., Schinnerer, E., Schruba, A., et al. 2019, A&A, 625, A19

    2019

  69. [77]

    E., Moore, T

    Ragan, S. E., Moore, T. J. T., Eden, D. J., et al. 2018, MNRAS, 479, 2361

    2018

  70. [78]

    Roberts, W. W. 1969, ApJ, 158, 123

    1969

  71. [79]

    & Blitz, L

    Rosolowsky, E. & Blitz, L. 2005, ApJ, 623, 826

    2005

  72. [80]

    K., et al

    Rosolowsky, E., Hughes, A., Leroy, A. K., et al. 2021, MNRAS, 502, 1218

    2021

  73. [81]

    J., et al

    Saintonge, A., Catinella, B., Tacconi, L. J., et al. 2017, ApJS, 233, 22 Sánchez, S. F., Barrera-Ballesteros, J. K., López-Cobá, C., et al. 2019, MNRAS, 484, 3042 Sánchez, S. F., Rosales-Ortega, F. F., Iglesias-Páramo, J., et al. 2014, A&A, 563, A49

    2017

  74. [82]

    2019, ApJ, 887, 49

    Schinnerer, E., Hughes, A., Leroy, A., et al. 2019, ApJ, 887, 49

    2019

  75. [83]

    & Leroy, A

    Schinnerer, E. & Leroy, A. K. 2024, ARA&A, 62, 369

    2024

  76. [84]

    E., Pety, J., et al

    Schinnerer, E., Meidt, S. E., Pety, J., et al. 2013, ApJ, 779, 42

    2013

  77. [85]

    1959, ApJ, 129, 243

    Schmidt, M. 1959, ApJ, 129, 243

    1959

  78. [86]

    Scholz, F. W. & Stephens, M. A. 1987, Journal of the American Statistical As- sociation, 82, 918

    1987

  79. [87]

    K., Walter, F., Sandstrom, K., & Rosolowsky, E

    Schruba, A., Leroy, A. K., Walter, F., Sandstrom, K., & Rosolowsky, E. 2010, ApJ, 722, 1699

    2010

  80. [88]

    Scoville, N. Z. & Hersh, K. 1979, ApJ, 229, 578

    1979

  81. [89]

    Scoville, N. Z. & Wilson, C. D. 2004, in Astronomical Society of the Pacific Conference Series, V ol. 322, The Formation and Evolution of Massive Young Star Clusters, ed. H. J. G. L. M. Lamers, L. J. Smith, & A. Nota, 245

    2004

  82. [90]

    Seigar, M. S. & James, P. A. 2002, MNRAS, 337, 1113

    2002

  83. [91]

    R., Howk, J

    Sembach, K. R., Howk, J. C., Ryans, R. S. I., & Keenan, F. P. 2000, ApJ, 528, 310

    2000

  84. [92]

    2019, PASJ, 71, S14

    Sorai, K., Kuno, N., Muraoka, K., et al. 2019, PASJ, 71, S14

    2019

  85. [93]

    Sun, B., Calzetti, D., & Battisti, A. J. 2024, ApJ, 973, 137

    2024

  86. [94]

    K., Rosolowsky, E., et al

    Sun, J., Leroy, A. K., Rosolowsky, E., et al. 2022, AJ, 164, 43

    2022

  87. [95]

    K., Schinnerer, E., et al

    Sun, J., Leroy, A. K., Schinnerer, E., et al. 2020, ApJ, 901, L8 V ogel, S. N., Kulkarni, S. R., & Scoville, N. Z. 1988, Nature, 334, 402

    2020

  88. [96]

    L., Chevance, M., Kruijssen, J

    Ward, J. L., Chevance, M., Kruijssen, J. M. D., et al. 2020, MNRAS, 497, 2286

    2020

  89. [97]

    L., Kruijssen, J

    Ward, J. L., Kruijssen, J. M. D., Chevance, M., Kim, J., & Longmore, S. N. 2022, MNRAS, 516, 4025

    2022

  90. [98]

    1945, Biometrics Bulletin, 1, 80

    Wilcoxon, F. 1945, Biometrics Bulletin, 1, 80

    1945

  91. [99]

    P., de Geus, E

    Williams, J. P., de Geus, E. J., & Blitz, L. 1994, ApJ, 428, 693

    1994

  92. [100]

    G., Kreckel, K., Belfiore, F., et al

    Williams, T. G., Kreckel, K., Belfiore, F., et al. 2022, MNRAS, 509, 1303

    2022

  93. [101]

    S., Joung, M

    Wood, K., Hill, A. S., Joung, M. R., et al. 2010, ApJ, 721, 1397

    2010

  94. [102]

    C., & Wang, J

    Yu, S.-Y ., Ho, L. C., & Wang, J. 2021, ApJ, 917, 88

    2021

  95. [103]

    A., Sarzi, M., et al

    Zabel, N., Davis, T. A., Sarzi, M., et al. 2020, MNRAS, 496, 2155 Article number, page 11 of 12 A&A proofs: manuscript no. main Appendix A: Best-fitting parameters Table A.1. Best-fitting parameters obtained from performing our statistical analysis to 22 spiral galaxies. Galax...

    2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.