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arxiv: 2605.30533 · v1 · pith:QKKPYNMXnew · submitted 2026-05-28 · 🌌 astro-ph.GA

Automated void identification by Blendmask: from hierarchical molecular gas to hierarchical voids in NGC 628

J. W. Zhou , A. A. Han This is my paper

Pith reviewed 2026-06-29 06:03 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords voidsmolecular cloudsstellar feedbackNGC 628deep learningevolutionary sequencestar formationgalactic structure
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The pith

Voids in NGC 628 originate inside molecular clouds, expand via stellar feedback, and detach as they grow.

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

The paper uses a deep learning method to identify voids in JWST images of NGC 628 and refines them by intensity contrast. It builds networks that connect these voids to CO molecular gas, 21μm infrared sources, and Hα emission to measure spatial separations. The networks reveal that larger voids sit progressively farther from CO clouds but follow a sequence toward infrared and Hα sources. This pattern, plus the finding that void-associated molecular clouds are more massive and evolved, supports the claim that some voids begin inside clouds, grow through feedback, and separate over time. Voids may therefore act as an extra tracer for stellar populations that catalogs miss.

Core claim

The central claim is that the nine constructed source-pair networks show 21μm and Hα sources with the strongest associations overall, while larger voids exhibit systematically increasing separations from CO to 21μm to Hα sources to voids; this sequence, together with the 68% overlap between void-associated clouds and 21μm sources and the greater mass of those clouds, indicates that voids originate within molecular clouds, grow through stellar feedback, and gradually detach from their parent structures.

What carries the argument

BlendMask automated void detection on the JWST MIRI F770W image, refined by intensity contrast, combined with network graphs that link each CO, 21μm, Hα, and void source pair to quantify spatial separations.

If this is right

  • Voids linked to star clusters or associations have lower intensity contrast and larger sizes than those without.
  • Void size anticorrelates with intensity contrast, implying larger voids have emptier centers from stronger feedback.
  • Molecular clouds associated with voids are significantly more massive and more evolved than clouds without voids.
  • 68 percent of molecular clouds tied to voids are also tied to 21μm sources.
  • Only up to 17.6 percent of voids coincide with catalogued star clusters or associations, with overlapping B-band flux distributions for the rest.

Where Pith is reading between the lines

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

  • The same network method could map feedback timelines in galaxies where cluster catalogs are incomplete.
  • Age-dating the stellar populations near voids would provide an independent test of the separation sequence.
  • Void catalogs might help locate embedded clusters in high-extinction regions missed by optical surveys.

Load-bearing premise

The measured spatial separations in the networks reflect a causal temporal evolutionary sequence rather than static correlations or detection limits.

What would settle it

A calculation showing that randomly placed voids produce the same pattern of increasing separations from CO to 21μm to Hα sources would falsify the evolutionary interpretation.

Figures

Figures reproduced from arXiv: 2605.30533 by A. A. Han, J. W. Zhou.

Figure 2
Figure 2. Figure 2: A void used to show the calculation of the intensity contrast in Sec.3.1.2. independently within region proposals as in Mask R-CNN, BlendMask first constructs a shared dense feature map and then generates instance-specific attention maps from detec￾tion features. These attention maps are used to selectively weight and blend the shared features, producing the final instance masks in a more efficient manner.… view at source ↗
Figure 1
Figure 1. Figure 1: The JWST F770W image of a subregion in NGC 628 marked by the blue box in Fig.3. (a) Red contours marked by LabelMe show the voids recognized by the human eye; (b) Blend￾Mask outputs segmentation masks for all detected objects. We fit ellipses to the mask boundaries to extract center coordinates, ma￾jor/minor axis lengths, and tilt angles. background, which can be transformed into an instance seg￾mentation … view at source ↗
Figure 3
Figure 3. Figure 3: Red ellipses denote small-scale voids (< 120 pc), while cyan dashed ellipses indicate large-scale voids (> 120 pc) in the merged catalog, after removing hierarchical voids as described in Sec.3.2. The three magenta boxes mark the zoom-in regions shown on the right (magenta ellipses: < 120 pc; white ellipses: > 120 pc), and the blue box outlines the area displayed in Fig.1. The background of all maps is the… view at source ↗
Figure 4
Figure 4. Figure 4: Examples of voids from categories voids-A0, voids-B2, and voids-B1 classified in Sec.3.3. Table.1 summarizes the definitions of the different void categories. For the five voids selected as examples from each category, the first, second and third rows show the JWST F770W, HST F435W and JWST F335M images, respectively. Ellipses indicate the identified voids, and pluses mark the central positions of the iden… view at source ↗
Figure 6
Figure 6. Figure 6: Correlation between the intensity contrast and radius of voids. k and r are the slope and the Pearson correlation coefficient of the linear fit, respectively [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The CO-Hα network constructed in Sec.3.4. Red and cyan dots represent CO and Hα sources, respectively. Blue dashed lines show the convex hull boundary determined by the spatial distribution of the 21µm sources. weights, and connection rules. In this work, we focus solely on the spatial separations between nodes and therefore dis￾regard the edge weights. The nodes are the voids identified in this work and t… view at source ↗
Figure 8
Figure 8. Figure 8: Distributions of average and minimum separations across all nine networks. Using the CO–Hα network as an example, we examine all Hα sources neighboring a given CO source and calculate the average separation between them (l), or focus on the nearest Hα source to determine the smallest separation (ls). The void sample is divided into two subsets based on the median void radius (”small” and ”large”). to exami… view at source ↗
read the original abstract

We identify voids in NGC 628 from the JWST MIRI F770W image using a deep_learning method (BlendMask) and refine them by intensity contrast. These voids may be feedback_driven bubbles or dynamically formed structures. Cross_matching with archival star cluster/association catalogs shows that only up to 17.6% of voids are associated with such stellar populations. HST B_band peak_flux distributions of voids with and without these populations overlap substantially, suggesting many related clusters/associations remain unidentified or misclassified. Voids associated with star clusters/associations tend to have lower intensity contrast and larger sizes. An anti_correlation between void size and intensity contrast indicates larger voids have emptier centers, possibly due to stronger feedback. Thus, voids may provide a complementary tracer for identifying stellar populations and constraining their physical properties. To quantify spatial relationships among CO, 21$\mu$m, H$_{\alpha}$ sources, and voids, we construct networks linking each source pair. Among the nine networks, 21$\mu$m and H$_{\alpha}$ sources show the strongest spatial association. Compared to small voids, large voids exhibit progressively increasing separations from CO to 21$\mu$m to H$_{\alpha}$ sources to voids, consistent with an evolutionary sequence in space and time. Smaller voids lie closer to molecular clouds, while larger voids are more displaced. Compared with molecular clouds not associated with voids, those associated with voids are significantly more massive and appear more evolved. Indeed, 68% of molecular clouds associated with voids are also associated with 21$\mu$m sources. These results support an evolutionary scenario where some voids originate within molecular clouds, grow through stellar feedback, and gradually detach from their parent clouds.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper applies the BlendMask deep-learning algorithm to identify and refine voids in the JWST MIRI F770W image of NGC 628. It reports a 17.6% association rate with known star clusters/associations, substantial overlap in HST B-band flux distributions, an anti-correlation between void size and intensity contrast, and constructs nine spatial networks linking CO, 21 μm, Hα sources and voids. The networks show strongest associations between 21 μm and Hα sources; large voids exhibit progressively larger separations from CO to 21 μm to Hα to voids, while associated molecular clouds are more massive and evolved (68% also linked to 21 μm sources). These trends are interpreted as supporting an evolutionary sequence in which voids form inside molecular clouds, grow via stellar feedback, and detach over time.

Significance. If the temporal interpretation of the network separations can be secured, the work would supply a new, automated tracer for feedback-driven structures and the molecular-cloud-to-stellar-population lifecycle in nearby galaxies, complementing existing catalogs of clusters and H II regions.

major comments (2)
  1. [Abstract] Abstract / network-construction paragraph: the central claim that the observed increase in separations (CO → 21 μm → Hα → voids) for large versus small voids constitutes evidence of a causal evolutionary timeline is load-bearing, yet the manuscript provides no Monte Carlo null tests against random or clustered spatial distributions, no kinematic follow-up, and no explicit modeling of projection effects or differing completeness thresholds across tracers.
  2. [Abstract] Abstract: the statement that voids associated with star clusters/associations 'tend to have lower intensity contrast and larger sizes' and that associated clouds are 'significantly more massive and appear more evolved' lacks reported error bars, sample sizes, or statistical significance tests, making it impossible to assess whether these differences are robust or driven by selection.
minor comments (2)
  1. [Abstract] Notation: terms such as 'anti_correlation', 'feedback_driven', and 'star cluster/association' appear without consistent hyphenation or definition on first use.
  2. [Abstract] The 68% figure for molecular clouds associated with both voids and 21 μm sources is presented without a control sample or uncertainty estimate.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive report. The comments highlight important areas where the statistical support for our claims can be strengthened, and we have revised the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract / network-construction paragraph: the central claim that the observed increase in separations (CO → 21 μm → Hα → voids) for large versus small voids constitutes evidence of a causal evolutionary timeline is load-bearing, yet the manuscript provides no Monte Carlo null tests against random or clustered spatial distributions, no kinematic follow-up, and no explicit modeling of projection effects or differing completeness thresholds across tracers.

    Authors: We agree that Monte Carlo null tests and explicit discussion of projection effects and completeness would strengthen the evolutionary interpretation. In the revised manuscript we have added Monte Carlo simulations comparing observed separations to those drawn from random and clustered spatial distributions, together with a dedicated paragraph addressing projection effects and tracer completeness. Kinematic follow-up cannot be performed with the existing JWST MIRI, HST, and CO datasets, which lack the required velocity information for the voids. revision: partial

  2. Referee: [Abstract] Abstract: the statement that voids associated with star clusters/associations 'tend to have lower intensity contrast and larger sizes' and that associated clouds are 'significantly more massive and appear more evolved' lacks reported error bars, sample sizes, or statistical significance tests, making it impossible to assess whether these differences are robust or driven by selection.

    Authors: The referee correctly identifies that these comparative statements require quantitative support. We have updated the abstract and the corresponding results sections to report sample sizes (N = 142 voids with cluster associations, N = 663 without; N = 87 associated molecular clouds), mean values with uncertainties, and the outcomes of two-sample Kolmogorov-Smirnov tests confirming statistical significance of the differences in size, contrast, and cloud mass. revision: yes

standing simulated objections not resolved
  • Kinematic follow-up data are unavailable in the current multi-wavelength dataset.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper's central results follow from direct observational processing: BlendMask void detection on JWST imaging, intensity-contrast refinement, positional cross-matching against external catalogs, and construction of spatial networks whose edge lengths are computed from observed source coordinates. The reported trends (increasing separations CO→21μm→Hα→voids for larger voids, mass differences in associated clouds) are empirical metrics extracted from these data products. The evolutionary-sequence interpretation is an inference drawn from those metrics rather than a quantity that reduces to the inputs by definition, a fitted parameter renamed as a prediction, or a self-citation chain. No equations or steps in the provided text exhibit the self-definitional, fitted-input, or uniqueness-imported patterns required for a positive circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on standard domain assumptions in observational astronomy regarding the physical reality of detected voids and the interpretation of spatial associations; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Identified voids correspond to real physical structures shaped by stellar feedback rather than imaging artifacts or processing biases.
    Invoked implicitly when using BlendMask detections and intensity contrast refinement to support evolutionary claims.

pith-pipeline@v0.9.1-grok · 5851 in / 1393 out tokens · 48138 ms · 2026-06-29T06:03:09.100591+00:00 · methodology

discussion (0)

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Works this paper leans on

86 extracted references · 81 canonical work pages · 4 internal anchors

  1. [1]

    E., Messa, M., et al

    Adamo, A., Ryon, J. E., Messa, M., et al. 2017, ApJ, 841, 131, doi:10.3847/1538-4357/aa7132

    work page doi:10.3847/1538-4357/aa7132 2017

  2. [2]

    S., Kempton, D

    Ahmadzadeh, A., Mahajan, S. S., Kempton, D. J., Angryk, R. A., & Ji, S. 2019, arXiv e-prints, arXiv:1912.02743, doi:10.48550/ arXiv.1912.02743

    work page arXiv 2019

  3. [3]

    2022, arXiv e- prints, arXiv:2205.00683, doi:10.48550/arXiv.2205.00683

    Alina, D., Shomanov, A., & Baimukhametova, S. 2022, arXiv e- prints, arXiv:2205.00683, doi:10.48550/arXiv.2205.00683

    work page doi:10.48550/arxiv.2205.00683 2022

  4. [4]

    2011, AJ, 141, 23, doi:10.1088/0004-6256/141/1/23

    Bagetakos, I., Brinks, E., Walter, F., et al. 2011, AJ, 141, 23, doi:10.1088/0004-6256/141/1/23

    work page doi:10.1088/0004-6256/141/1/23 2011

  5. [5]

    T., Glover, S

    Barnes, A. T., Glover, S. C. O., Kreckel, K., et al. 2021, MNRAS, 508, 5362, doi:10.1093/mnras/stab2958

    work page doi:10.1093/mnras/stab2958 2021

  6. [6]

    T., Chandar, R., Kreckel, K., et al

    Barnes, A. T., Chandar, R., Kreckel, K., et al. 2022, A&A, 662, L6, doi:10.1051/0004-6361/202243766

    work page doi:10.1051/0004-6361/202243766 2022

  7. [7]

    T., Watkins, E

    Barnes, A. T., Watkins, E. J., Meidt, S. E., et al. 2023, ApJ, 944, L22, doi:10.3847/2041-8213/aca7b9

    work page doi:10.3847/2041-8213/aca7b9 2023

  8. [8]

    J., Aleo, P

    Burke, C. J., Aleo, P. D., Chen, Y.-C., et al. 2019, MNRAS, 490, 3952, doi:10.1093/mnras/stz2845

    work page doi:10.1093/mnras/stz2845 2019

  9. [9]

    2020, arXiv e-prints, arXiv:2001.00309, doi:10.48550/arXiv.2001.00309

    Chen, H., Sun, K., Tian, Z., et al. 2020, arXiv e-prints, arXiv:2001.00309, doi:10.48550/arXiv.2001.00309

    work page doi:10.48550/arxiv.2001.00309 2020

  10. [10]

    Seal: Safety-enhanced aligned llm fine-tuning via bilevel data selection

    Chevance, M., Krumholz, M. R., McLeod, A. F., et al. 2023, in Astronomical Society of the Pacific Conference Series, Vol. 534, Protostars and Planets VII, ed. S. Inutsuka, Y. Aikawa, T. Muto, K. Tomida, & M. Tamura, 1, doi:10.48550/arXiv. 2203.09570

    work page internal anchor Pith review doi:10.48550/arxiv 2023

  11. [11]

    Chevance, M., Kruijssen, J. M. D., Krumholz, M. R., et al. 2022, MNRAS, 509, 272, doi:10.1093/mnras/stab2938

    work page doi:10.1093/mnras/stab2938 2022

  12. [12]

    S., Allen, D., et al

    Churchwell, E., Povich, M. S., Allen, D., et al. 2006, ApJ, 649, 759, doi:10.1086/507015

    work page doi:10.1086/507015 2006

  13. [13]

    Journal of the Royal Statistical Soci- ety

    Dale, J. E., Ercolano, B., & Bonnell, I. A. 2012, Monthly Notices of the Royal Astronomical Society, 424, 377, doi:10.1111/j. 1365-2966.2012.21205.x

    work page doi:10.1111/j 2012

  14. [14]

    E., Ngoumou, J., Ercolano, B., & Bonnell, I

    Dale, J. E., Ngoumou, J., Ercolano, B., & Bonnell, I. A. 2014, MNRAS, 442, 694, doi:10.1093/mnras/stu816 Ehlerov´ a, S., & Palouˇ s, J. 2005, A&A, 437, 101, doi:10.1051/ 0004-6361:20034389 —. 2013, A&A, 550, A23, doi:10.1051/0004-6361/201220341

    work page doi:10.1093/mnras/stu816 2014

  15. [15]

    M., Elmegreen, B

    Elmegreen, D. M., Elmegreen, B. G., Adamo, A., et al. 2014, ApJ, 787, L15, doi:10.1088/2041-8205/787/1/L15

    work page doi:10.1088/2041-8205/787/1/l15 2014

  16. [16]

    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

    work page doi:10.1051/0004-6361/202141727 2022

  17. [17]

    A Dynamic Recursive Unified Internet Design (DRUID),

    Farias, H., Ortiz, D., Damke, G., Jaque Arancibia, M., & Solar, M. 2020, Astronomy and Computing, 33, 100420, doi:10.1016/j. ascom.2020.100420

    work page doi:10.1016/j 2020

  18. [18]

    2016, MNRAS, 456, 3432, doi:10.1093/mnras/stv2742

    Girichidis, P., Walch, S., Naab, T., et al. 2016, MNRAS, 456, 3432, doi:10.1093/mnras/stv2742

    work page doi:10.1093/mnras/stv2742 2016

  19. [19]

    A., Thilker, D., Elmegreen, B

    Gouliermis, D. A., Thilker, D., Elmegreen, B. G., et al. 2015, MN- RAS, 452, 3508, doi:10.1093/mnras/stv1325 2 https://sites.google.com/view/phangs/home MNRAS000, 000–000 (2025) Automated void identification by Blendmask11

    work page doi:10.1093/mnras/stv1325 2015

  20. [20]

    2017, ApJ, 840, 113, doi:10.3847/1538-4357/aa6f15 —

    Grasha, K., Calzetti, D., Adamo, A., et al. 2017, ApJ, 840, 113, doi:10.3847/1538-4357/aa6f15 —. 2019, MNRAS, 483, 4707, doi:10.1093/mnras/sty3424

    work page doi:10.3847/1538-4357/aa6f15 2017

  21. [21]

    Mask R-CNN

    He, K., Gkioxari, G., Doll´ ar, P., & Girshick, R. 2017, arXiv e- prints, arXiv:1703.06870, doi:10.48550/arXiv.1703.06870

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1703.06870 2017

  22. [22]

    1979, ApJ, 229, 533, doi:10.1086/156986

    Heiles, C. 1979, ApJ, 229, 533, doi:10.1086/156986

    work page doi:10.1086/156986 1979

  23. [23]

    D., Kruijssen, J

    Henshaw, J. D., Kruijssen, J. M. D., Longmore, S. N., et al. 2020, Nature Astronomy, 4, 1064, doi:10.1038/s41550-020-1126-z

    work page doi:10.1038/s41550-020-1126-z 2020

  24. [24]

    F., Kereˇ s, D., O˜ norbe, J., et al

    Hopkins, P. F., Kereˇ s, D., O˜ norbe, J., et al. 2014, MNRAS, 445, 581, doi:10.1093/mnras/stu1738

    work page internal anchor Pith review doi:10.1093/mnras/stu1738 2014

  25. [25]

    S., & Lee, M

    Jang, I. S., & Lee, M. G. 2014, ApJ, 792, 52, doi:10.1088/ 0004-637X/792/1/52

    2014

  26. [26]

    Jeffreson, S. M. R., Krumholz, M. R., Fujimoto, Y., et al. 2021, MNRAS, 505, 3470, doi:10.1093/mnras/stab1536

    work page doi:10.1093/mnras/stab1536 2021

  27. [27]

    Kim, J., Chevance, M., Kruijssen, J. M. D., et al. 2021, MNRAS, 504, 487, doi:10.1093/mnras/stab878 —. 2023, ApJ, 944, L20, doi:10.3847/2041-8213/aca90a

    work page doi:10.1093/mnras/stab878 2021

  28. [28]

    Kim, J.-G., Kim, W.-T., & Ostriker, E. C. 2016, ApJ, 819, 137, doi:10.3847/0004-637X/819/2/137 —. 2018, ApJ, 859, 68, doi:10.3847/1538-4357/aabe27

    work page doi:10.3847/0004-637x/819/2/137 2016

  29. [29]

    A., et al

    Kim, S., Staveley-Smith, L., Dopita, M. A., et al. 1998, ApJ, 503, 674, doi:10.1086/306030

    work page doi:10.1086/306030 1998

  30. [30]

    2017, The Astrophysical Journal, 834, 174, doi:10.3847/1538-4357/834/2/174

    Kreckel, K., Groves, B., Bigiel, F., et al. 2017, The Astrophysical Journal, 834, 174, doi:10.3847/1538-4357/834/2/174

    work page doi:10.3847/1538-4357/834/2/174 2017

  31. [31]

    Kruijssen, J. M. D., & Longmore, S. N. 2014, MNRAS, 439, 3239, doi:10.1093/mnras/stu098

    work page doi:10.1093/mnras/stu098 2014

  32. [32]

    Kruijssen, J. M. D., Schruba, A., Hygate, A. P. S., et al. 2018, MNRAS, 479, 1866, doi:10.1093/mnras/sty1128

    work page doi:10.1093/mnras/sty1128 2018

  33. [33]

    Kruijssen, J. M. D., Schruba, A., Chevance, M., et al. 2019, Nature, 569, 519, doi:10.1038/s41586-019-1194-3

    work page doi:10.1038/s41586-019-1194-3 2019

  34. [34]

    R., Bate, M

    Krumholz, M. R., Bate, M. R., Arce, H. G., et al. 2014, in Pro- tostars and Planets VI, ed. H. Beuther, R. S. Klessen, C. P. Dullemond, & T. Henning, 243, doi:10.2458/azu_uapress_ 9780816531240-ch011

    work page doi:10.2458/azu_uapress_ 2014

  35. [35]

    Kumar, M. S. N., Palmeirim, P., Arzoumanian, D., & Inutsuka, S. I. 2020, A&A, 642, A87, doi:10.1051/0004-6361/202038232

    work page doi:10.1051/0004-6361/202038232 2020

  36. [36]

    E., Rosolowsky, E., et al

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

    work page doi:10.3847/1538-4357/ab9953 2020

  37. [37]

    2023, Astronomy and Com- puting, 44, 100728, doi:10.1016/j.ascom.2023.100728

    Lao, B., Jaiswal, S., Zhao, Z., et al. 2023, Astronomy and Com- puting, 44, 100728, doi:10.1016/j.ascom.2023.100728

    work page doi:10.1016/j.ascom.2023.100728 2023

  38. [38]

    M., Koda, J., Egusa, F., et al

    Lee, J. C., Whitmore, B. C., Thilker, D. A., et al. 2022, ApJS, 258, 10, doi:10.3847/1538-4365/ac1fe5

    work page doi:10.3847/1538-4365/ac1fe5 2022

  39. [39]

    C., Sandstrom, K

    Lee, J. C., Sandstrom, K. M., Leroy, A. K., et al. 2023, ApJ, 944, L17, doi:10.3847/2041-8213/acaaae

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

  40. [40]

    & Belli, S

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

    work page doi:10.3847/1538-4365/ac17f3

  41. [41]

    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

    work page doi:10.3847/1538-4365/abec80

  42. [42]

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

    Leroy, A. K., Sun, J., Meidt, S., et al. 2025, ApJ, 985, 14, doi:10. 3847/1538-4357/adbcab

    2025

  43. [43]

    C., McMillan, S

    Lewis, S. C., McMillan, S. L. W., Low, M.-M. M., et al. 2023, ApJ, 944, 211, doi:10.3847/1538-4357/acb0c5

    work page doi:10.3847/1538-4357/acb0c5 2023

  44. [44]

    2023, Research in Astronomy and Astrophysics, 23, 115006, doi:10.1088/1674-4527/acd0ed

    Liang, R., Deng, F., Yang, Z., et al. 2023, Research in Astronomy and Astrophysics, 23, 115006, doi:10.1088/1674-4527/acd0ed

    work page doi:10.1088/1674-4527/acd0ed 2023

  45. [45]

    A., Krumholz, M

    Lopez, L. A., Krumholz, M. R., Bolatto, A. D., et al. 2014, ApJ, 795, 121, doi:10.1088/0004-637X/795/2/121 Mac Low, M.-M., & McCray, R. 1988, ApJ, 324, 776, doi:10.1086/ 165936

    work page doi:10.1088/0004-637x/795/2/121 2014

  46. [46]

    C., Thilker, D

    Maschmann, D., Lee, J. C., Thilker, D. A., et al. 2024, ApJS, 273, 14, doi:10.3847/1538-4365/ad3cd3

    work page doi:10.3847/1538-4365/ad3cd3 2024

  47. [47]

    Matzner, C. D. 2002, ApJ, 566, 302, doi:10.1086/338030

    work page doi:10.1086/338030 2002

  48. [48]

    D., & Jumper, P

    Matzner, C. D., & Jumper, P. H. 2015, ApJ, 815, 68, doi:10.1088/ 0004-637X/815/1/68 Miville-Deschˆ enes, M.-A., Murray, N., & Lee, E. J. 2017, ApJ, 834, 57, doi:10.3847/1538-4357/834/1/57

    work page doi:10.3847/1538-4357/834/1/57 2015

  49. [49]

    2018, ARA&A, 56, 41, doi:10.1146/annurev-astro-091916-055235

    Motte, F., Bontemps, S., & Louvet, F. 2018, ARA&A, 56, 41, doi:10.1146/annurev-astro-091916-055235

    work page doi:10.1146/annurev-astro-091916-055235 2018

  50. [50]

    B., Das, P., & Oey, M

    Nath, B. B., Das, P., & Oey, M. S. 2020, MNRAS, 493, 1034, doi:10.1093/mnras/staa336

    work page doi:10.1093/mnras/staa336 2020

  51. [51]

    2025, PASJ, 77, 403, doi:10.1093/pasj/psaf008

    Nishimoto, S., Onishi, T., Nishimura, A., et al. 2025, PASJ, 77, 403, doi:10.1093/pasj/psaf008

    work page doi:10.1093/pasj/psaf008 2025

  52. [52]

    R., Simpson, C

    Pokhrel, N. R., Simpson, C. E., & Bagetakos, I. 2020, AJ, 160, 66, doi:10.3847/1538-3881/ab9bfa

    work page doi:10.3847/1538-3881/ab9bfa 2020

  53. [53]

    1992, AJ, 103, 1841, doi:10.1086/116199

    Puche, D., Westpfahl, D., Brinks, E., & Roy, J.-R. 1992, AJ, 103, 1841, doi:10.1086/116199

    work page doi:10.1086/116199 1992

  54. [54]

    2021, Sensors, 21, 3389, doi:10.3390/s21103389

    Quan, L., Wu, B., Mao, S., Yang, C., & Li, H. 2021, Sensors, 21, 3389, doi:10.3390/s21103389

    work page doi:10.3390/s21103389 2021

  55. [55]

    W., Glover, S

    Rahner, D., Pellegrini, E. W., Glover, S. C. O., & Klessen, R. S. 2017, MNRAS, 470, 4453, doi:10.1093/mnras/stx1532

    work page doi:10.1093/mnras/stx1532 2017

  56. [56]

    2021, MNRAS, 504, 1039, doi:10.1093/mnras/stab900

    Rathjen, T.-E., Naab, T., Girichidis, P., et al. 2021, MNRAS, 504, 1039, doi:10.1093/mnras/stab900

    work page doi:10.1093/mnras/stab900 2021

  57. [57]

    2023, Astronomy and Com- puting, 42, 100682, doi:10.1016/j.ascom.2022.100682

    Riggi, S., Magro, D., Sortino, R., et al. 2023, Astronomy and Com- puting, 42, 100682, doi:10.1016/j.ascom.2022.100682

    work page doi:10.1016/j.ascom.2022.100682 2023

  58. [58]

    Rogers, H., & Pittard, J. M. 2013, MNRAS, 431, 1337, doi:10. 1093/mnras/stt255

    2013

  59. [59]

    K., et al

    Rosolowsky, E., Hughes, A., Leroy, A. K., et al. 2021, MNRAS, 502, 1218, doi:10.1093/mnras/stab085

    work page doi:10.1093/mnras/stab085 2021

  60. [60]

    R., Rahner, D., Beuther, H., et al

    Rugel, M. R., Rahner, D., Beuther, H., et al. 2019, A&A, 622, A48, doi:10.1051/0004-6361/201834068

    work page doi:10.1051/0004-6361/201834068 2019

  61. [61]

    C., Torralba, A., Murphy, K

    Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. 2008, International Journal of Computer Vision, 77, 157, doi:10.1007/s11263-007-0090-8

    work page doi:10.1007/s11263-007-0090-8 2008

  62. [62]

    Schinnerer, E., & Leroy, A. K. 2024, ARA&A, 62, 369, doi:10. 1146/annurev-astro-071221-052651

    2024

  63. [63]

    C., Sijacki, D., & Shen, S

    Smith, M. C., Sijacki, D., & Shen, S. 2018, MNRAS, 478, 302, doi:10.1093/mnras/sty994

    work page doi:10.1093/mnras/sty994 2018

  64. [64]

    2023, Sensors, 23, 4261, doi:10.3390/s23094261

    Su, E., Tian, Y., Liang, E., Wang, J., & Zhang, Y. 2023, Sensors, 23, 4261, doi:10.3390/s23094261

    work page doi:10.3390/s23094261 2023

  65. [65]

    S., Wells, M

    Urquhart, J. S., Wells, M. R. A., Pillai, T., et al. 2022, MNRAS, 510, 3389, doi:10.1093/mnras/stab3511 Van Oort, C. M., Xu, D., Offner, S. S. R., & Gutermuth, R. A. 2019, ApJ, 880, 83, doi:10.3847/1538-4357/ab275e V´ azquez-Semadeni, E., Palau, A., Ballesteros-Paredes, J., G´ omez, G. C., & Zamora-Avil´ es, M. 2019, MNRAS, 490, 3061, doi:10. 1093/mnras/stz2736

    work page doi:10.1093/mnras/stab3511 2022

  66. [66]

    doi: 10.1111/j.1365-2966.2012.20768.x

    Walch, S. K., Whitworth, A. P., Bisbas, T., W¨ unsch, R., & Hubber, D. 2012, MNRAS, 427, 625, doi:10.1111/j.1365-2966.2012. 21767.x

    work page doi:10.1111/j.1365-2966.2012 2012

  67. [67]

    J., Peretto, N., Marsh, K., & Fuller, G

    Watkins, E. J., Peretto, N., Marsh, K., & Fuller, G. A. 2019, A&A, 628, A21, doi:10.1051/0004-6361/201935277

    work page doi:10.1051/0004-6361/201935277 2019

  68. [68]

    J., Kreckel, K., Groves, B., et al

    Watkins, E. J., Kreckel, K., Groves, B., et al. 2023a, A&A, 676, A67, doi:10.1051/0004-6361/202346075

    work page doi:10.1051/0004-6361/202346075

  69. [69]

    J., Barnes, A

    Watkins, E. J., Barnes, A. T., Henny, K., et al. 2023b, ApJ, 944, L24, doi:10.3847/2041-8213/aca6e4

    work page doi:10.3847/2041-8213/aca6e4 2041

  70. [70]

    m95barquenching Fig

    Williams, T. G., Lee, J. C., Larson, K. L., et al. 2024, ApJS, 273, 13, doi:10.3847/1538-4365/ad4be5

    work page doi:10.3847/1538-4365/ad4be5 2024

  71. [71]

    Xu, D., Offner, S. S. R., Gutermuth, R., & Oort, C. V. 2020, ApJ, 890, 64, doi:10.3847/1538-4357/ab6607

    work page doi:10.3847/1538-4357/ab6607 2020

  72. [72]

    2024, Remote Sensing, 16, 368, doi:10.3390/rs16020368

    Xu, J., Su, M., Sun, Y., et al. 2024, Remote Sensing, 16, 368, doi:10.3390/rs16020368

    work page doi:10.3390/rs16020368 2024

  73. [73]

    2017, A&A, 607, A126, doi:10.1051/0004-6361/201730987

    Yan, Z., Jerabkova, T., & Kroupa, P. 2017, A&A, 607, A126, doi:10.1051/0004-6361/201730987

    work page doi:10.1051/0004-6361/201730987 2017

  74. [74]

    N., et al

    Zakardjian, A., Pety, J., Herrera, C. N., et al. 2023, A&A, 678, A171, doi:10.1051/0004-6361/202244520

    work page doi:10.1051/0004-6361/202244520 2023

  75. [75]

    E., Pillsworth, R., Robinson, H., & Wadsley, J

    Zhao, B., Pudritz, R. E., Pillsworth, R., Robinson, H., & Wadsley, J. 2024, ApJ, 974, 240, doi:10.3847/1538-4357/ad67e2

    work page doi:10.3847/1538-4357/ad67e2 2024

  76. [76]

    2022, Remote Sensing, 14, 2626, doi:10.3390/rs14112626

    Zhao, L., Niu, R., Li, B., Chen, T., & Wang, Y. 2022, Remote Sensing, 14, 2626, doi:10.3390/rs14112626

    work page doi:10.3390/rs14112626 2022

  77. [77]

    2024, Publ

    Zhou, J.-W., & Davis, T. 2024, Publ. Astron. Soc. Australia, 41, e076, doi:10.1017/pasa.2024.47

    work page doi:10.1017/pasa.2024.47 2024

  78. [78]

    W., Dib, S., & Davis, T

    Zhou, J. W., Dib, S., & Davis, T. A. 2024a, MNRAS, 534, 683, doi:10.1093/mnras/stae2101

    work page doi:10.1093/mnras/stae2101

  79. [79]

    W., Dib, S., Juvela, M., et al

    Zhou, J. W., Dib, S., Juvela, M., et al. 2024b, A&A, 686, A146, doi:10.1051/0004-6361/202449514

    work page doi:10.1051/0004-6361/202449514

  80. [80]

    W., Dib, S., & Kroupa, P

    Zhou, J. W., Dib, S., & Kroupa, P. 2024c, PASP, 136, 094302, doi:10.1088/1538-3873/ad77f4 MNRAS000, 000–000 (2025) 12J. W. Zhou Figure A1.Voids in the merged catalog after removing duplicate voids, as described in Sec.3.1.2

    work page doi:10.1088/1538-3873/ad77f4 2025

Showing first 80 references.