Pith. sign in

REVIEW 2 major objections 1 minor 116 references

The EDGE-CALIFA Survey: Star Formation Efficiency and Galaxy Quenching across 62 Main Sequence, Green Valley, and Red Galaxies

T0 review · 2 major / 1 minor · reviewed 2026-06-26 · grok-4.3

Pith's one-line read Quenched galaxies below the main sequence retain molecular gas but convert it to stars far less efficiently.

desk verdict New GBT CO(1-0) maps for 62 CALIFA galaxies show depletion times rising sharply from main-sequence to red-sequence objects, with the trend holding across tested X_CO prescriptions, but the applicability of those prescriptions to quenched systems is the main open question. read the letter →

arxiv 2606.23649 v1 pith:PXY72VKK submitted 2026-06-22 astro-ph.GA

classification astro-ph.GA
keywords galaxyquenchingstarformationefficiencymoleculargasCOsurveymainsequencegreenvalleyreddepletiontime
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 survey maps CO(1-0) emission across 62 nearby galaxies that span the star-forming main sequence, green valley, and red sequence. Median molecular-gas depletion times rise from roughly 2 Gyr on the main sequence to 7 Gyr in the green valley and 128 Gyr in red galaxies. The trend of lower star-formation efficiency with increasing distance below the main sequence remains after testing several CO-to-H2 conversion-factor prescriptions. This pattern indicates that the reduced star-formation rates in quenched systems arise mainly from inefficient gas-to-star conversion rather than from a wholesale absence of molecular gas. Galaxies below the sequence therefore keep substantial molecular reservoirs yet form stars slowly, consistent with the joint action of low gas density and morphological effects.

What carries the argument

Star-formation efficiency (SFR per unit molecular gas mass) measured from CO(1-0) luminosity and optical SFR tracers, tracked as a function of offset from the star-forming main sequence.

What would settle it

A direct measurement, using an independent gas-mass tracer such as dust continuum or [C I], showing that red-sequence galaxies actually contain far less molecular gas than the CO-derived values or that the efficiency trend disappears when gas mass is estimated without CO.

Watch

Extended reading notes

Core claim

By combining new GBT CO(1-0) maps with CALIFA integral-field spectroscopy, the survey derives molecular gas masses, star-formation rates, and metallicities for 62 galaxies. Median depletion times are 2.10 Gyr on the main sequence, 6.90 Gyr in the green valley, and 127.7 Gyr on the red sequence when a Galactic conversion factor is used. Systematic decline in star-formation efficiency with offset below the main sequence persists across multiple conversion-factor choices, demonstrating that many quenched galaxies retain molecular gas masses comparable to star-forming systems yet form stars at much lower rates.

Load-bearing premise

That CO(1-0) emission traces the total molecular gas mass across the full range of metallicities and conditions in green-valley and red galaxies once the tested conversion-factor prescriptions are applied.

Editorial extensions

If this is right

  • Quenched galaxies can hold molecular gas reservoirs similar in mass to those on the main sequence.
  • Depletion times lengthen by factors of several to more than 50 below the main sequence.
  • Suppressed efficiency, rather than gas removal, accounts for the bulk of the drop in star-formation rate.
  • Low gas density together with morphological stabilization can jointly reduce efficiency.

Reading between the lines

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

  • Quenching processes may act primarily by lowering the dense-gas fraction or raising the density threshold for collapse rather than by expelling the entire molecular reservoir.
  • High-resolution maps of dense-gas tracers in green-valley systems could test whether the efficiency drop is localized to particular galactic structures.
  • Evolutionary models that treat quenching as simple gas exhaustion would need revision if efficiency suppression is the dominant mechanism.
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, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper presents the GBT-EDGE CO(1-0) survey with the Green Bank Telescope, mapping molecular gas in 62 nearby galaxies (10-140 Mpc) selected from the CALIFA IFS survey. The sample spans the star-forming main sequence, green valley, and red sequence. Combining CO data with CALIFA optical measurements, the authors derive molecular gas masses, SFRs, metallicities, and stellar densities to compute depletion times and star formation efficiencies. They report median depletion times of 2.10, 6.90, and 127.7 Gyr for MS, GV, and red galaxies under a Galactic X_CO, and find that the systematic decline in SFE below the SFMS persists across multiple conversion-factor prescriptions. The central claim is that low SFR in quenched galaxies is driven primarily by suppressed SFE rather than absence of molecular gas, with red galaxies retaining substantial molecular reservoirs.

Significance. If the CO(1-0) luminosities reliably trace total M_H2, the result would provide direct evidence that quenching involves reduced efficiency rather than complete gas removal, with implications for morphological quenching and density-dependent star formation. The multi-prescription test and use of a representative IFS-selected sample are strengths that allow comparison across environments. The work adds to the literature on gas content in the green valley and red sequence by reporting spatially matched measurements.

major comments (2)
  1. [Abstract] Abstract and implied methods: the central claim that 'the low SFR in some quenched galaxies is primarily driven by suppressed SFE rather than an absence of molecular gas' and that 'galaxies below the main sequence can retain substantial molecular gas reservoirs' depends on the CO(1-0) emission yielding reliable total M_H2. The tested prescriptions (Galactic X_CO and variants) are largely calibrated on star-forming systems; no explicit test or justification is provided for systematic shifts in excitation, optical depth, or CO-dark H2 fractions expected at the low densities and metallicities of green-valley/red galaxies. A bias that underestimates M_H2 in red objects would artificially shorten their reported depletion times (127.7 Gyr median) and weaken the conclusion.
  2. [Abstract] Sample description and data combination: the abstract states the sample is 'selected from the CALIFA survey' and that CO and IFS data are combined, but does not specify the exact selection criteria, the spatial matching procedure between GBT single-dish CO maps and CALIFA IFS apertures, or the treatment of non-detections and upper limits when computing depletion times for the red-sequence subsample. These details are load-bearing for the reported median values and the cross-population comparison.
minor comments (1)
  1. [Abstract] The asymmetric uncertainties on the depletion times are reported but the method for deriving them (e.g., bootstrap, Monte Carlo on fluxes and SFRs) is not stated in the abstract; adding a brief methods sentence would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive report. We address each major comment below and outline revisions that will strengthen the manuscript while preserving its core conclusions.

read point-by-point responses
  1. Referee: [Abstract] Abstract and implied methods: the central claim that 'the low SFR in some quenched galaxies is primarily driven by suppressed SFE rather than an absence of molecular gas' and that 'galaxies below the main sequence can retain substantial molecular gas reservoirs' depends on the CO(1-0) emission yielding reliable total M_H2. The tested prescriptions (Galactic X_CO and variants) are largely calibrated on star-forming systems; no explicit test or justification is provided for systematic shifts in excitation, optical depth, or CO-dark H2 fractions expected at the low densities and metallicities of green-valley/red galaxies. A bias that underestimates M_H2 in red objects would artificially shorten their reported depletion times (127.7 Gyr median) and weaken the conclusion.

    Authors: We appreciate the referee's emphasis on this potential systematic uncertainty. Our multi-prescription analysis demonstrates that the SFE decline persists even when adopting metallicity- and density-dependent X_CO variants. Nevertheless, we agree that the prescriptions are primarily calibrated on star-forming systems and that an explicit discussion of applicability to quenched galaxies is warranted. In the revised manuscript we will add a dedicated subsection (likely in Section 4 or 5) that (i) reviews literature on CO excitation and CO-dark H2 in low-density, low-metallicity regimes, (ii) quantifies the plausible range of bias in M_H2 for our red-sequence subsample, and (iii) shows how even a factor-of-two underestimate in M_H2 would still leave the median depletion time for red galaxies an order of magnitude longer than for the main sequence. This addition will not alter the reported medians but will better bound the robustness of the central claim. revision: partial

  2. Referee: [Abstract] Sample description and data combination: the abstract states the sample is 'selected from the CALIFA survey' and that CO and IFS data are combined, but does not specify the exact selection criteria, the spatial matching procedure between GBT single-dish CO maps and CALIFA IFS apertures, or the treatment of non-detections and upper limits when computing depletion times for the red-sequence subsample. These details are load-bearing for the reported median values and the cross-population comparison.

    Authors: We agree that the abstract's brevity omits key methodological information. The full selection function (stellar-mass and redshift cuts, morphological and environmental criteria drawn from the CALIFA parent sample), the GBT-to-CALIFA aperture matching procedure (including beam convolution and centering), and the statistical treatment of non-detections (Kaplan–Meier estimator for censored data) are described in Sections 2 and 3. To address the referee's concern we will expand the abstract by one or two sentences that (i) state the primary selection criteria, (ii) note that GBT maps are spatially matched to the CALIFA hexagonal apertures, and (iii) indicate that upper limits are incorporated via survival analysis when computing medians for the red subsample. These changes will make the abstract self-contained while remaining within length limits. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: depletion times and SFE trends are direct empirical measurements

full rationale

The central result (longer depletion times below the SFMS) follows from tau_dep = M_H2 / SFR, where M_H2 is obtained from observed CO(1-0) luminosity scaled by independently chosen X_CO prescriptions and SFR is measured separately via CALIFA optical IFS. No equation defines SFE or depletion time in terms of the reported trend; the systematic decline in SFE is an observed correlation across the sample, not a fitted or self-referential quantity. No self-citation is invoked as a uniqueness theorem or load-bearing premise for the main claim. The applicability of X_CO prescriptions is an external assumption subject to falsification, not a circular reduction. The derivation chain is therefore self-contained against the paper's own data products.

Assumptions & free parameters 1 free parameters · 1 assumptions · 0 invented entities

Central claim depends on the CO-to-H2 conversion factor being the dominant systematic and on the assumption that the 62-galaxy sample is representative of local environments; no new entities postulated.

free parameters (1)
  • CO-to-H2 conversion factor
    Galactic value used for quoted medians; multiple alternative prescriptions tested to check robustness of SFE trend.
assumptions (1)
  • domain assumption CO(1-0) luminosity traces total molecular hydrogen mass across the metallicity and density range of the sample
    Invoked when converting observed CO flux to H2 mass for depletion-time calculation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The EDGE-CALIFA Survey: Star Formation Efficiency and Galaxy Quenching across 62 Main Sequence, Green Valley, and Red Galaxies." pith.science (2026). https://pith.science/paper/PXY72VKK

@misc{pith2026260623649,
  author       = {Pith},
  title        = {Pith review of: The EDGE-CALIFA Survey: Star Formation Efficiency and Galaxy Quenching across 62 Main Sequence, Green Valley, and Red Galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PXY72VKK}},
  note         = {Machine review of arXiv:2606.23649}
}
abstract

We present GBT-EDGE, a new CO(1-0) survey using the Green Bank Telescope to map 62 nearby (10-140 Mpc) galaxies spanning the star-forming main sequence (SFMS), green valley, and red sequence. The galaxy sample is selected from the CALIFA survey with integral field spectroscopy (IFS), which provides a representative census of local galactic environments. Combining the CO dataset with CALIFA's optical IFS measurements, we derive molecular gas masses, star formation rates (SFR), metallicities, and stellar mass densities to measure star formation efficiency (SFE) and investigate the physical drivers of galaxy quenching. We obtain a median molecular gas depletion time of $2.10^{+2.35}_{-1.31}$, $6.90^{+17.00}_{-3.67}$, and $127.7^{+201.6}_{-113.4}$ Gyr for our sample of main sequence, green valley, and red galaxies, respectively, assuming a Galactic CO-to-H2 conversion factor. By applying various conversion factor prescriptions, we also confirm a systematic decrease of SFE with galaxy's offset below the SFMS, regardless of the adopted prescription. This suggests that the low SFR in some quenched galaxies is primarily driven by suppressed SFE rather than an absence of molecular gas. Our results provide evidence that galaxies below the main sequence can retain substantial molecular gas reservoirs comparable to star-forming galaxies, but they exhibit longer depletion times and form stars inefficiently, possibly due to the combined effects of low gas density and morphological quenching mechanisms.

Figures

Figures reproduced from arXiv: 2606.23649 by the authors.

Figure 1
Figure 1. The relation between global star formation rate (SFR) and stellar mass (Mstar) for our GBT sample (black points; 62 galaxies), the CARMA sample (gray stars; A. D. Bolatto et al. 2017), and the full CALIFA sample (contours; S. F. S´anchez et al. 2016a). The blue (upper) and red (lower) dashed lines indicate best linear fits from M. Cano-D´ıaz et al. (2016) for the main sequence and red galaxies, respectively. The GBT… view at source ↗
Figure 2
Figure 2. SDSS g (blue channel), r (green channel), and i (red channel) composite images for all 62 galaxies in our GBT-EDGE sample. Green valley (GV) and red galaxies (RGs) are labeled by a letter ‘G’ and ‘R’, respectively, in the top-right corners of their panels. These galaxies cover a variety of morphologies and galaxy environments, which constitute a representative sample of the local Universe ( [PITH_FULL_IMAGE:figures… view at source ↗
Figure 3
Figure 3. An example of our background measurements (gray) and empirical OFF model construction (black), rep￾resented by the start (top panel) and end (bottom panel) of selected observing scans. These OFF models are used to remove variations in the background signals (Equation 3). This figure exemplifies the changes occurring in the spectral baseline during a leg of the on-the-fly map, caused by a com￾bination of atmospheric … view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: The peak signal to noise (S/N) maps of all 62 galaxies, integrating over a channel width of 90 km s−1 to highlight gas structures with line widths at that scale. GV and RGs are labeled by ‘G’ and ‘R’, respectively, on the top-right corner. The color scale (bottom-right…
Figure 5
Figure 5. Figure 5: Comparison of the GBT CO (1–0) integrated line fluxes with those from the ALMA ACA CO (2–1) data (V. Villanueva et al. 2024) for the nine galaxies that overlap be￾tween both samples. They show a CO (2–1)/(1–0) line ratio (R21) of 0.70 ± 0.27. Galaxies with the highest …
Figure 6
Figure 6. Figure 6: Examples of the moment 0 (left), 1 (middle), and 2 (right) maps, based on Hα + CO-dilated masking (see Section 3.1). The white contours show S/N > 5 based on the moment-0 maps. The common beam size and cyan contours are the same as in [PITH_FULL_IMAGE:figures/full_fig…
Figure 7
Figure 7. Figure 7: Comparison of αCO values estimated via different prescriptions (B13, SL24, and T24*, which represent Equa￾tions 8–10). The dashed lines indicate a one-to-one relation, and the thick dotted lines label the Galactic αCO value of 4.35 M⊙ (K km s−1 pc2 ) −1 . While the pre…
Figure 8
Figure 8. Figure 8: (a) The SFR–Mmol relation across all 62 galaxies, using Hα-based SFR estimates and Mmol derived via a Galactic αCO. The dotted lines show constant molecular gas depletion times (tdep) of 0.1, 1, and 10 Gyr. (b) The derived tdep increases systematically as galaxies go f…
Figure 9
Figure 9. Figure 9: Histograms of the derived gas depletion times under four different αCO treatments: constant MW value, a CO velocity dispersion-based prescription (T24*; Equation 10), and two metallicity + stellar mass density-based prescriptions (B13 and SL24; Equation 8 and 9). The v…
Figure 10
Figure 10. Figure 10: Comparison between SFRs estimated via Hα (Section 3.2.1) and the simple stellar population (SSP) anal￾ysis. The dashed line indicates a one-to-one relation. For the MS and GV populations, SFRs inferred from Hα are consistent with those inferred from the SSP analysis w…
Figure 11
Figure 11. Figure 11: Same as [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]
Figure 12
Figure 12. Figure 12: Same as [PITH_FULL_IMAGE:figures/full_fig_p025_12.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

116 extracted references · 115 canonical work pages

  1. [1]

    doi:10.1046/j.1365-8711.1999.02853.x , abstract=

    Abadi, M. G., Moore, B., & Bower, R. G. 1999, MNRAS, 308, 947, doi: 10.1046/j.1365-8711.1999.02715.x

  2. [2]

    2017, MNRAS, 470, 4750, doi: 10.1093/mnras/stx1556 Arrigoni Battaia, F., Gavazzi, G., Fumagalli, M., et al

    Accurso, G., Saintonge, A., Catinella, B., et al. 2017, MNRAS, 470, 4750, doi: 10.1093/mnras/stx1556

  3. [3]
  4. [4]

    A., Bureau, M., et al

    Alatalo, K., Davis, T. A., Bureau, M., et al. 2013, MNRAS, 432, 1796, doi: 10.1093/mnras/sts299

  5. [5]

    The Chemical Composition of the Sun

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481, doi: 10.1146/annurev.astro.46.060407.145222 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167, doi: 10.3847/1538-4357/ac7c74

  6. [6]

    K., Heckman , T., S \'a nchez , S

    Barrera-Ballesteros, J. K., Heckman, T., S´ anchez, S. F., et al. 2021, ApJ, 909, 131, doi: 10.3847/1538-4357/abd855

  7. [7]

    K., Cruz-Gonz´ alez, I., Colombo, D., et al

    Barrera-Ballesteros, J. K., Cruz-Gonz´ alez, I., Colombo, D., et al. 2025, ApJ, 978, 23, doi: 10.3847/1538-4357/ad85d1

  8. [8]

    2025, A&A, 697, A149, doi: 10.1051/0004-6361/202453437

    Bazzi, Z., Colombo, D., Bigiel, F., et al. 2025, A&A, 697, A149, doi: 10.1051/0004-6361/202453437

Show all 116 references
  1. [9]

    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

  2. [10]

    2016, ARA&A, 54, 529, doi: 10.1146/annurev-astro-081915-023441

    Bland-Hawthorn, J., & Gerhard, O. 2016, ARA&A, 54, 529, doi: 10.1146/annurev-astro-081915-023441

  3. [11]

    Bluck, A. F. L., Piotrowska, J. M., & Maiolino, R. 2023, ApJ, 944, 108, doi: 10.3847/1538-4357/acac7c

  4. [12]

    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

  5. [13]

    D., Wong, T., Utomo, D., et al

    Bolatto, A. D., Wong, T., Utomo, D., et al. 2017, ApJ, 846, 159, doi: 10.3847/1538-4357/aa86aa

  6. [14]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151, doi: 10.1111/j.1365-2966.2004.07881.x

  7. [15]

    2020, MNRAS, 498, L66, doi: 10.1093/mnrasl/slaa128 Cano-D´ ıaz, M.,´Avila-Reese, V., S´ anchez, S

    Carniani, S. 2020, MNRAS, 498, L66, doi: 10.1093/mnrasl/slaa128 Cano-D´ ıaz, M.,´Avila-Reese, V., S´ anchez, S. F., et al. 2019, MNRAS, 488, 3929, doi: 10.1093/mnras/stz1894 Cano-D´ ıaz, M., S´ anchez, S. F., Zibetti, S., et al. 2016, ApJL, 821, L26, doi: 10.3847/2041-8205/821/2/L26

  8. [16]

    A., Clayton, G

    Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245, doi: 10.1086/167900 Catal´ an-Torrecilla, C., Gil de Paz, A., Castillo-Morales, A., et al. 2015, A&A, 584, A87, doi: 10.1051/0004-6361/201526023

  9. [17]

    2014, Publications of the Astronomical Society of Australia, 31, 48, doi: 10.1017/pasa.2014.43

    Chennamangalam, J., Scott, S., Jones, G., et al. 2014, Publications of the Astronomical Society of Australia, 31, 48, doi: 10.1017/pasa.2014.43

  10. [18]

    M., Chastenet, J., et al

    Chiang, I.-D., Sandstrom, K. M., Chastenet, J., et al. 2024, ApJ, 964, 18, doi: 10.3847/1538-4357/ad23ed

  11. [19]

    2019, MNRAS, 484, 5192, doi: 10.1093/mnras/stz349

    Chown, R., Li, C., Athanassoula, E., et al. 2019, MNRAS, 484, 5192, doi: 10.1093/mnras/stz349

  12. [20]

    2018, MNRAS, 475, 1791, doi: 10.1093/mnras/stx3233

    Colombo, D., Kalinova, V., Utomo, D., et al. 2018, MNRAS, 475, 1791, doi: 10.1093/mnras/stx3233

  13. [21]

    F., Bolatto, A

    Colombo, D., Sanchez, S. F., Bolatto, A. D., et al. 2020, A&A, 644, A97, doi: 10.1051/0004-6361/202039005

  14. [22]

    2025a, A&A, 699, A367, doi: 10.1051/0004-6361/202453217

    Colombo, D., Kalinova, V., Bazzi, Z., et al. 2025a, A&A, 699, A367, doi: 10.1051/0004-6361/202453217

  15. [23]

    2025b, A&A, 699, A366, doi: 10.1051/0004-6361/202453179

    Colombo, D., Kalinova, V., Bazzi, Z., et al. 2025b, A&A, 699, A366, doi: 10.1051/0004-6361/202453179

  16. [24]

    2017, MNRAS, 465, 1384, doi: 10.1093/mnras/stw2766

    Curti, M., Cresci, G., Mannucci, F., et al. 2017, MNRAS, 465, 1384, doi: 10.1093/mnras/stw2766

  17. [25]

    A., Young, L

    Davis, T. A., Young, L. M., Crocker, A. F., et al. 2014, MNRAS, 444, 3427, doi: 10.1093/mnras/stu570 De Vis, P., Jones, A., Viaene, S., et al. 2019, A&A, 623, A5, doi: 10.1051/0004-6361/201834444 den Brok, J., Jim´ enez-Donaire, M. J., Leroy, A., et al. 2025, AJ, 169, 18, doi:...

  18. [26]

    L., Thorp, M

    Ellison, S. L., Thorp, M. D., Pan, H.-A., et al. 2020a, MNRAS, 492, 6027, doi: 10.1093/mnras/staa001

  19. [27]

    L., Thorp, M

    Ellison, S. L., Thorp, M. D., Lin, L., et al. 2020b, MNRAS, 493, L39, doi: 10.1093/mnrasl/slz179

  20. [28]

    L., Wong, T., S´ anchez, S

    Ellison, S. L., Wong, T., S´ anchez, S. F., et al. 2021, MNRAS, 505, L46, doi: 10.1093/mnrasl/slab047

  21. [29]

    T., Maddalena, R

    Frayer, D. T., Maddalena, R. J., White, S., et al. 2019, Calibration of Argus and the 4mm Receiver on the GBT,, Green Bank Telescope Memorandum 302, June 5, 2019, 21 pages doi: 10.48550/arXiv.1906.02307

  22. [30]

    K., Colombo, D., et al

    Garay-Solis, Y., Barrera-Ballesteros, J. K., Colombo, D., et al. 2023, ApJ, 952, 122, doi: 10.3847/1538-4357/acd781

  23. [31]

    Gensior, J., Kruijssen, J. M. D., & Keller, B. W. 2020, MNRAS, 495, 199, doi: 10.1093/mnras/staa1184

  24. [32]

    J., Lutz, D., et al

    Genzel, R., Tacconi, L. J., Lutz, D., et al. 2015, ApJ, 800, 20, doi: 10.1088/0004-637X/800/1/20

  25. [33]

    2019, radio-astro-tools/spectral-cube: Release v0.4.5, v0.4.5 Zenodo, doi: 10.5281/zenodo.3558614

    Ginsburg, A., Koch, E., Robitaille, T., et al. 2019, radio-astro-tools/spectral-cube: Release v0.4.5, v0.4.5 Zenodo, doi: 10.5281/zenodo.3558614

  26. [34]

    M., Papaderos, P., Kehrig, C., et al

    Gomes, J. M., Papaderos, P., Kehrig, C., et al. 2016, A&A, 588, A68, doi: 10.1051/0004-6361/201525976

  27. [35]

    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 SFE and Galaxy Quenching with GBT-EDGE27 Gonz´ alez Delgado, R. M., P´ erez, E., Cid Fernandes, R., et al. 2014, A&A, 562, A47, doi: 10.1051/0004-6361/201322011 Gonz´ alez De...

  28. [36]

    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

  29. [37]

    M., & Best, P

    Heckman, T. M., & Best, P. N. 2014, ARA&A, 52, 589, doi: 10.1146/annurev-astro-081913-035722

  30. [38]

    Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90

  31. [39]

    G., Nielsen, N

    Kacprzak, G. G., Nielsen, N. M., Nateghi, H., et al. 2021, MNRAS, 500, 2289, doi: 10.1093/mnras/staa3461

  32. [40]

    F., et al

    Kalinova, V., Colombo, D., S´ anchez, S. F., et al. 2021, A&A, 648, A64, doi: 10.1051/0004-6361/202039896

  33. [41]

    Kennicutt, R. C. 1998, ApJ, 498, 541, doi: 10.1086/305588

  34. [42]

    C., & Evans, N

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

  35. [43]

    J., Dopita, M

    Kewley, L. J., Dopita, M. A., Sutherland, R. S., Heisler, C. A., & Trevena, J. 2001, ApJ, 556, 121, doi: 10.1086/321545

  36. [44]

    2007, PASJ, 59, 117, doi: 10.1093/pasj/59.1.117

    Kuno, N., Sato, N., Nakanishi, H., et al. 2007, PASJ, 59, 117, doi: 10.1093/pasj/59.1.117

  37. [45]

    Lacerda, E. A. D., S´ anchez, S. F., Cid Fernandes, R., et al. 2020, MNRAS, 492, 3073, doi: 10.1093/mnras/staa008

  38. [46]

    Lacerda, E. A. D., S´ anchez, S. F., Mej´ ıa-Narv´ aez, A., et al. 2022, NewA, 97, 101895, doi: 10.1016/j.newast.2022.101895

  39. [47]

    L., & Watts, D

    Zakamska, N. L., & Watts, D. J. 2019, MNRAS, 487, 1823, doi: 10.1093/mnras/stz1316

  40. [48]

    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

  41. [49]

    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

  42. [50]

    C., Bolatto, A

    Levy, R. C., Bolatto, A. D., Teuben, P., et al. 2018, ApJ, 860, 92, doi: 10.3847/1538-4357/aac2e5

  43. [51]

    L., et al

    Lin, L., Pan, H.-A., Ellison, S. L., et al. 2019a, ApJL, 884, L33, doi: 10.3847/2041-8213/ab4815

  44. [52]

    2019b, ApJ, 872, 50, doi: 10.3847/1538-4357/aafa84

    Lin, L., Hsieh, B.-C., Pan, H.-A., et al. 2019b, ApJ, 872, 50, doi: 10.3847/1538-4357/aafa84

  45. [53]

    L., Pan, H.-A., et al

    Lin, L., Ellison, S. L., Pan, H.-A., et al. 2020, ApJ, 903, 145, doi: 10.3847/1538-4357/abba3a

  46. [54]

    L., Pan, H.-A., et al

    Lin, L., Ellison, S. L., Pan, H.-A., et al. 2022, ApJ, 926, 175, doi: 10.3847/1538-4357/ac4ccc

  47. [55]

    L., et al

    Lin, L., Pan, H.-A., Ellison, S. L., et al. 2024, ApJ, 963, 115, doi: 10.3847/1538-4357/ad18b9

  48. [56]

    D., et al

    Lin, L., Wu, P.-F., Thorp, M. D., et al. 2026, ApJ, 999, 263, doi: 10.3847/1538-4357/ae3b2b

  49. [57]

    2022, MNRAS, 514, 5035, doi: 10.1093/mnras/stac1583

    Lu, A., Boyce, H., Haggard, D., et al. 2022, MNRAS, 514, 5035, doi: 10.1093/mnras/stac1583

  50. [58]

    2023, ApJ, 943, 7, doi: 10.3847/1538-4357/aca664

    Maeda, F., Egusa, F., Ohta, K., Fujimoto, Y., & Habe, A. 2023, ApJ, 943, 7, doi: 10.3847/1538-4357/aca664

  51. [59]

    2019, A&A Rv, 27, 3, doi: 10.1007/s00159-018-0112-2

    Maiolino, R., & Mannucci, F. 2019, A&A Rv, 27, 3, doi: 10.1007/s00159-018-0112-2

  52. [60]

    2014, A&A, 570, A13, doi: 10.1051/0004-6361/201423496

    Vauglin, I. 2014, A&A, 570, A13, doi: 10.1051/0004-6361/201423496

  53. [62]

    G., Emerson, D

    Mangum, J. G., Emerson, D. T., & Greisen, E. W. 2007, A&A, 474, 679, doi: 10.1051/0004-6361:20077811

  54. [63]

    2009, ApJ, 707, 250, doi: 10.1088/0004-637X/707/1/250

    Martig, M., Bournaud, F., Teyssier, R., & Dekel, A. 2009, ApJ, 707, 250, doi: 10.1088/0004-637X/707/1/250

  55. [64]

    1996, Nature, 379, 613, doi: 10.1038/379613a0

    Moore, B., Katz, N., Lake, G., Dressler, A., & Oemler, A. 1996, Nature, 379, 613, doi: 10.1038/379613a0

  56. [65]

    2019, PASJ, 71, S15, doi: 10.1093/pasj/psz015

    Muraoka, K., Sorai, K., Miyamoto, Y., et al. 2019, PASJ, 71, S15, doi: 10.1093/pasj/psz015

  57. [66]

    2012, MNRAS, 421, 3127, doi: 10.1111/j.1365-2966.2012.20536.x

    Hernquist, L. 2012, MNRAS, 421, 3127, doi: 10.1111/j.1365-2966.2012.20536.x

  58. [67]

    J., Leroy, A

    Neumann, L., Jim´ enez-Donaire, M. J., Leroy, A. K., et al. 2025, A&A, 693, L13, doi: 10.1051/0004-6361/202453208

  59. [68]

    J., Symeonidis, M., Vieira, J

    Page, M. J., Symeonidis, M., Vieira, J. D., et al. 2012, Nature, 485, 213, doi: 10.1038/nature11096

  60. [69]

    L., et al

    Pan, H.-A., Lin, L., Ellison, S. L., et al. 2024, ApJ, 964, 120, doi: 10.3847/1538-4357/ad28c1

  61. [70]

    P., van der Werf, P., Xilouris, E., Isaak, K

    Papadopoulos, P. P., van der Werf, P., Xilouris, E., Isaak, K. G., & Gao, Y. 2012, ApJ, 751, 10, doi: 10.1088/0004-637X/751/1/10

  62. [71]

    J., Kovaˇ c, K., et al

    Peng, Y.-j., Lilly, S. J., Kovaˇ c, K., et al. 2010, ApJ, 721, 193, doi: 10.1088/0004-637X/721/1/193 P´ erez, F., & Granger, B. E. 2007, Computing in Science and Engineering, 9, 21, doi: 10.1109/MCSE.2007.53

  63. [72]

    Pettini, M., & Pagel, B. E. J. 2004, MNRAS, 348, L59, doi: 10.1111/j.1365-2966.2004.07591.x

  64. [73]

    S., & Grebel, E

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

  65. [74]

    2023, A&A, 680, A4, doi: 10.1051/0004-6361/202143023

    Querejeta, M., Pety, J., Schruba, A., et al. 2023, A&A, 680, A4, doi: 10.1051/0004-6361/202143023

  66. [75]

    K., Meidt, S

    Querejeta, M., Leroy, A. K., Meidt, S. E., et al. 2024, A&A, 687, A293, doi: 10.1051/0004-6361/202449733

  67. [76]

    J., & Dame, T

    Reid, M. J., & Dame, T. M. 2016, ApJ, 832, 159, doi: 10.3847/0004-637X/832/2/159

  68. [77]

    2019, A&A, 621, A104, doi: 10.1051/0004-6361/201834397 28Teng et al

    Renaud, F., Bournaud, F., Daddi, E., & Weiß, A. 2019, A&A, 621, A104, doi: 10.1051/0004-6361/201834397 28Teng et al

  69. [78]

    Robitaille, T., Deil, C., & Ginsburg, A. 2020, reproject: Python-based astronomical image reprojection,, Astrophysics Source Code Library, record ascl:2011.023 http://ascl.net/2011.023 Rosa-Gonz´ alez, D., Terlevich, E., & Terlevich, R. 2002, MNRAS, 332, 283, doi: 10.1046/j.13...

  70. [79]

    2006, PASP, 118, 590, doi: 10.1086/502982

    Rosolowsky, E., & Leroy, A. 2006, PASP, 118, 590, doi: 10.1086/502982

  71. [80]

    1966, Proceedings of the IEEE, 54, 633, doi: 10.1109/PROC.1966.4784

    Ruze, J. 1966, Proceedings of the IEEE, 54, 633, doi: 10.1109/PROC.1966.4784

  72. [81]

    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

  73. [82]

    2011, MNRAS, 415, 61, doi: 10.1111/j.1365-2966.2011.18823.x

    Saintonge, A., Kauffmann, G., Wang, J., et al. 2011, MNRAS, 415, 61, doi: 10.1111/j.1365-2966.2011.18823.x

  74. [83]

    J., Fabello, S., et al

    Saintonge, A., Tacconi, L. J., Fabello, S., et al. 2012, ApJ, 758, 73, doi: 10.1088/0004-637X/758/2/73

  75. [84]

    2016, MNRAS, 462, 1749, doi: 10.1093/mnras/stw1715

    Saintonge, A., Catinella, B., Cortese, L., et al. 2016, MNRAS, 462, 1749, doi: 10.1093/mnras/stw1715

  76. [85]

    J., et al

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

  77. [86]

    K., Ishizuki, S., & Scoville, N

    Sakamoto, K., Okumura, S. K., Ishizuki, S., & Scoville, N. Z. 1999, ApJ, 525, 691, doi: 10.1086/307910

  78. [87]

    2023, ApJ, 958, 183, doi: 10.3847/1538-4357/ad04db

    Salim, S., Tacchella, S., Osborne, C., et al. 2023, ApJ, 958, 183, doi: 10.3847/1538-4357/ad04db

  79. [88]

    M., Charlot, S., et al

    Salim, S., Rich, R. M., Charlot, S., et al. 2007, ApJS, 173, 267, doi: 10.1086/519218

  80. [89]

    Salpeter, E. E. 1955, ApJ, 121, 161, doi: 10.1086/145971 S´ anchez, S. F. 2020, ARA&A, 58, 99, doi: 10.1146/annurev-astro-012120-013326 S´ anchez, S. F., Galbany, L., Walcher, C. J., Garc´ ıa-Benito, R., & Barrera-Ballesteros, J. K. 2023, MNRAS, 526, 5555, doi: 10.1093/mnras/s...

  81. [90]

    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

  82. [91]

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

  83. [92]

    Teuben, P. J. 2005, ApJ, 632, 217, doi: 10.1086/432409

  84. [93]

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

  85. [94]

    2014, in Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VII, ed

    Sieth, M., Devaraj, K., Voll, P., et al. 2014, in Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VII, ed. W. S. Holland & J. Zmuidzinas, Vol. 9153, 91530P, doi: 10.1117/12.2055655

  86. [95]

    2022, ApJ, 934, 173, doi: 10.3847/1538-4357/ac77fd

    Su, Y.-C., Lin, L., Pan, H.-A., et al. 2022, ApJ, 934, 173, doi: 10.3847/1538-4357/ac77fd

  87. [96]

    K., Rosolowsky, E., et al

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

  88. [97]

    K., Ostriker, E

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

  89. [98]

    2025, ApJ, 994, 263, doi: 10.3847/1538-4357/ae10be

    Sun, J., Teng, Y.-H., Chiang, I.-D., et al. 2025, ApJ, 994, 263, doi: 10.3847/1538-4357/ae10be

  90. [99]

    M., Sun, J., et al

    Teng, Y.-H., Sandstrom, K. M., Sun, J., et al. 2022, ApJ, 925, 72, doi: 10.3847/1538-4357/ac382f

  91. [100]

    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

  92. [101]

    M., et al

    Teng, Y.-H., Chiang, I.-D., Sandstrom, K. M., et al. 2024, ApJ, 961, 42, doi: 10.3847/1538-4357/ad10ae

  93. [102]

    G., Sormani, M

    Tress, R. G., Sormani, M. C., Glover, S. C. O., et al. 2020, MNRAS, 499, 4455, doi: 10.1093/mnras/staa3120

  94. [103]

    D., Wong, T., et al

    Utomo, D., Bolatto, A. D., Wong, T., et al. 2017, ApJ, 849, 26, doi: 10.3847/1538-4357/aa88c0

  95. [104]

    2021, ApJ, 923, 60, doi: 10.3847/1538-4357/ac2b29

    Villanueva, V., Bolatto, A., Vogel, S., et al. 2021, ApJ, 923, 60, doi: 10.3847/1538-4357/ac2b29

  96. [105]

    D., Vogel, S., et al

    Villanueva, V., Bolatto, A. D., Vogel, S., et al. 2022, ApJ, 940, 176, doi: 10.3847/1538-4357/ac9d3c

  97. [106]

    D., Vogel, S

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

  98. [107]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: https://doi.org/10.1038/s41592-019-0686-2

  99. [108]

    J., Wisotzki, L., Bekerait´ e, S., et al

    Walcher, C. J., Wisotzki, L., Bekerait´ e, S., et al. 2014, A&A, 569, A1, doi: 10.1051/0004-6361/201424198

  100. [109]

    R., Harrison, C

    Ward, S. R., Harrison, C. M., Costa, T., & Mainieri, V. 2022, MNRAS, 514, 2936, doi: 10.1093/mnras/stac1219

  101. [110]

    G., Hollenbach, D., & McKee, C

    Wolfire, M. G., Hollenbach, D., & McKee, C. F. 2010, ApJ, 716, 1191, doi: 10.1088/0004-637X/716/2/1191

  102. [111]

    2024, ApJS, 271, 35, doi: 10.3847/1538-4365/ad20c9

    Wong, T., Cao, Y., Luo, Y., et al. 2024, ApJS, 271, 35, doi: 10.3847/1538-4365/ad20c9

  103. [112]

    K., Martin, D

    Wyder, T. K., Martin, D. C., Schiminovich, D., et al. 2007, ApJS, 173, 293, doi: 10.1086/521402

  104. [113]

    2021, PASJ, 73, 257, doi: 10.1093/pasj/psaa119 SFE and Galaxy Quenching with GBT-EDGE29

    Yajima, Y., Sorai, K., Miyamoto, Y., et al. 2021, PASJ, 73, 257, doi: 10.1093/pasj/psaa119 SFE and Galaxy Quenching with GBT-EDGE29

  105. [114]

    2023, PASJ, 75, 743, doi: 10.1093/pasj/psad034

    Yasuda, A., Kuno, N., Sorai, K., et al. 2023, PASJ, 75, 743, doi: 10.1093/pasj/psad034

  106. [115]

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

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

  107. [116]

    M., Bureau, M., Davis, T

    Young, L. M., Bureau, M., Davis, T. A., et al. 2011, MNRAS, 414, 940, doi: 10.1111/j.1365-2966.2011.18561.x

  108. [117]

    2022, A&A, 666, A175, doi: 10.1051/0004-6361/202244306

    Yu, S.-Y., Kalinova, V., Colombo, D., et al. 2022, A&A, 666, A175, doi: 10.1051/0004-6361/202244306

Pith tools

Reviewed June 26, 2026 · model on record in the stance chip above.