Pith. sign in

REVIEW 2 major objections 5 minor 1 cited by

Constraining resolved extragalactic $R_{21}$ variation with well calibrated ALMA observations

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Using well-calibrated ALMA maps of 14 nearby galaxies, this paper finds the galaxy-to-galaxy scatter in the CO line ratio R21 is only about 0.05 dex, roughly half the previously reported values, and argues the earlier larger scatter was…

desk verdict A careful, genuinely useful ALMA line-ratio study; the headline 0.05 dex scatter is real but needs a tighter calibration argument. read the letter →

arxiv 2506.09125 v1 pith:5T7FNTS5 submitted 2025-06-10 astro-ph.GA

Jakob den Brok , Elias K. Oakes , Adam K. Leroy , Eric W. Koch , Antonio Usero , Erik W. Rosolowsky , Frank Bigiel , Jiayi Sun
show 8 more authors
Hao He Ashley T. Barnes Yixian Cao Fu-Heng Liang Hsi-An Pan Toshiki Saito Sumit K. Sarbadhicary Thomas G. Williams
This is my paper
classification astro-ph.GA
keywords COlineratiosR21moleculargasnearbygalaxiesALMAfluxcalibrationstarformationratesurfacedensityradiolines:
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

This paper tries to establish how much the CO(2-1)/CO(1-0) line ratio, R21, really varies between and within nearby star-forming galaxies once calibration systematics are controlled. Using new ALMA 7m+TP CO(1-0) maps of 12 galaxies plus two archival maps, matched to ALMA CO(2-1) maps of the same galaxies on a common 1.7 kpc grid, the authors find a galaxy-to-galaxy scatter of only 0.05 dex. That is about half or less the scatter reported by single-dish surveys, and the paper argues most of the old scatter was flux calibration noise rather than astrophysics. Within galaxies, R21 varies about 0.1 dex and rises toward centers, with the local star-formation surface density the best predictor. If correct, a fixed R21 = 0.64 is adequate for kpc-scale molecular gas masses across main-sequence galaxy samples, with calibration and the CO-to-H2 conversion factor dominating the systematics.

What carries the argument

The load-bearing object is the resolved line ratio $R_{21} = W_{\mathrm{CO(2-1)}}/W_{\mathrm{CO(1-0)}}$ measured from integrated-intensity maps at a common physical resolution of 1.7 kpc. The argument is carried by three matched elements: new ALMA 7m+TP CO(1-0) observations processed through the survey imaging pipeline to recover short-spacing flux; the companion ALMA CO(2-1) maps of the same galaxies; and a comparison of survey permutations that isolates the 10–20% systematic offsets single-dish flux calibration injects into $R_{21}$. Three averaging schemes—the area-weighted median, ratio-of-sums, and spectral stacking—cross-check the ratio estimates, and the stated ~5% ALMA relative calibration uncertainty is the yardstick against which the 0.05 dex scatter is judged.

What would settle it

Re-observe the same 14 galaxies' CO(1-0) and CO(2-1) emission with two independent, absolutely calibrated facilities and recompute the galaxy-to-galaxy scatter; if the scatter rises back above 0.1 dex, the low ALMA value is a calibration artifact, not an intrinsic property.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the intrinsic galaxy-to-galaxy variation of R21 among nearby main-sequence star-forming galaxies is about 0.05 dex, smaller than the >0.1 dex quoted in earlier work, and that this smaller scatter is the astrophysical signal once flux calibration is controlled by using ALMA for both transitions. The sample-wide area-weighted mean ratio is 0.64, with central regions (r<2 kpc) rising to about 0.75, and typical internal scatter of 0.1 dex. The paper further finds that R21 correlates with SFR surface density with a power-law slope of 0.10–0.12 depending on binning, anticorrelates with galactocentric radius and metallicity, and that the previously reported large scatter largely disappears when both lines come from the same, well-calibrated facility. As a practical consequence, the authors conclude that a fixed $R_{21}=0.64$ does not significantly bias kpc-scale molecular gas mass estimates derived from CO(2-1) for large samples, while flux calibration and the CO-to-H2 conversion factor contribute more systematic scatter.

Load-bearing premise

The relative flux calibration between the new CO(1-0) and the CO(2-1) maps is stable to about 5 percent across all 14 galaxies, so the measured 0.05 dex galaxy-to-galaxy scatter reflects intrinsic astrophysical variation rather than leftover calibration noise.

Editorial extensions

If this is right

  • A fixed $R_{21}=0.64$ can be used to convert kpc-scale CO(2-1) maps to molecular gas surface densities across main-sequence galaxy samples without introducing more than about 10 percent systematic bias.
  • Galaxy-to-galaxy comparisons of molecular gas excitation should focus on within-galaxy environmental drivers (centers, bars, arms) rather than global galaxy properties, since internal scatter is about 0.1 dex and exceeds the 0.05 dex inter-galaxy scatter.
  • The log-log slope of about 0.10–0.12 between $R_{21}$ and SFR surface density offers an empirical correction for resolved studies that want to improve on the fixed ratio.
  • Stellar mass provides a weaker but useful galaxy-wide predictor, with $\log_{10}\langle R_{21}\rangle_{\mathrm{gal}} \approx -0.07\, \log_{10}(M_\star/M_\odot) + 0.57$ over $10^9$–$10^{11}\,M_\odot$.
  • Switching from CO(1-0) to CO(2-1) with fixed $R_{21}$ shifts the molecular Kennicutt-Schmidt index by about 2% sample-wide, up to roughly 10–20% for individual galaxies, which is smaller than conversion-factor uncertainties.

Reading between the lines

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

  • If the 0.05 dex inter-galaxy scatter is real, then CO excitation is a nearly universal property of star-forming disks at kpc scales, and reported environmental trends from mixed-telescope studies should be re-examined with same-facility data before being interpreted physically.
  • The survey-permutation offsets of 10–20% imply that future multi-line mapping campaigns should either secure both lines with the same array or tie single-dish data to absolute calibration standards, otherwise spurious trends of order the full astrophysical range can be manufactured.
  • A testable extension: because $R_{21}$ rises with SFR surface density with slope ~0.1, high-resolution (GMC-scale) maps should show steep local enhancements of $R_{21}$ around star-forming regions, a prediction that can be checked with existing high-resolution ALMA CO data.
  • The metallicity trend rests on only 8 galaxies spanning ~0.25 dex; extending the same matched-calibration analysis to low-metallicity dwarfs would determine whether the anti-correlation steepens, which would matter for high-redshift CO mass estimates.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper presents new ALMA ACA 7m+TP CO(1-0) maps of 12 nearby galaxies, combines them with two archival ALMA CO(1-0) maps and PHANGS-ALMA CO(2-1) data for 14 galaxies, and measures the resolved CO(2-1)/CO(1-0) ratio R21 at a common 1.7 kpc resolution. It reports a sample-wide median R21 of about 0.64, a galaxy-to-galaxy scatter of 0.05 dex that is lower than the >0.1 dex scatter of earlier single-dish studies, an internal within-galaxy scatter of about 0.1 dex with enhanced central values near 0.75, and correlations of R21 with galactocentric radius, SFR surface density, specific SFR, and metallicity. The paper concludes that assuming a fixed R21 = 0.64 does not significantly bias kpc-scale molecular gas mass estimates for main-sequence galaxy samples, and that calibration uncertainties and the CO-to-H2 conversion factor dominate systematic scatter.

Significance. If the low galaxy-to-galaxy scatter and the reported scaling relations hold, this is a valuable calibration-quality benchmark for CO-derived molecular gas measurements and provides practical advice for studies using CO(2-1). The paper benefits from new ALMA data, a careful homogenization to a common resolution and grid, multiple averaging approaches, completeness checks, and a systematic cross-survey permutation analysis. The main quantitative claim, however, depends on the relative flux calibration accuracy between the new CO(1-0) and PHANGS CO(2-1) data; that dependence is the load-bearing point of the paper and needs explicit, quantitative support.

major comments (2)
  1. [§2.2.3, §4.1.1, Fig. 4] The headline claim that the galaxy-to-galaxy scatter is 0.05 dex is not separable from plausible per-line flux calibration errors at the level the paper itself discusses. If each line carries an independent 10% flux error, the ratio inherits sqrt(0.1^2 + 0.1^2) ≈ 0.141 dex fractional error, i.e., about 0.057 dex in log10, which equals the observed 0.05 dex scatter; at the adopted 5% per line, the injected scatter is about 0.030 dex and the residual intrinsic scatter is only about 0.04 dex after quadrature. The statement in §2.2.3 that the conclusions still hold at ~10% uncertainty is therefore not supported by the error budget as written, unless the 10% errors are strongly correlated between the two lines. Please provide a quantitative calibration-error treatment: either report direct repeatability or band-to-band transfer tests for the new Cycle 9 Band 3 observations, or explicitly reframe the 0.05 dex galaxy-to-galaxy scatter as an upper limit that includes plausible uncorrelated calibration noise.
  2. [§4.1.1 and Conclusions] The size of the headline galaxy-to-galaxy scatter is quoted inconsistently: the Abstract and §4.1.1 give 0.05 dex, while point 1 of the Conclusions gives 0.06 dex. Since the comparison to the 0.11 dex single-dish scatter is central, please state one consistent number and specify exactly which quantity it describes (residual scatter around the fitted line, scatter of the galaxy medians, or scatter after subtracting a calibration term).
minor comments (5)
  1. [§3.2] The text contains a typo: 'resect' should be 'respect' in the description of the ratio-of-sums average.
  2. [§4.4] In the paragraph comparing to Leroy et al. (2022), the phrase 'in jibthe sample' appears to be an editing artifact and should be corrected.
  3. [Figure 1 caption] The caption contains the typo 'tht' for 'that' in the opening sentence describing the sample selection.
  4. [Table 3] For the log-log fits, the intercept q is reported in dex but the units are not stated; adding the units would avoid ambiguity for readers applying Eq. (5) to their own data.
  5. [§4.2] The sentence 'When we perform a liner regression' should read 'linear regression.'

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation found; the R21 measurements are direct ratios of independent ALMA maps, and the paper's scaling relations are empirical fits rather than predictions derived from fitted inputs.

full rationale

The paper's central quantity, R21, is defined in Eq. 1 as the ratio of two independently calibrated CO integrated-intensity maps (CO(2-1)/CO(1-0)). The reported galaxy-to-galaxy scatter of 0.05 dex is computed from these measured ratios and compared against single-dish surveys; it is not obtained by inverting a model whose parameters were fitted to the same scatter. The recommended fixed value R21=0.64 is the sample median of the measured ratios, presented as a practical recommendation rather than as a prediction emerging from a fitted model. The power-law slopes versus SFR surface density, specific SFR, metallicity, and radius (Table 3) are descriptive regressions of the measured ratios; they do not claim first-principles derivation, so there is no reduction by construction. The flux-calibration discussion in Section 2.2.3 contains an arguable robustness claim — 'Our conclusions still hold even when assuming a higher uncertainty of ~10%' — and at 10% per-line calibration error the ratio uncertainty would be comparable to the reported 0.05 dex scatter; however, this is an uncertainty and sensitivity concern, not a circularity, because the scatter is measured directly rather than defined via a fitted calibration parameter. The paper does cite prior PHANGS work (e.g., Leroy et al. 2021b; Neumann et al. 2023a) for flux recovery and calibration repeatability, and these involve overlapping authors, but they are used as supporting external characterizations of the data, not as a self-referential uniqueness or forcing argument. No equation or fitted parameter in the paper is defined in terms of the target result, and no prediction is statistically forced by a prior fit to the same data. The central empirical content — resolved R21 maps, their internal scatter, and their environmental correlations — stands independently of any self-citation chain.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central results are empirical fits; no new physical entities are introduced.

free parameters (3)
  • Power-law slope m for log10 R21 vs log10 Sigma_SFR = 0.12 (binned) or 0.10-0.11 (other methods)
    Fitted to the binned medians of the observed R21 values.
  • Intercept q for the same relation = 0.06
    Fitted simultaneously with the slope.
  • Power-law slope for log10 R21 vs log10 stellar mass = -0.07
    Fitted to the galaxy-wide median R21 values.
assumptions (4)
  • domain assumption ALMA flux calibration uncertainty is about 5 percent in both Band 3 and Band 6
    Justified by technical handbook and repeat observations, but is an assumed input for the error budget.
  • domain assumption The 7m+TP+12m combinations recover at least 90 percent of the total CO flux
    Based on prior simulations by Leroy et al. (2021b) and Neumann et al. (2023a).
  • domain assumption WISE band 3 luminosity traces CO emission well enough for aperture corrections
    Used to correct for missing flux outside the ALMA field of view.
  • domain assumption The sample of 14 galaxies is representative of massive main-sequence galaxies
    Selection aimed at dynamic range in SFR and stellar mass, but limited to 14 nearby galaxies.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Constraining resolved extragalactic $R_{21}$ variation with well calibrated ALMA observations." pith.science (2026). https://pith.science/paper/5T7FNTS5

@misc{pith2026250609125,
  author       = {Pith},
  title        = {Pith review of: Constraining resolved extragalactic $R_21$ variation with well calibrated ALMA observations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5T7FNTS5}},
  note         = {Machine review of arXiv:2506.09125}
}
abstract

CO(1-0) and CO(2-1) are commonly used as bulk molecular gas tracers. The CO line ratios (especially CO(2-1)/CO(1-0) - $R_{21}$) vary within and among galaxies, yet previous studies on $R_{21}$ and alike often rely on measurements constructed by combining data from facilities with substantial relative calibration uncertainties that have the same order as physical line ratio variations. Hence robustly determining systematic $R_{21}$ variations is challenging. Here, we compare CO(1-0) and CO(2-1) mapping data from ALMA for 14 nearby galaxies, at a common physical resolution of 1.7 kpc. Our dataset includes new ALMA (7m+TP) CO(1-0) maps of 12 galaxies. We investigate $R_{21}$ variation to understand its dependence on global galaxy properties, kpc-scale environmental factors, and its correlation with star formation rate (SFR) surface density and metallicity. We find that the galaxy-to-galaxy scatter is 0.05 dex. This is lower than previous studies which reported over 0.1 dex variation, likely reflecting significant flux calibration uncertainties in single-dish surveys. Within individual galaxies, $R_{21}$ has a typical mean value of ~0.64 and 0.1 dex variation, with an increase to ~0.75 towards galactic centers. We find strong correlations between $R_{21}$ and various galactic parameters, particularly SFR surface density, which shows a power-law slope of 0.10-0.11 depending on the adopted binning/fitting methods. Our findings suggest that, for studies covering main sequence galaxy samples, assuming a fixed $R_{21}$=0.64 does not significantly bias kpc-scale molecular gas mass estimates from CO(2-1). Instead, systematic uncertainties from flux calibration and the CO-to-H$_2$ conversion factor account for more systematic scatter of CO-derived molecular gas properties.

Figures

Figures reproduced from arXiv: 2506.09125 by the authors.

Figure 1
Figure 1. Sample Overview (Left) The panel presents the distribution of studied galaxies in the SFR−M∗ parameter space. The orange squares and hexagons indicate the 14 galaxies tht form our sample. For reference, the red stars present the nine sources analyzed as part of the EMPIRE CO line ratio paper (den Brok et al. 2021). The green circles represent the galaxies for which Leroy et al. (2022) present R21 measurements. For c… view at source ↗
Figure 2
Figure 2. ALMA CO (1-0) moment-0 maps The panel with the galaxy name label in the top right corner present the CO(1-0) observations at the native ALMA 7m resolution (∼15′′). The black circle shows the beam size and the black line indicates a physical scale of 3 kpc, when accounting for the galaxy’s distance. For NGC 3627 and NGC 5236, higher angular resolution data is available, but we convolved the maps to 15′′ for consisten… view at source ↗
Figure 3
Figure 3. Sample-wide R21 distribution (Left) The kernel density estimation (KDE) of all significantly detected sightlines across the full galaxy sample. Here we also separate by sightlines associated with center (orange shaded region); disk (cyan shaded region). For reference, we indicate the equally weighted (i.e., area weighted) mean and 16th-to-84th percentile range of the three different KDE’s in the top. (Right) These v… view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Trends of galaxy-wide R21 and galaxy-integrated properties These panels illustrate the equally-weighted median R21 per galaxy with respect to the galaxy-wide stellar mass, SFR and specific SFR. For reference, we have included the sample of galaxies studied by Leroy et …
Figure 5
Figure 5. Figure 5: Distribution of galaxy-integrated ⟨R21⟩ in the SFR-M∗ parameter space The 14 galaxies in our sample are colored based on the galaxy-wide average R21 (see [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: R21 correlation trends combining all sightlines The panels present the trends for the significantly detected sightlines. The points are color-coded using a density distribution function to illustrate the where the majority of points lie within the respective parameter …
Figure 7
Figure 7. Figure 7: Resolved R21 variation All maps have been convolved to a common physical resolution of 1.7 kpc. Only sightlines for which both the CO(1-0) and CO(2-1) emission are significantly detected at S/N>5 are shown. The black circle in the lower right corner of each panel prese…
Figure 8
Figure 8. Figure 8: Radial R21 variation The colored points represent significantly detected (S/N>5) individual sightlines with adjacent lines of sight spaced by one half the beam width. Data with lower signal to noise are plotted using triangles either as upper or lower limits, if only o…
Figure 9
Figure 9. Figure 9: R21 variation with SFR surface density This figure follows in layout and design [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Estimating instrumental-dependent scatter in [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: Comparing R21 ratio surveys at different scales. (Galaxy integrated) Each marker represents the median line ratio per galaxy. We also illustrate the sample wide median and 16th-to-84th percentile range using a larger, grey-colored marker. The y axis carries no informa…
Figure 13
Figure 13. Figure 13: The Kennicutt-Schmidt relation. The blue points show the correlation of the molecular gas surface density based on the CO(1-0) line intensity with SFR surface density. The dark blue points indicate central (r<2 kpc) sightlines. We fit a linear regression in log-log sp…
Figure 14
Figure 14. Figure 14: Galaxy-to-galaxy variation in depletion time [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]
Figure 15
Figure 15. Figure 15: Overlap in uv-distance for CO(1-0) and CO(2-1) 7m+TP observations. The normalized histograms illustrate the distribution of the channel and time averaged amplitudes of the visibilities for the ALMA 7m observations of CO(1-0) in brown and CO(2-1) in green. In addition,…
Figure 16
Figure 16. Figure 16: Contrasting different R21 fitting prescriptions. In the paper, we employ a simple power law (black line) to describe the scaling relation between the line ratio and the SFR surface density. Alternate functional forms of the scaling relations have been suggested, captu…

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Arizona Molecular ISM Survey with the SMT: The Diverse Carbon Monoxide Line Ratios and Spectral Line Energy Distributions of Star Forming Galaxies

    astro-ph.GA 2025-07 accept novelty 6.0 of 10

    CO line ratios in 47 nearby star-forming galaxies rise with star formation rate, leading to empirical prescriptions for converting higher-J CO luminosities to molecular gas masses.

Reference graph

Works this paper leans on

90 extracted references · 7 canonical work pages · cited by 1 Pith paper

  1. [1]

    S., Lee, J

    Anand, G. S., Lee, J. C., Van Dyk, S. D., et al. 2021, MNRAS, 501, 3621, doi: 10.1093/mnras/staa3668 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 Collaborat...

    doi:10.1093/mnras/staa3668 2021

  2. [2]

    2020, A&A, 642, A37, doi: 10.1051/0004-6361/202038396

    Bassini, L., Rasia, E., Borgani, S., et al. 2020, A&A, 642, A37, doi: 10.1051/0004-6361/202038396

    doi:10.1051/0004-6361/202038396 2020

  3. [3]

    K., Sun, J., et al

    Belfiore, F., Leroy, A. K., Sun, J., et al. 2023, A&A, 670, A67, doi: 10.1051/0004-6361/202244863

    doi:10.1051/0004-6361/202244863 2023

  4. [4]

    2008, AJ, 136, 2846, doi: 10.1088/0004-6256/136/6/2846

    Bigiel, F., Leroy, A., Walter, F., et al. 2008, AJ, 136, 2846, doi: 10.1088/0004-6256/136/6/2846

    doi:10.1088/0004-6256/136/6/2846 2008

  5. [5]

    D., Wolfire, M., & Leroy, A

    Bolatto, A. D., Wolfire, M., & Leroy, A. K. 2013, ARA&A, 51, 207, doi: 10.1146/annurev-astro-082812-140944

    doi:10.1146/annurev-astro-082812-140944 2013

  6. [6]

    1992, A&A, 264, 433

    Braine, J., & Combes, F. 1992, A&A, 264, 433

    1992

  7. [7]

    1993, A&AS, 97, 887

    Braine, J., Combes, F., Casoli, F., et al. 1993, A&AS, 97, 887

    1993

  8. [8]

    Braine, J., Ferguson, A. M. N., Bertoldi, F., & Wilson, C. D. 2007, ApJL, 669, L73, doi: 10.1086/524135

    doi:10.1086/524135 2007

Show all 90 references
  1. [9]

    D., Zabel, N., et al

    Brown, T., Wilson, C. D., Zabel, N., et al. 2021, ApJS, 257, 21, doi: 10.3847/1538-4365/ac28f5

    2021 doi

  2. [10]

    2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x

    Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x

    2003

  3. [11]

    M., & Scoville, N

    Bryant, P. M., & Scoville, N. Z. 1996, ApJ, 457, 678, doi: 10.1086/176763

    1996 doi

  4. [12]

    C., Engelbracht, C

    Calzetti, D., Kennicutt, R. C., Engelbracht, C. W., et al. 2007, ApJ, 666, 870, doi: 10.1086/520082 CASA Team, Bean, B., Bhatnagar, S., et al. 2022, PASP, 134, 114501, doi: 10.1088/1538-3873/ac9642

    2007 doi

  5. [13]

    2020, A&A, 633, A100, doi: 10.1051/0004-6361/201936665 29

    Casasola, V., Bianchi, S., De Vis, P., et al. 2020, A&A, 633, A100, doi: 10.1051/0004-6361/201936665 29

    2020 doi

  6. [14]

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

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

    2003 doi

  7. [15]

    2017, A&A, 604, A53, doi: 10.1051/0004-6361/201730605

    Cicone, C., Bothwell, M., Wagg, J., et al. 2017, A&A, 604, A53, doi: 10.1051/0004-6361/201730605

    2017 doi

  8. [16]

    P., & Turner, J

    Crosthwaite, L. P., & Turner, J. L. 2007, AJ, 134, 1827, doi: 10.1086/521645 den Brok, J., Neumann, L., Eibensteiner, C., et al. 2024, PyStructure software, v3.0.1, Zenodo, doi: 10.5281/zenodo.13787728 den Brok, J. S., Chatzigiannakis, D., Bigiel, F., et al. 2021, MNRAS, 504, ...

    2007 doi

  9. [17]

    L., Snell, R

    Dickman, R. L., Snell, R. L., & Schloerb, F. P. 1986, ApJ, 309, 326, doi: 10.1086/164604

    1986 doi

  10. [18]

    1990, ApJ, 348, 434, doi: 10.1086/168252

    Eckart, A., Downes, D., Genzel, R., et al. 1990, ApJ, 348, 434, doi: 10.1086/168252

    1990 doi

  11. [19]

    T., Bigiel, F., et al

    Eibensteiner, C., Barnes, A. T., Bigiel, F., et al. 2022, A&A, 659, A173, doi: 10.1051/0004-6361/202142624

    2022 doi

  12. [20]

    2022, A&A, 659, A191, doi: 10.1051/0004-6361/202141727

    Emsellem, E., Schinnerer, E., Santoro, F., et al. 2022, A&A, 659, A191, doi: 10.1051/0004-6361/202141727

    2022 doi

  13. [21]

    G., Hora, J

    Fazio, G. G., Hora, J. L., Allen, L. E., et al. 2004, ApJS, 154, 10, doi: 10.1086/422843

    2004 doi

  14. [22]

    2020, AJ, 160, 270, doi: 10.3847/1538-3881/abbe1a

    Harsono, D. 2020, AJ, 160, 270, doi: 10.3847/1538-3881/abbe1a

    2020 doi

  15. [23]

    J., Leroy, A

    Gallagher, M. J., Leroy, A. K., Bigiel, F., et al. 2018, ApJ, 858, 90, doi: 10.3847/1538-4357/aabad8

    2018 doi

  16. [24]

    C., Kim, C.-G., & Kim, J.-G

    Gong, M., Ostriker, E. C., Kim, C.-G., & Kim, J.-G. 2020, ApJ, 903, 142, doi: 10.3847/1538-4357/abbdab

    2020 doi

  17. [25]

    D., & Alexander, D

    Goulding, A. D., & Alexander, D. M. 2009, MNRAS, 398, 1165, doi: 10.1111/j.1365-2966.2009.15194.x

    2009

  18. [26]

    2023, MNRAS, 520, 4902, doi: 10.1093/mnras/stad114

    Groves, B., Kreckel, K., Santoro, F., et al. 2023, MNRAS, 520, 4902, doi: 10.1093/mnras/stad114

    2023 doi

  19. [27]

    R., Millman, K

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

    2020 doi

  20. [28]

    1997, in Astronomical Society of the Pacific Conference Series, Vol

    Hasegawa, T., Morino, J., Sorai, K., et al. 1997, in Astronomical Society of the Pacific Conference Series, Vol. 124, Diffuse Infrared Radiation and the IRTS, ed. H. Okuda, T. Matsumoto, & T. Rollig, 244

    1997

  21. [29]

    Heyer, M., & Dame, T. M. 2015, ARA&A, 53, 583, doi: 10.1146/annurev-astro-082214-122324

    2015 doi

  22. [30]

    Hu, C.-Y., Sternberg, A., & van Dishoeck, E. F. 2023, ApJ, 952, 140, doi: 10.3847/1538-4357/acdcfa

    2023 doi

  23. [31]

    R., Indebetouw, R., Brogan, C

    Hunter, T. R., Indebetouw, R., Brogan, C. L., et al. 2023, PASP, 135, 074501, doi: 10.1088/1538-3873/ace216

    2023 doi

  24. [32]

    C., Richings, A

    Keating, L. C., Richings, A. J., Murray, N., et al. 2020, MNRAS, 499, 837, doi: 10.1093/mnras/staa2839

    2020 doi

  25. [33]

    P., Marrone, D

    Keenan, R. P., Marrone, D. P., & Keating, G. K. 2025, ApJ, 979, 228, doi: 10.3847/1538-4357/ada361

    2025 doi

  26. [34]

    P., Marrone, D

    Keenan, R. P., Marrone, D. P., Keating, G. K., et al. 2024, ApJ, 975, 150, doi: 10.3847/1538-4357/ad7504

    2024 doi

  27. [35]

    C., & Evans, N

    Kennicutt, R. C., & Evans, N. J. 2012, ARA&A, 50, 531, doi: 10.1146/annurev-astro-081811-125610

    2012 doi

  28. [36]

    Kennicutt, Jr., R. C. 1998, ARA&A, 36, 189, doi: 10.1146/annurev.astro.36.1.189

    1998 doi

  29. [37]

    A., Leroy, A

    Kepley, A. A., Leroy, A. K., Johnson, K. E., Sandstrom, K., & Chen, C. H. R. 2016, ApJ, 828, 50, doi: 10.3847/0004-637X/828/1/50

    2016 doi

  30. [38]

    2012, ApJ, 761, 41, doi: 10.1088/0004-637X/761/1/41

    Koda, J., Scoville, N., Hasegawa, T., et al. 2012, ApJ, 761, 41, doi: 10.1088/0004-637X/761/1/41

    2012 doi

  31. [39]

    2020, ApJL, 890, L10, doi: 10.3847/2041-8213/ab70b7

    Koda, J., Sawada, T., Sakamoto, K., et al. 2020, ApJL, 890, L10, doi: 10.3847/2041-8213/ab70b7

    2020 doi

  32. [40]

    2023, ApJ, 949, 108, doi: 10.3847/1538-4357/acc65e

    Koda, J., Hirota, A., Egusa, F., et al. 2023, ApJ, 949, 108, doi: 10.3847/1538-4357/acc65e

    2023 doi

  33. [41]

    2020, ApJ, 889, 103, doi: 10.3847/1538-4357/ab6221

    Lamperti, I., Saintonge, A., Koss, M., et al. 2020, ApJ, 889, 103, doi: 10.3847/1538-4357/ab6221

    2020 doi

  34. [42]

    E., Rosolowsky, E., et al

    Lang, P., Meidt, S. E., Rosolowsky, E., et al. 2020, ApJ, 897, 122, doi: 10.3847/1538-4357/ab9953

    2020 doi

  35. [43]

    J., Zhang, Q., Ricci, L., et al

    Law, C. J., Zhang, Q., Ricci, L., et al. 2018, ApJ, 865, 17, doi: 10.3847/1538-4357/aadca9

    2018 doi

  36. [44]

    D., Cannon, J

    Lee, H., Skillman, E. D., Cannon, J. M., et al. 2006, ApJ, 647, 970, doi: 10.1086/505573

    2006 doi

  37. [45]

    1994, A&A, 292, 371

    Lequeux, J., Le Bourlot, J., Pineau des Forets, G., et al. 1994, A&A, 292, 371

    1994

  38. [46]

    K., Walter, F., Brinks, E., et al

    Leroy, A. K., Walter, F., Brinks, E., et al. 2008, AJ, 136, 2782, doi: 10.1088/0004-6256/136/6/2782

    2008 doi

  39. [47]

    K., Walter, F., Bigiel, F., et al

    Leroy, A. K., Walter, F., Bigiel, F., et al. 2009, AJ, 137, 4670, doi: 10.1088/0004-6256/137/6/4670

    2009 doi

  40. [48]

    K., Bolatto, A., Gordon, K., et al

    Leroy, A. K., Bolatto, A., Gordon, K., et al. 2011, ApJ, 737, 12, doi: 10.1088/0004-637X/737/1/12

    2011 doi

  41. [49]

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

    Leroy, A. K., Walter, F., Sandstrom, K., et al. 2013, AJ, 146, 19, doi: 10.1088/0004-6256/146/2/19

    2013 doi

  42. [50]

    K., Usero, A., Schruba, A., et al

    Leroy, A. K., Usero, A., Schruba, A., et al. 2017, ApJ, 835, 217, doi: 10.3847/1538-4357/835/2/217

    2017 doi

  43. [51]

    K., Sandstrom, K

    Leroy, A. K., Sandstrom, K. M., Lang, D., et al. 2019, ApJS, 244, 24, doi: 10.3847/1538-4365/ab3925

    2019 doi

  44. [52]

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

    Leroy, A. K., Schinnerer, E., Hughes, A., et al. 2021a, ApJS, 257, 43, doi: 10.3847/1538-4365/ac17f3

    doi

  45. [53]

    K., Hughes, A., Liu, D., et al

    Leroy, A. K., Hughes, A., Liu, D., et al. 2021b, ApJS, 255, 19, doi: 10.3847/1538-4365/abec80

    doi

  46. [54]

    K., Rosolowsky, E., Usero, A., et al

    Leroy, A. K., Rosolowsky, E., Usero, A., et al. 2022, ApJ, 927, 149, doi: 10.3847/1538-4357/ac3490

    2022 doi

  47. [55]

    2023, A&A, 672, A36, doi: 10.1051/0004-6361/202244564

    Liu, D., Schinnerer, E., Saito, T., et al. 2023, A&A, 672, A36, doi: 10.1051/0004-6361/202244564

    2023 doi

  48. [56]

    Narayanan, D., & Krumholz, M. R. 2014, MNRAS, 442, 1411, doi: 10.1093/mnras/stu834 30

    2014 doi

  49. [57]

    S., Bigiel, F., et al

    Neumann, L., den Brok, J. S., Bigiel, F., et al. 2023a, A&A, 675, A104, doi: 10.1051/0004-6361/202346129

    doi

  50. [58]

    J., Bigiel, F., et al

    Neumann, L., Gallagher, M. J., Bigiel, F., et al. 2023b, MNRAS, 521, 3348, doi: 10.1093/mnras/stad424

    doi

  51. [59]

    P., & Seaquist, E

    Papadopoulos, P. P., & Seaquist, E. R. 1998, ApJ, 492, 521, doi: 10.1086/305052 Pe˜ naloza, C. H., Clark, P. C., Glover, S. C. O., Shetty, R., & Klessen, R. S. 2017, MNRAS, 465, 2277, doi: 10.1093/mnras/stw2892

    1998 doi

  52. [60]

    S., & Grebel, E

    Pilyugin, L. S., & Grebel, E. K. 2016, MNRAS, 457, 3678, doi: 10.1093/mnras/stw238

    2016 doi

  53. [61]

    A., et al

    Remijan, A., Biggs, A., Cortes, P. A., et al. 2019, ALMA Technical Handbook,ALMA Doc. 7.3, ver. 1.1, 2019, ALMA Technical Handbook,ALMA Doc. 7.3, ver. 1.1ISBN 978-3-923524-66-2, doi: 10.5281/zenodo.4511522

    2019 doi

  54. [62]

    2022, ARA&A, 60, 319, doi: 10.1146/annurev-astro-021022-043545

    Saintonge, A., & Catinella, B. 2022, ARA&A, 60, 319, doi: 10.1146/annurev-astro-021022-043545

    2022 doi

  55. [63]

    J., et al

    Saintonge, A., Catinella, B., Tacconi, L. J., et al. 2017, ApJS, 233, 22, doi: 10.3847/1538-4365/aa97e0

    2017 doi

  56. [64]

    1994, ApJ, 425, 641, doi: 10.1086/174011

    Oka, T. 1994, ApJ, 425, 641, doi: 10.1086/174011

    1994 doi

  57. [65]

    Salim, S., Boquien, M., & Lee, J. C. 2018, ApJ, 859, 11, doi: 10.3847/1538-4357/aabf3c

    2018 doi

  58. [66]

    C., Janowiecki, S., et al

    Salim, S., Lee, J. C., Janowiecki, S., et al. 2016, ApJS, 227, 2, doi: 10.3847/0067-0049/227/1/2

    2016 doi

  59. [67]

    M., Leroy, A

    Sandstrom, K. M., Leroy, A. K., Walter, F., et al. 2013, ApJ, 777, 5, doi: 10.1088/0004-637X/777/1/5

    2013 doi

  60. [68]

    2001, ApJS, 136, 189, doi: 10.1086/321793

    Sawada, T., Hasegawa, T., Handa, T., et al. 2001, ApJS, 136, 189, doi: 10.1086/321793

    2001 doi

  61. [69]

    Schinnerer, E., & Leroy, A. K. 2024, ARA&A, 62, 369, doi: 10.1146/annurev-astro-071221-052651

    2024 doi

  62. [70]

    1959, ApJ, 129, 243, doi: 10.1086/146614

    Schmidt, M. 1959, ApJ, 129, 243, doi: 10.1086/146614

    1959 doi

  63. [71]

    R., Calzetti, D., Armus, L., et al

    Schmitt, H. R., Calzetti, D., Armus, L., et al. 2006, ApJS, 164, 52, doi: 10.1086/501529

    2006 doi

  64. [72]

    K., Walter, F., et al

    Schruba, A., Leroy, A. K., Walter, F., et al. 2011, AJ, 142, 37, doi: 10.1088/0004-6256/142/2/37

    2011 doi

  65. [73]

    Bisbas, T. G. 2020, MNRAS, 492, 1465, doi: 10.1093/mnras/stz3563

    2020 doi

  66. [74]

    L., et al

    Sheth, K., Regan, M., Hinz, J. L., et al. 2010, PASP, 122, 1397, doi: 10.1086/657638

    2010 doi

  67. [75]

    Shirley, Y. L. 2015, PASP, 127, 299, doi: 10.1086/680342

    2015 doi

  68. [76]

    M., Rivolo, A

    Solomon, P. M., Rivolo, A. R., Barrett, J., & Yahil, A. 1987, ApJ, 319, 730, doi: 10.1086/165493

    1987 doi

  69. [77]

    M., & Vanden Bout, P

    Solomon, P. M., & Vanden Bout, P. A. 2005, ARA&A, 43, 677, doi: 10.1146/annurev.astro.43.051804.102221

    2005 arXiv

  70. [78]

    K., Rosolowsky, E., et al

    Sun, J., Leroy, A. K., Rosolowsky, E., et al. 2022, AJ, 164, 43, doi: 10.3847/1538-3881/ac74bd

    2022 doi

  71. [79]

    K., Ostriker, E

    Sun, J., Leroy, A. K., Ostriker, E. C., et al. 2023, ApJL, 945, L19, doi: 10.3847/2041-8213/acbd9c

    2023 doi

  72. [80]

    M., Sun, J., et al

    Teng, Y.-H., Sandstrom, K. M., Sun, J., et al. 2023, ApJ, 950, 119, doi: 10.3847/1538-4357/accb86

    2023 doi

  73. [81]

    A., Heckman, T

    Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898, doi: 10.1086/423264

    2004 doi

  74. [82]

    K., Walter, F., et al

    Usero, A., Leroy, A. K., Walter, F., et al. 2015, AJ, 150, 115, doi: 10.1088/0004-6256/150/4/115

    2015 doi

  75. [83]

    D., Vogel, S

    Villanueva, V., Bolatto, A. D., Vogel, S. N., et al. 2024, ApJ, 962, 88, doi: 10.3847/1538-4357/ad1387

    2024 doi

  76. [84]

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

    Williams, T. G., Kreckel, K., Belfiore, F., et al. 2022, MNRAS, 509, 1303, doi: 10.1093/mnras/stab3082

    2022 doi

  77. [85]

    2002, ApJ, 569, 157, doi: 10.1086/339287

    Wong, T., & Blitz, L. 2002, ApJ, 569, 157, doi: 10.1086/339287

    2002 doi

  78. [86]

    2021, PASJ, 73, 257, doi: 10.1093/pasj/psaa119

    Yajima, Y., Sorai, K., Miyamoto, Y., et al. 2021, PASJ, 73, 257, doi: 10.1093/pasj/psaa119

    2021 doi

  79. [87]

    2010, PASJ, 62, 1277, doi: 10.1093/pasj/62.5.1277

    Yoda, T., Handa, T., Kohno, K., et al. 2010, PASJ, 62, 1277, doi: 10.1093/pasj/62.5.1277

    2010 doi

  80. [88]

    1996, AJ, 112, 1903, doi: 10.1086/118152

    Rownd, B. 1996, AJ, 112, 1903, doi: 10.1086/118152

    1996 doi

  81. [89]

    S., & Scoville, N

    Young, J. S., & Scoville, N. Z. 1991, ARA&A, 29, 581, doi: 10.1146/annurev.aa.29.090191.003053

    1991

  82. [90]

    S., Xie, S., Tacconi, L., et al

    Young, J. S., Xie, S., Tacconi, L., et al. 1995, ApJS, 98, 219, doi: 10.1086/192159

    1995 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.