ALMA CO-CAVITY I. Resolved Molecular Gas in Void Galaxies
Pith reviewed 2026-05-25 04:09 UTC · model grok-4.3
The pith
Void galaxies exhibit molecular gas scaling relations compatible with those in denser environments.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The ALMA CO-CAVITY survey of 41 void galaxies finds that scaling relations for molecular gas, including the molecular gas main sequence, Schmidt-Kennicutt relation, and star-forming main sequence, are compatible with those in denser environments when derived from integrated properties.
What carries the argument
ALMA CO(1-0) interferometric observations at 1 arcsec resolution combined with CAVITY optical IFU data, enabling pixel-to-pixel maps of molecular gas, star formation rate, and stellar mass surface densities at 2.5 arcsec resolution.
Load-bearing premise
The sample of 41 void galaxies provides a good representation of the void galaxy population and follows the distribution of key properties seen in star-forming galaxy samples.
What would settle it
A larger sample of void galaxies or one selected differently showing statistically significant offsets or increased scatter in the molecular gas main sequence relative to denser-environment samples would falsify the compatibility claim.
Figures
read the original abstract
The environment plays a key role in galaxy evolution, yet it remains unclear how detailed molecular gas properties and their connection to star formation and stellar content are influenced by both large-scale and local environments. Here we introduce the ALMA CO-CAVITY project, the first interferometric CO(1-0) survey of a large sample of 41 void galaxies (VGs) to characterise in detail their molecular gas properties. It is built over the CAVITY project, offering optical integral field unit (IFU) data, enabling a direct, pixel-to-pixel comparison between molecular gas (from ALMA), star formation, and stellar properties, as well as the derivation of their scaling relations. In this work we present ALMA data products for our sample, containing data cubes, moment maps and position-velocity diagrams at angular resolutions of 1 arcsec. We also present molecular gas, stellar mass, and star formation rate surface density maps at a common resolution of 2.5 arcsec. We contextualise our sample against representative unresolved and resolved surveys. While our sample provides a good representation of the VG population and follows the distribution of key properties seen in star-forming galaxy samples, galaxies included in resolved studies from the literature tend to be more massive, less isolated, and located in denser large-scale environments. We present global scaling relations for the ALMA CO-CAVITY sample and find that the molecular gas main sequence exhibits the smallest scatter (0.21 dex), followed by the Schmidt-Kennicutt relation and the star-forming main sequence. From integrated properties alone, we find that these scaling relations for VGs are compatible with those for denser environments. This paper lays the foundation for forthcoming studies exploiting this unique dataset.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript introduces the ALMA CO-CAVITY project as the first interferometric CO(1-0) survey of 41 void galaxies, built on the CAVITY IFU sample. It presents data products (cubes, moment maps, PV diagrams at ~1 arcsec resolution and surface density maps at 2.5 arcsec), contextualizes the sample against literature surveys, and reports that the molecular gas main sequence (scatter 0.21 dex), Schmidt-Kennicutt relation, and star-forming main sequence derived from integrated properties are compatible with those found in denser environments.
Significance. If the sample is shown to be representative, the work supplies the first sizable resolved molecular-gas dataset in voids and indicates that large-scale underdensity does not alter the integrated scaling relations, providing a useful baseline for environmental studies of the molecular gas-star formation connection.
major comments (2)
- [Abstract] Abstract and sample contextualization section: The assertion that 'our sample provides a good representation of the VG population and follows the distribution of key properties seen in star-forming galaxy samples' is presented without quantitative statistical comparisons (e.g., KS tests, histograms, or median/percentile tables) to the parent CAVITY catalog or to the unresolved/resolved literature samples referenced; this directly underpins the generalization of the compatibility result.
- [Sample selection and observations] Sample selection and observations section: No explicit quantification of ALMA target selection effects or post-selection biases in M_star, SFR, or isolation is provided, nor is there verification that the 41 galaxies match the parent distribution in these parameters; without this, the central claim that the relations are compatible from integrated properties alone cannot be evaluated for the broader VG population.
minor comments (2)
- [Data products] The common 2.5 arcsec resolution for the surface density maps is stated but the convolution kernel and any associated flux-loss corrections are not described.
- [Scaling relations] The reported scatter of 0.21 dex for the molecular gas main sequence should be accompanied by the fitting method (e.g., orthogonal distance regression) and the number of galaxies entering each relation.
Simulated Author's Rebuttal
We thank the referee for their constructive comments, which highlight the need for stronger statistical support of our sample's representativeness. We will revise the manuscript to address these points directly.
read point-by-point responses
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Referee: [Abstract] Abstract and sample contextualization section: The assertion that 'our sample provides a good representation of the VG population and follows the distribution of key properties seen in star-forming galaxy samples' is presented without quantitative statistical comparisons (e.g., KS tests, histograms, or median/percentile tables) to the parent CAVITY catalog or to the unresolved/resolved literature samples referenced; this directly underpins the generalization of the compatibility result.
Authors: We agree that quantitative comparisons are required to substantiate the representativeness claim. In the revised manuscript we will add Kolmogorov-Smirnov tests, histograms, and tables reporting medians and percentiles for stellar mass, SFR, and isolation, comparing the ALMA CO-CAVITY sample both to the full CAVITY parent catalog and to the unresolved and resolved literature samples cited in the text. revision: yes
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Referee: [Sample selection and observations] Sample selection and observations section: No explicit quantification of ALMA target selection effects or post-selection biases in M_star, SFR, or isolation is provided, nor is there verification that the 41 galaxies match the parent distribution in these parameters; without this, the central claim that the relations are compatible from integrated properties alone cannot be evaluated for the broader VG population.
Authors: We acknowledge that explicit quantification of selection effects is necessary. The revised sample selection section will include a discussion of ALMA target selection criteria and any resulting biases, together with statistical verification (KS tests and distribution comparisons) confirming that the 41 galaxies are consistent with the parent CAVITY distributions in M_star, SFR, and isolation. These additions will allow readers to assess the applicability of the integrated scaling relations to the broader void-galaxy population. revision: yes
Circularity Check
No circularity: observational data presentation with independent scaling relations
full rationale
The paper reports new ALMA CO(1-0) observations of 41 void galaxies, produces moment maps and surface density maps, and computes global scaling relations (molecular gas main sequence, Schmidt-Kennicutt, star-forming main sequence) directly from the integrated and resolved data. No equations derive a quantity from a fitted parameter that is then relabeled as a prediction; no self-citation chain supplies a uniqueness theorem or ansatz that the central claim depends on; the compatibility statement is an empirical comparison of the observed relations to literature values for denser environments. The representativeness statement is an assertion about sample selection, not a derivation that reduces to its own inputs by construction. This is a standard observational survey paper whose results are externally falsifiable against independent datasets.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Standard CO-to-H2 conversion factor assumptions in extragalactic astronomy
Reference graph
Works this paper leans on
-
[1]
2017, MNRAS, 470, 4750 8 Argudo-Fernández, M., Verley, S., Bergond, G., et al
Accurso, G., Saintonge, A., Catinella, B., et al. 2017, MNRAS, 470, 4750 8 Argudo-Fernández, M., Verley, S., Bergond, G., et al. 2015, A&A, 578, A110 4, 7 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167 13
2017
-
[2]
M., Maiolino, R., Bluck, A
Baker, W. M., Maiolino, R., Bluck, A. F. L., et al. 2022, MNRAS, 510, 3622 3
2022
-
[3]
K., Sánchez, S
Barrera-Ballesteros, J. K., Sánchez, S. F., Heckman, T., et al. 2021, MNRAS, 503, 3643 3
2021
-
[4]
M., & van Gorkom, J
Beygu, B., Kreckel, K., van de Weygaert, R., van der Hulst, J. M., & van Gorkom, J. H. 2013, AJ, 145, 120 1, 2
2013
-
[5]
M., et al
Beygu, B., Kreckel, K., van der Hulst, J. M., et al. 2016, MNRAS, 458, 394 1
2016
-
[6]
Blanton, M. R. & Moustakas, J. 2009, ARA&A, 47, 159 1
2009
-
[7]
D., Wolfire, M., & Leroy, A
Bolatto, A. D., Wolfire, M., & Leroy, A. K. 2013, ARA&A, 51, 207 3
2013
-
[8]
D., Wong, T., Utomo, D., et al
Bolatto, A. D., Wong, T., Utomo, D., et al. 2017, ApJ, 846, 159 2, 6, 10
2017
-
[9]
R., Kofman, L., & Pogosyan, D
Bond, J. R., Kofman, L., & Pogosyan, D. 1996, Nature, 380, 603 1
1996
-
[10]
Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151 3, 9
2004
-
[11]
2013, in ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 108–122 13
Buitinck, L., Louppe, G., Blondel, M., et al. 2013, in ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 108–122 13
2013
-
[12]
A., Law, D
Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7 7
2015
-
[13]
Cautun, M., van de Weygaert, R., Jones, B. J. T., & Frenk, C. S. 2014, MNRAS, 441, 2923 1
2014
-
[14]
2003, Publications of the Astronomical Society of the Pacific, 115, 763 2
Chabrier, G. 2003, Publications of the Astronomical Society of the Pacific, 115, 763 2
2003
-
[15]
2025, A&A, 699, A367 9, 10, 12
Colombo, D., Kalinova, V ., Bazzi, Z., et al. 2025, A&A, 699, A367 9, 10, 12
2025
-
[16]
Curtis, O., McDonough, B., & Brainerd, T. G. 2024, ApJ, 962, 58 2
2024
-
[17]
2015, ApJ, 815, 40 2 Domínguez-Gómez, J., Lisenfeld, U., Pérez, I., et al
Das, M., Saito, T., Iono, D., Honey, M., & Ramya, S. 2015, ApJ, 815, 40 2 Domínguez-Gómez, J., Lisenfeld, U., Pérez, I., et al. 2022, A&A, 658, A124 1, 10 Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., &
2015
-
[18]
Fischer, J. L. 2018, MNRAS, 476, 3661 3, 6 Domínguez-Gómez, J., Pérez, I., Ruiz-Lara, T., et al. 2023, Nature, 619, 269, aDS Bibcode: 2023Natur.619..269D 1, 2, 12
2018
-
[19]
1980, ApJ, 236, 351 2 Duarte Puertas, S., Vilchez, J
Dressler, A. 1980, ApJ, 236, 351 2 Duarte Puertas, S., Vilchez, J. M., Iglesias-Páramo, J., et al. 2017, A&A, 599, A71 6
1980
-
[20]
S., Moiseev, A
Egorova, E. S., Moiseev, A. V ., & Egorov, O. V . 2019, MNRAS, 482, 3403 1
2019
-
[21]
J., Weinberg, D
Eisenstein, D. J., Weinberg, D. H., Agol, E., et al. 2011, AJ, 142, 72 2
2011
-
[22]
L., Lin, L., Thorp, M
Ellison, S. L., Lin, L., Thorp, M. D., et al. 2021, MNRAS, 501, 4777 3
2021
-
[23]
L., Pan, H.-A., Bluck, A
Ellison, S. L., Pan, H.-A., Bluck, A. F. L., et al. 2024, MNRAS, 527, 10201 2, 3
2024
-
[24]
L., Thorp, M
Ellison, S. L., Thorp, M. D., Lin, L., et al. 2020, MNRAS, 493, L39 3
2020
-
[25]
2022, A&A, 659, A191 2
Emsellem, E., Schinnerer, E., Santoro, F., et al. 2022, A&A, 659, A191 2
2022
-
[26]
2018, ApJ, 866, 77 12
Espada, D., Martin, S., Verley, S., et al. 2018, ApJ, 866, 77 12
2018
-
[27]
A., Kannappan, S
Florez, J., Berlind, A. A., Kannappan, S. J., et al. 2021, ApJ, 906, 97 2 García-Benito, R., Jiménez, A., Sánchez-Menguiano, L., et al. 2024, A&A, 691, A161 6, 7
2021
-
[28]
M., Brasseur, C
Ginsburg, A., Sip˝ocz, B. M., Brasseur, C. E., et al. 2019, AJ, 157, 98 13
2019
-
[29]
E., et al
Ginsburg, A., Sip˝ocz, B., Brasseur, C. E., et al. 2024, astropy/astroquery: v0.4.7 13
2024
-
[30]
2024, scipy/scipy: SciPy 1.13.0 13
Gommers, R., Virtanen, P., Haberland, M., et al. 2024, scipy/scipy: SciPy 1.13.0 13
2024
-
[31]
2024, scikit-learn/scikit-learn: Scikit-learn 1.4.1.post1 13
Grisel, O., Mueller, A., Lars, et al. 2024, scikit-learn/scikit-learn: Scikit-learn 1.4.1.post1 13
2024
-
[32]
R., Millman, K
Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357 13
2020
-
[33]
Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90 13
2007
-
[34]
2020, MN- RAS, 493, 1982 3, 4, 6, 8, 9
Janowiecki, S., Catinella, B., Cortese, L., Saintonge, A., & Wang, J. 2020, MN- RAS, 493, 1982 3, 4, 6, 8, 9
2020
-
[35]
M., White, S
Kauffmann, G., Heckman, T. M., White, S. D. M., et al. 2003, MNRAS, 341, 33 3, 6
2003
-
[36]
Kennicutt, R. C. & Evans, N. J. 2012, ARA&A, 50, 531 8
2012
-
[37]
Kennicutt, Jr., R. C. 1998, ARA&A, 36, 189 9
1998
-
[38]
A., Jones, D
Kleiner, D., Pimbblet, K. A., Jones, D. H., Koribalski, B. S., & Serra, P. 2017, MNRAS, 466, 4692 2
2017
-
[39]
2016, in ELPUB, 87–90 13
Kluyver, T., Ragan-Kelley, B., Pérez, F., et al. 2016, in ELPUB, 87–90 13
2016
-
[40]
Kreckel, K., Croxall, K., Groves, B., van de Weygaert, R., & Pogge, R. W. 2015, ApJ, 798, L15 2
2015
-
[41]
A., et al
Kreckel, K., Platen, E., Aragón-Calvo, M. A., et al. 2012, AJ, 144, 16 2
2012
-
[42]
2001, Monthly Notices of the Royal Astronomical Society, 322, 231, publisher: Oxford Academic 2
Kroupa, P. 2001, Monthly Notices of the Royal Astronomical Society, 322, 231, publisher: Oxford Academic 2
2001
-
[43]
A., Sánchez, S., Mejía-Narváez, A., et al
Lacerda, E. A., Sánchez, S., Mejía-Narváez, A., et al. 2022, New Astronomy, 97, 101895 7
2022
-
[44]
N., Cen, R., Ostriker, J
Lackner, C. N., Cen, R., Ostriker, J. P., & Joung, M. R. 2012, MNRAS, 425, 641 1
2012
-
[45]
L., Pan, H.-A., et al
Lin, L., Ellison, S. L., Pan, H.-A., et al. 2020, ApJ, 903, 145 2, 6
2020
-
[46]
L., et al
Lin, L., Pan, H.-A., Ellison, S. L., et al. 2019, ApJ, 884, L33 3, 11
2019
-
[47]
2011, A&A, 534, A102 12
Lisenfeld, U., Espada, D., Verdes-Montenegro, L., et al. 2011, A&A, 534, A102 12
2011
-
[48]
& Dickinson, M
Madau, P. & Dickinson, M. 2014, Annual Review of Astronomy and Astro- physics, 52, 415 3
2014
-
[49]
C., V ogeley, M
Pan, D. C., V ogeley, M. S., Hoyle, F., Choi, Y .-Y ., & Park, C. 2012, MNRAS, 421, 926 1, 3, 4, 7
2012
-
[50]
L., et al
Pan, H.-A., Lin, L., Ellison, S. L., et al. 2024, ApJ, 964, 120 3, 10 pandas development team, T. 2024, pandas-dev/pandas: Pandas 13
2024
-
[51]
2011, Journal of Machine Learning Research, 12, 2825 13
Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825 13
2011
-
[52]
Peebles, P. J. E. 1980, The large-scale structure of the universe 1
1980
-
[53]
& Granger, B
Perez, F. & Granger, B. E. 2007, Computing in Science and Engineering, 9, 21 13 Pérez, I., Gil, L., Ferré-Mateu, A., et al. 2025, A&A, 695, A84 2 Pérez, I., Verley, S., Sánchez-Menguiano, L., et al. 2024, A&A, 689, A213 3, 4, 6, 7
2007
-
[54]
& Pagel, B
Pettini, M. & Pagel, B. E. J. 2004, MNRAS, 348, L59 6, 8 Article number, page 13 of 20 A&A proofs:manuscript no. aanda
2004
-
[55]
Pustilnik, S. A. & Tepliakova, A. L. 2011, MNRAS, 415, 1188 2
2011
-
[56]
2014, MNRAS, 445, 4045 1, 11 Rodríguez, M
Ricciardelli, E., Cava, A., Varela, J., & Quilis, V . 2014, MNRAS, 445, 4045 1, 11 Rodríguez, M. I., Lisenfeld, U., Duarte Puertas, S., et al. 2024, A&A, 692, A125 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 18, 20
2014
-
[57]
Rosas-Guevara, Y ., Tissera, P., Lagos, C. d. P., Paillas, E., & Padilla, N. 2022, MNRAS, 517, 712 2
2022
-
[58]
J., Weistrop, D., Cruzen, S., & Kompe, C
Sage, L. J., Weistrop, D., Cruzen, S., & Kompe, C. 1997, AJ, 114, 1753 2
1997
-
[59]
2016, MNRAS, 462, 1749 9
Saintonge, A., Catinella, B., Cortese, L., et al. 2016, MNRAS, 462, 1749 9
2016
-
[60]
J., et al
Saintonge, A., Catinella, B., Tacconi, L. J., et al. 2017, ApJS, 233, 22 2, 6, 9
2017
-
[61]
M., Charlot, S., et al
Salim, S., Rich, R. M., Charlot, S., et al. 2007, ApJS, 173, 267 3, 6
2007
-
[62]
Salpeter, E. E. 1955, ApJ, 121, 161 2 Sánchez, S. F., Barrera-Ballesteros, J. K., Lacerda, E., et al. 2022, ApJS, 262, 36 7 Sánchez, S. F., García-Benito, R., González Delgado, R., et al. 2024, Rev. Mex- icana Astron. Astrofis., 60, 323 7 Sánchez, S. F., García-Benito, R., Zibetti, S., et al. 2016a, A&A, 594, A36 7 Sánchez, S. F., Pérez, E., Sánchez-Blázq...
1955
-
[63]
2026, A&A, 706, A265 2, 12
Sharma, E., Biswas, P., Das, M., et al. 2026, A&A, 706, A265 2, 12
2026
-
[64]
M., Downes, D., Radford, S
Solomon, P. M., Downes, D., Radford, S. J. E., & Barrett, J. W. 1997, ApJ, 478, 144 8
1997
-
[65]
S., & White, S
Springel, V ., Frenk, C. S., & White, S. D. M. 2006, Nature, 440, 1137 1
2006
-
[66]
2022, ApJ, 934, 173 10
Su, Y .-C., Lin, L., Pan, H.-A., et al. 2022, ApJ, 934, 173 10
2022
-
[67]
Tempel, E., Tuvikene, T., Kipper, R., & Libeskind, N. I. 2017, A&A, 602, A100 3, 4, 7 Torres-Ríos, G., Pérez, I., Verley, S., et al. 2024, A&A, 691, A341 2, 4, 7
2017
-
[68]
A., Heckman, T
Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898 3 van de Weygaert, R. & Platen, E. 2011, in International Journal of Modern Physics Conference Series, V ol. 1, International Journal of Modern Physics Conference Series, 41–66 1 Van Rossum, G. & Drake, F. L. 2009, Python 3 Reference Manual (Scotts Valley, CA: CreateSpace) 13
2004
-
[69]
E., et al
Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261 13 Wes McKinney. 2010, in Proceedings of the 9th Python in Science Conference, ed. Stéfan van der Walt & Jarrod Millman, 56 – 61 13
2020
-
[70]
2021, MNRAS, 506, 3962 6
Westmeier, T., Kitaeff, S., Pallot, D., et al. 2021, MNRAS, 506, 3962 6
2021
-
[71]
2024, ApJS, 271, 35 2 Article number, page 14 of 20 D
Wong, T., Cao, Y ., Luo, Y ., et al. 2024, ApJS, 271, 35 2 Article number, page 14 of 20 D. Espada et al.: ALMA CO-CA VITY I. Resolved Molecular Gas in V oid Galaxies Appendix A: Information of ALMA CO-CAVITY observations and properties of ALMA data cubes Tab. A.1 summarises the observational and processing characteristics of the ALMA datasets used in thi...
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