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The paper presents BWFields, the first automated method to identify and analyze enclosed molecular bubbles directly in spectral-line data cubes, by accumulating cavity signatures into a bubble-weight field and segmenting its interiors as cl

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BWFields automatically finds and analyzes 93 molecular bubble candidates in MWISP 13CO data toward G17, using a PPV weight field.

T0 review reviewed 2026-08-06 challenge →

load-bearing objection Genuinely new weight-field construction for finding molecular bubbles in CO cubes, honest benchmarks, shipped code—but the priority claim is unearned and the 93-candidate catalog lacks completeness and false-positive assessment. the 5 major comments →

arxiv 2608.01052 v1 pith:D3NDC6NC submitted 2026-08-02 astro-ph.GA astro-ph.IM

Investigations of MWISP Bubbles: Identification and Analysis of Enclosed Molecular Bubbles by Weight Fields

Yu Jiang , Xuepeng Chen , Ji Yang , Ke Wang , Zhibo Jiang , Sheng Zheng , Yan Sun , Yang Su
show 2 more authors
Min Fang Qing-Zeng Yan
This is my paper
classification astro-ph.GA astro-ph.IM
keywords molecular bubblesstellar feedbackposition-position-velocity spacespectral-line surveysbubble-weight fieldMWISPcavity detectionCO observations
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

Molecular bubbles — cavities blown by massive stars into the surrounding gas — are usually found by eye, with an astronomer scanning emission maps and velocity channels for shell-like structures. BWFields tries to replace that with an objective, automated pipeline that works inside the three-dimensional position–position–velocity (PPV) space of a CO survey cube. Its central move is to score every voxel by accumulated evidence of lying inside a cavity, gathered across many signal-to-noise thresholds and velocity-integration widths, producing a bubble-weight field $W_{l,b,v}$; contiguous high-weight regions are then segmented as candidates, measured for shell radius, thickness, and symmetry, and diagnosed for expansion-like velocity structure. Applied to the MWISP $^{13}$CO cube of the G17 region, the method recovers known bubbles (N4, N19, the M16 surroundings) and finds 93 candidates, including two line-of-sight-overlapping shells that separate only in velocity and a candidate with no infrared or radio counterpart. If it works as claimed, it makes uniform, survey-scale bubble catalogs feasible, turning stellar-feedback statistics from a manual exercise into a quantitative one.

Core claim

Bubbles are best detected as a score, not a shape. Every PPV voxel accumulates cavity-interior evidence across signal-to-noise tiers and velocity-integration widths, weighted by $\mathrm{SNR}_i/\Delta V_j$ to favor bright, kinematically narrow cavities. Contiguous high-score regions become candidates, linked to parent clouds via PPV overlap. Shell radius and thickness come from double-Gaussian fits to radial profiles; shell geometry from the emission-defined intensity skeleton; kinematics from PV slices summarized by expansion velocity and the turbulence-normalized significance $S_{\mathrm{exp}}$. Claimed as the first PPV-native automated pipeline, BWFields finds 93 candidates in the MWISP G

What carries the argument

The bubble-weight field $W_{l,b,v}=\sum_{i,j,k}(\mathrm{SNR}_i/\Delta V_j)\,\mathbb{1}_{i}^{B(j,k)}(l,b,v)$ accumulates cavity-interior evidence, found by morphological hole-filling in each slab-integrated projection, across SNR tiers and velocity scales. The weighting $\mathrm{SNR}_i/\Delta V_j$ is load-bearing: real bubbles are high-contrast and kinematically coherent, so relative weights mark the most probable interiors. The pipeline then chains FacetClumps segmentation, centroid-constrained ellipse fitting, double-Gaussian radial profiles, graph-based ridge tracing into an intensity skeleton, and PV diagnostics with sign-coherence fractions and $S_{\mathrm{exp}}$.

Load-bearing premise

BWFields can only see a bubble whose interior appears as a closed, hole-fillable brightness depression in at least one velocity-slab projection at some signal-to-noise level; shells that are partially open, fragmented, or too deformed to close in projection will be underweighted, split, or missed.

What would settle it

Take the MWISP 13CO cube of the G17 region, inject synthetic expanding shells of known radius, expansion velocity, and shell-opening angle (from fully closed down to half-open), and measure how BWFields' recall varies with opening angle and contrast at fixed noise. The enclosure assumption predicts a sharp drop in recovery once a projected shell stops closing under hole-filling; a companion run on pure noise would fix the contamination rate at the adopted BubWeight threshold — two numbers the paper itself does not yet report.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • A full Galactic-plane run of BWFields on MWISP becomes straightforward: the pipeline is released as an open-source Python package, and the authors state a companion paper will deliver the plane-wide bubble-candidate catalog.
  • Overlapping or confused shells, which look like single structures in integrated maps, can be separated automatically when they occupy different velocity ranges — demonstrated by N74 and N75, coincident in projection but about 20 km s$^{-1}$ apart in systemic velocity.
  • Candidates come with quantitative, reproducible morphology (radius, thickness, symmetry score) and kinematics (expansion velocity, P/N/D classes, $S_{\rm exp}$), so population statistics can be built without manual channel-by-channel inspection.
  • The method finds candidates invisible at other wavelengths (Bub B has no infrared or ionized-gas counterpart), so CO-only detection can enlarge feedback surveys beyond infrared-bubble catalogs, with multiwavelength follow-up applied selectively.
  • Because identification only requires a coherent cavity signature in PPV space, the pipeline is designed to transfer across tracers and resolutions, as long as a bubble exhibits a cavity that closes in projection.

Where Pith is reading between the lines

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

  • The SNR-tier × velocity-scale accumulation is a generic template: any survey seeking embedded voids or shells in a 3D scalar field — H I supershells, outflow cavities, chemically distinct regions — could reuse the hole-filling-plus-weighting construction without the bubble-specific machinery.
  • A calibration experiment the paper does not run: injecting synthetic shells of known radius, expansion velocity, and shell-opening angle into the G17 cube would map the completeness boundary implied by the enclosure assumption, yielding the minimum detectable expansion for a shell of given size and ambient linewidth.
  • The velocity separation of N74 and N75 suggests that some 'bubble complexes' in integrated-light catalogs are projection superpositions; if common, automated PPV identification will revise association statistics between bubbles, H II regions, and host clouds toward lower physical merger rates.
  • Bub B-type candidates — CO cavities without infrared or radio counterparts — offer a direct test of the feedback interpretation: if they are genuine bubbles they should appear in deeper continuum or recombination-line data; if not, they measure the false-positive rate of morphology-only identification.
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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

5 major / 6 minor

Summary. The paper presents BWFields, a spectral-line data cube algorithm for identifying molecular bubbles as enclosed cavities in position–position–velocity (PPV) space. A bubble-weight field W_{l,b,v} (Eq. 1) is accumulated from morphologically detected holes in velocity-slab integrated maps at multiple SNR tiers and slab widths; the field is segmented with FacetClumps, merged, fit with ellipses, and analyzed with radial intensity profiles and PV slices. The pipeline returns geometric and kinematic descriptors (R, T, S_sym, v_exp, S_exp) for each candidate. Applied to MWISP 13CO data toward G17, it yields 93 candidates; three known bubbles (N4, N74, N75) are used as benchmarks. The paper claims the first PPV-native automated method of this kind and provides public code.

Significance. If the method is as reliable as claimed, this is a valuable contribution: it directly attacks the 3D identification problem and produces quantifiable shell properties over large surveys. Strengths include a detailed pipeline description, public code repository, multiwavelength validation of three benchmark cases, and a candid limitations section (Sec. 3.7). The importance is high because existing automated searches are mostly 2D or projection-based. However, the current evidence does not support the scalable/objective claim at the level required for a methods paper: there is no blind test, no completeness or false-positive measurement, no sensitivity analysis of the Table 1 parameter set, and at least one benchmark classification is internally inconsistent. The method's potential is real, but the demonstration remains conditional.

major comments (5)
  1. [§3.7, §4.2, Table C.1] The 93-candidate catalog has no external validation within the demonstrated field. The three benchmark bubbles (Appendix A) are located outside G17 (N4 at l≈11.9; N74/N75 at l≈38.9), and no G17 candidate is checked against an independent catalog (e.g., Churchwell et al. 2006 bubbles or the M16/N19 structures). Because §3.7 concedes that partially open/deformed shells may be underweighted or missed, the method's completeness for real G17 bubbles is unknown. Add an injection-recovery experiment into the MWISP 13CO data (vary radius, thickness, SNR, expansion velocity, opening angle) and report recovery fraction, false-positive rate, and dependence on the enclosure assumption; also run a null test on velocity-scrambled or noise-only data.
  2. [§4.1, Table 1] The Table 1 parameters are free and are described as being 'supported' by the three Appendix A benchmarks, but no sensitivity analysis is given. The list of 93 candidates is potentially a strong function of Threshold, SliceDV, BubWeight, BubSizeLBV, MergeOR, SymScore, and ExpSign. Show how the candidate count and key physical parameter distributions change when each parameter is varied over a reasonable grid, or demonstrate robustness in a defined neighborhood. This is load-bearing because the objectivity/scalability claim rests on reproducible detections, not a manually tuned configuration.
  3. [§3.2, §5, Abstract] The Abstract's claim that BWFields is 'automated and objective' conflicts with explicit human-in-the-loop steps in the pipeline. Section 3.2 states that 'channel maps are inspected to confirm that the cavity persists', and Section 5 says that interpreting individual candidates requires additional multiwavelength context. This makes the pipeline semi-automated as presented. Specify the inspection protocol, report how many candidates were added/removed/changed by inspection, provide inter-operator agreement, or soften the objectivity claim accordingly.
  4. [Appendix A, Figure A.2(f), Table C.1] The N74 benchmark is internally inconsistent. Figure A.2(f) prints 'Sexp(light black)=1.20, P' while the same panel reports P=0.389, N=0, D=0.611; Section 5 describes N74 as 'D'; Table C.1 lists Sexp=0.21 for N74. The final classification rule and the exact quantity reported in Table C.1 must be defined. Because N74 is one of only three validation cases, the reader cannot currently assess the method's performance on this case.
  5. [§3.6, Eqs. (16)–(18)] S_exp is called a 'turbulence-normalised expansion significance', but it is not calibrated as a statistical significance: no null distribution, false-positive rate, or uncertainty is provided for the threshold ExpSign=1. The name and the high/low-S_exp split in Figure 10 imply a calibrated test. Either calibrate S_exp with synthetic data (e.g., from the injection-recovery experiment) or rename it as a relative expansion coefficient and remove the significance language throughout.
minor comments (6)
  1. [Abstract, §1] The 'first PPV-native method' claim would be strengthened by a focused comparison with Xu & Offner 2017, Xu et al. 2020, Nishimoto et al. 2025, and Zhou & Han 2026, explicitly defining the sense in which those methods are not PPV-native.
  2. [Table C.1] The illustrative catalog mixes benchmark candidates from outside the G17 field (N4, N74, N75) with G17 candidate rows. Separate the benchmark table from the G17 catalog or add a field flag so the catalog is unambiguous.
  3. [§3.4, Eqs. (12)–(13)] Equation (12) defines a cost that is minimized by an MST, while Eq. (13) is maximized for skeleton selection. Clarify the sign convention and state whether the selected path is the minimum-cost path, the maximum-brightness path, or a combination.
  4. [§3.3] The sentence 'The total number of cuts is half the ellipse circumference' needs units and pixel treatment; it is unclear how a non-integer number of cuts is handled.
  5. [Figures 3 and 10] Figure captions should state the contour levels used (e.g., whether the contours correspond to a fixed multiple of the weight threshold or to a percentile).
  6. [Table C.1] The full machine-readable catalog should accompany the paper rather than only an illustrative excerpt; otherwise the 93-candidate claim cannot be independently checked.

Circularity Check

0 steps flagged

No significant circularity: BWFields is an operational method whose outputs are transparently defined from its inputs; no prediction reduces to a fit or to a self-citation chain.

full rationale

BWFields is a method paper rather than a derivation that predicts a physical quantity from independent inputs. The chain is explicit: Eq. (1) defines the bubble-weight field W as a weighted sum of hole-filled cavity masks from slab-integrated emission; Section 3.2 segments W with FacetClumps into weight-clumps; Sections 3.3–3.6 derive radii, intensity skeletons, and PV-based diagnostics from the same cube. Every output is labeled operationally: candidates are 'weight-clumps', kinematics are 'expansion-like', and Section 3.7 states that the method targets enclosed cavities and that the kinematic diagnostics are not a unique confirmation of physical expansion. No equation produces an output that is secretly identical to its own input by construction, and no fitted parameter is renamed as a prediction. The Table 1 thresholds are said to be 'supported by the benchmark cases' in Appendix A, but the benchmarks are not 'predicted' from those thresholds; the absence of injection-recovery or completeness tests is a real validation gap, not a circularity. The confidence metric in Table C.1 is transparently defined as the mean bubble-weight within the weight-clump, so it is a ranking score by construction rather than an independent confirmation. Self-citations (FacetClumps, DPConCFil, Jiang et al. 2025a/b) are code-reproduced algorithmic components, not a self-authorized uniqueness theorem that forces the choice of W or S_exp. The 'first PPV-native method' claim is a literature statement, not an output of the pipeline. No circular step meets the required quoted-reduction standard.

Axiom & Free-Parameter Ledger

8 free parameters · 6 axioms · 0 invented entities

No new physical entities are introduced. The framework's novelty lies in computational measures (weight field, Sexp), which are constructions over observed data, not independent physical postulates. The central detection pipeline depends on seven hand-set thresholds plus an unspecified SNR-tier discretization, calibrated qualitatively on benchmark bubbles rather than derived or optimized.

free parameters (8)
  • Threshold multiplier = 5 x RMS = 1.1 K
    Sets the minimum intensity tier in Eq. (1); chosen by hand to exclude faint noise, no formal optimization.
  • SliceDV = 12 pixels (~2 km/s)
    Maximum velocity slab width for the weight-field aggregation; controls which velocity-integration scales contribute to W.
  • BubWeight = 4
    Minimum cumulative bubble-weight for retaining a weight-clump candidate; hand-set to suppress noise detections.
  • BubSizeLBV = [16, 5] pixels/channels
    Minimum spatial and velocity extent of a weight-clump; hand-set and directly affects candidate counts.
  • MergeOR = 0.4
    Footprint overlap ratio threshold for merging candidate weight-clumps; hand-set, no sensitivity test.
  • SymScore = 0.8
    Minimum radial-profile symmetry score for reliable shell characterization; 'supported' by benchmark cases in Appendix A, i.e., tuned.
  • ExpSign = 1.0
    Threshold separating high and low turbulence-normalised expansion significance; hand-set.
  • SNR tier discretization = not specified
    Eq. (1) sums over SNR tiers from Threshold to maximum SNR, but the number and spacing of tiers is not documented; an unstated free choice.
axioms (6)
  • domain assumption Morphological hole-filling in 2D integrated projections reliably identifies cavity interiors
    Used in Section 3.1 to define B^(j,k)_i; assumes closed brightness depressions correspond to physical cavities and ignores projection filling from unrelated line-of-sight gas.
  • domain assumption The weighting factor SNR_i/DeltaV_j is a proxy for bubble contrast and kinematic coherence
    Stated in Section 3.1 without derivation; biases the field toward high-SNR, narrow-velocity cavities and may miss diffuse shells.
  • domain assumption 13CO traces the molecular shells of feedback-driven bubbles in the G17 region
    The entire application rests on 13CO emissivity tracing the relevant cool gas; no opacity or excitation correction is applied.
  • ad hoc to paper FacetClumps segmentation of the weight field yields contiguous bubble interiors that merge into physical cavities
    FacetClumps was developed by the same group; its defaults are used without re-tuning or validation on simulations.
  • standard math MST-based graph search in unwrapped coordinates finds the brightest continuous shell skeleton
    Graph theory edge-cost formulation in Eqs. (12)-(13); standard but the choice of boundary node pairs and seam enforcement is heuristic.
  • domain assumption The quadratic coefficient c in V(x)=a+bx+cx^2 and the central-environment velocity difference capture expansion-like kinematics
    Eqs. (16)-(18) define Sexp; no model comparison shows this separates expansion from rotation, shear, or gradients. The paper itself warns that PV signatures are not unique.

reviewed 2026-08-06 · how reviews work

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Cite this review

Pith. "Pith review of Investigations of MWISP Bubbles: Identification and Analysis of Enclosed Molecular Bubbles by Weight Fields." pith.science (2026). https://pith.science/paper/D3NDC6NC

@misc{pith2026260801052,
  author       = {Pith},
  title        = {Pith review of: Investigations of MWISP Bubbles: Identification and Analysis of Enclosed Molecular Bubbles by Weight Fields},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D3NDC6NC}},
  note         = {Machine review of arXiv:2608.01052}
}
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abstract

Molecular bubbles are widely used as tracers of stellar feedback; yet, their identification in spectral-line surveys remains challenging because both cavity morphology and kinematic structure must be assessed consistently in position--position--velocity (PPV) space. We present the Bubble-Weight Fields (BWFields) framework, a PPV-based method that for the first time enables the automated and objective identification and analysis of enclosed molecular bubbles directly from spectral-line data cubes. BWFields constructs a bubble-weight field, $W_{l,b,v}$, which encodes cumulative evidence for cavity interiors by aggregating topological signatures across multiple signal-to-noise tiers and velocity-integration scales. Contiguous cavity interiors are segmented as weight-clumps and associated with surrounding molecular gas, linking candidate bubbles to the structure of their host clouds. Shell morphology is characterized using radial intensity profiles and emission-defined intensity skeletons, which capture the shell geometry as traced by the observed emission. Bubble kinematics are quantified using azimuthally sampled position-velocity (PV) diagnostics, along with a turbulence-normalized expansion significance, which serves as a direct measure of the expansion-like velocity organisation. Applied to MWISP $^{13}$CO observations of the G17 region, BWFields identifies a population of bubble candidates with a broad range of morphologies and velocity structures in complex environments. BWFields establishes a scalable and physically interpretable framework for molecular-bubble studies in large surveys, enabling systematic investigations of stellar feedback in the Galactic interstellar medium.

Figures

Figures reproduced from arXiv: 2608.01052 by Ji Yang, Ke Wang, Min Fang, Qing-Zeng Yan, Sheng Zheng, Xuepeng Chen, Yang Su, Yan Sun, Yu Jiang, Zhibo Jiang.

Figure 1
Figure 1. Figure 1: An illustrative view of the G17 region used to demonstrate BWFields. The background shows a three-color composite from the MWISP survey, with C18O in red, 13CO in green, and 12CO in blue, integrated over velocities from −10 to 50 km s−1 . Prominent ionized regions and infrared bubbles are labeled, including M16 H ii and N19. O-, B-, and A-type stars are overlaid as red, gold, and violet star symbols, respe… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the BWFields pipeline for identifying molecular bubble candidates. A bubble-weight field Wl,b,v is constructed from slab-integrated maps to highlight cavity interiors across multiple signal-to-noise tiers and velocity scales. Weight-clumps are segmented and linked to the surrounding molecular gas, with a fitted ellipse defining the initial geometric reference. Based on this reference, shell mor… view at source ↗
Figure 3
Figure 3. Figure 3: Spatial and morphological distributions of molecular gas and identified bubble structures in the EoC region. (a) Integrated intensity map of the molecular emission. Colored points and contours show the positions and boundaries of bubble regions extracted from the bubble-weight cube Wl,b,v. The blue-violet dot and circle indicate the position and radius of bubble N19. (b) Velocity-integrated bubble-weight m… view at source ↗
Figure 4
Figure 4. Figure 4: Three orthogonal projections of the N19–A weight-clump identified in the bubble-weight cube Wl,b,v by FacetClumps. Left: integrated weight in the longitude–latitude plane, showing the projected cavity morphology. Middle and right: latitude￾and longitude-integrated weight maps. The green marker indicates the weighted center of mass, with its coordinates and velocity range annotated. Colors encode the bubble… view at source ↗
Figure 5
Figure 5. Figure 5: Gas distribution and profile-extraction geometry for N19–A. (a) Velocity-integrated molecular emission. The green contour and dot mark the boundary and centroid of the weight-clump, and the cyan ellipse shows the centroid-constrained fitted inner boundary. Gas clumps directly associated with the weight-clump are indicated by red stars and short line segments, while a representative clump boundary is outlin… view at source ↗
Figure 6
Figure 6. Figure 6: Channel maps of N19–A across consecutive velocity intervals. Each panel shows emission integrated over a uniform velocity width, revealing the evolution of gas morphology with velocity. The gas exhibits a shell-like structure, with enhanced emission along the shell and a comparatively depleted interior that persists across multiple channels. The cyan ellipse marks the centroid-constrained fitted cavity bou… view at source ↗
Figure 7
Figure 7. Figure 7: Radial sampling scheme used for the intensity-profile and PV analyses. (a) Radial sampling scheme for the first intensity-profile analysis. Yellow rays represent radial cuts passing through the intensity-weighted centroid of weight-clumps, intersecting reference points along the fitted cavity contour. Intensity profiles are extracted within the gas-clump mask shown in [PITH_FULL_IMAGE:figures/full_fig_p00… view at source ↗
Figure 8
Figure 8. Figure 8: Azimuth-radius unwrapping and intensity-based skeleton extraction for a representative molecular bubble. (a) Definition of the unwrapping annulus centerd on the intensity-weighted centroid of the weight-clump. The unwrapping central line is defined by a reference ellipse whose semi-major axis equals the characteristic bubble radius, and whose axis ratio is fixed to that of the fitted ellipse shown in [PIT… view at source ↗
Figure 9
Figure 9. Figure 9: PV analysis for N19–A. (a) PV diagram along the radial cut showing the largest deviation of the central velocity from the systemic value; contours trace 13CO emission, and the green dot marks the bubble centroid. (b) Intensity-weighted velocity profile from panel (a); the black curve is the mean velocity, the shaded region is the velocity dispersion, and red/blue dash-dotted curves are the mean redshifted/… view at source ↗
Figure 10
Figure 10. Figure 10: Bubble candidates identified by BWFields in the G17 region. A total of 93 candidates are detected. The background shows the velocity-integrated molecular emission, while colored contours trace the fitted intensity skeleton of each candidate. Central symbols mark the centroids of the fitted ellipses. Symbol shapes encode the PV sign-consistency class and whether the symmetry parameter Ssym falls below the … view at source ↗

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Works this paper leans on

59 extracted references · 25 canonical work pages · 2 internal anchors

  1. [1]

    2013, ApJ, 770, 1, doi: 10.1088/0004-637X/770/1/1

    Arvidsson, K. 2013, ApJ, 770, 1, doi: 10.1088/0004-637X/770/1/1

    work page doi:10.1088/0004-637x/770/1/1 2013

  2. [2]

    D., Bania, T

    Anderson, L. D., Bania, T. M., Balser, D. S., et al. 2014, ApJS, 212, 1, doi: 10.1088/0067-0049/212/1/1

    doi:10.1088/0067-0049/212/1/1 2014

  3. [3]

    G., Borkin, M

    Arce, H. G., Borkin, M. A., Goodman, A. A., Pineda, J. E., & Beaumont, C. N. 2011, ApJ, 742, 105, doi: 10.1088/0004-637X/742/2/105 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-...

    doi:10.1088/0004-637x/742/2/105 2011

  4. [4]

    T., Watkins, E

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

    doi:10.3847/2041-8213/aca7b9 2023

  5. [5]

    N., Goodman, A

    Beaumont, C. N., Goodman, A. A., Kendrew, S., Williams, J. P., & Simpson, R. 2014, ApJS, 214, 3, doi: 10.1088/0067-0049/214/1/3

    work page doi:10.1088/0067-0049/214/1/3 2014

  6. [6]

    N., & Williams, J

    Beaumont, C. N., & Williams, J. P. 2010, ApJ, 709, 791, doi: 10.1088/0004-637X/709/2/791 16Jiang et al

    doi:10.1088/0004-637x/709/2/791 2010

  7. [7]

    H., White, R

    Becker, R. H., White, R. L., & Helfand, D. J. 2006, in American Institute of Physics Conference Series, Vol. 840, The Transient Milky Way: A Perspective for MIRAX, ed. F. D’Amico, J. Braga, & R. E. Rothschild (AIP), 102–104, doi: 10.1063/1.2216612

    doi:10.1063/1.2216612 2006

  8. [8]

    A., Churchwell, E., Babler, B

    Benjamin, R. A., Churchwell, E., Babler, B. L., et al. 2003, PASP, 115, 953, doi: 10.1086/376696

    doi:10.1086/376696 2003

  9. [9]

    2016, A&A, 595, A32, doi: 10.1051/0004-6361/201629143

    Beuther, H., Bihr, S., Rugel, M., et al. 2016, A&A, 595, A32, doi: 10.1051/0004-6361/201629143

    doi:10.1051/0004-6361/201629143 2016

  10. [10]

    J., Noriega-Crespo, A., Mizuno, D

    Carey, S. J., Noriega-Crespo, A., Mizuno, D. R., et al. 2009, PASP, 121, 76, doi: 10.1086/596581

    doi:10.1086/596581 2009

  11. [11]

    S., Allen, D., et al

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

    doi:10.1086/507015 2006

  12. [12]

    F., Povich, M

    Churchwell, E., Watson, D. F., Povich, M. S., et al. 2007, ApJ, 670, 428, doi: 10.1086/521646

    doi:10.1086/521646 2007

  13. [13]

    M., Vider, S., Schneider, N., et al

    Dannhauer, S. M., Vider, S., Schneider, N., et al. 2025, A&A, 703, A197, doi: 10.1051/0004-6361/202556159

    work page doi:10.1051/0004-6361/202556159 2025

  14. [14]

    D., et al

    Deharveng, L., Schuller, F., Anderson, L. D., et al. 2010, A&A, 523, A6, doi: 10.1051/0004-6361/201014422

    doi:10.1051/0004-6361/201014422 2010

  15. [15]

    G., Hora, J

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

    doi:10.1086/422843 2004

  16. [16]

    D., Camilo, F., et al

    Goedhart, S., Cotton, W. D., Camilo, F., et al. 2024, MNRAS, 531, 649, doi: 10.1093/mnras/stae1166

    doi:10.1093/mnras/stae1166 2024

  17. [17]

    A., & Heyer, M

    Gutermuth, R. A., & Heyer, M. 2020, MIPSGAL 24 micron

    work page 2020

  18. [18]

    Archive, NASA IPAC DataSet, IRSA259, doi: 10.26131/IRSA259

    work page doi:10.26131/irsa259

  19. [19]

    J., & Schult, D

    Hagberg, A., Swart, P. J., & Schult, D. A. 2008, Proceedings of the 7th Python in Science Conference (SciPy2008). https://www.osti.gov/biblio/960616

    work page 2008

  20. [20]

    J., Scowen, P

    Hester, J. J., Scowen, P. A., Sankrit, R., et al. 1996, AJ, 111, 2349, doi: 10.1086/117968

    work page doi:10.1086/117968 1996

  21. [21]

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

    doi:10.1146/annurev-astro-082214-122324 2015

  22. [22]

    2012, A&A, 542, A114, doi: 10.1051/0004-6361/201219009

    Hill, T., Motte, F., Didelon, P., et al. 2012, A&A, 542, A114, doi: 10.1051/0004-6361/201219009

    work page doi:10.1051/0004-6361/201219009 2012

  23. [23]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    doi:10.1109/mcse.2007.55 2007

  24. [24]

    2023, FacetClumps: A Facet-based Molecular Clump Detection Algorithm, v0.0.4, Zenodo, doi: 10.5281/zenodo.7991006 —

    Jiang, Y. 2023, FacetClumps: A Facet-based Molecular Clump Detection Algorithm, v0.0.4, Zenodo, doi: 10.5281/zenodo.7991006 —. 2024, DPConCFil: A Collection of Filament Identification and Analysis Algorithms, Zenodo, doi: 10.5281/zenodo.13853675

    work page doi:10.5281/zenodo.7991006 2023

  25. [25]

    2026, BWFields: Identification and Analysis of Enclosed Molecular Bubbles by Weight Fields, BWFields V0.0.2, Zenodo, doi: 10.5281/zenodo.20621543

    Jiang, Y. 2026, BWFields: Identification and Analysis of Enclosed Molecular Bubbles by Weight Fields, BWFields V0.0.2, Zenodo, doi: 10.5281/zenodo.20621543

    work page doi:10.5281/zenodo.20621543 2026

  26. [26]

    2023, ApJS, 267, 32, doi: 10.3847/1538-4365/acda89

    Jiang, Y., Chen, Z., Zheng, S., et al. 2023, ApJS, 267, 32, doi: 10.3847/1538-4365/acda89

    work page doi:10.3847/1538-4365/acda89 2023

  27. [27]

    2025a, ApJS, 280, 75, doi: 10.3847/1538-4365/ae00b9

    Jiang, Y., Yan, Q.-Z., Yang, J., et al. 2025a, ApJS, 280, 75, doi: 10.3847/1538-4365/ae00b9

    work page doi:10.3847/1538-4365/ae00b9

  28. [28]

    2025b, ApJS, 276, 27, doi: 10.3847/1538-4365/ad91a8

    Jiang, Y., Chen, X., Zheng, S., et al. 2025b, ApJS, 276, 27, doi: 10.3847/1538-4365/ad91a8

    work page doi:10.3847/1538-4365/ad91a8

  29. [29]

    L., Pound, M

    Karim, R. L., Pound, M. W., Tielens, A. G. G. M., et al. 2023, AJ, 166, 240, doi: 10.3847/1538-3881/acff6c —. 2025, ApJ, 995, 196, doi: 10.3847/1538-4357/ae17cc Kavak, ¨U., Bally, J., Goicoechea, J. R., et al. 2022a, A&A, 663, A117, doi: 10.1051/0004-6361/202243332 Kavak, ¨U., Goicoechea, J. R., Pabst, C. H. M., et al. 2022b, A&A, 660, A109, doi: 10.1051/...

    work page doi:10.3847/1538-3881/acff6c 2023

  30. [30]

    2025, PASJ, 77, 1036, doi: 10.1093/pasj/psaf081

    Kohno, M., Sofue, Y., Fukui, Y., & Tachihara, K. 2025, PASJ, 77, 1036, doi: 10.1093/pasj/psaf081

    work page doi:10.1093/pasj/psaf081 2025

  31. [31]

    Krumholz, M. R. 2014, PhR, 539, 49, doi: 10.1016/j.physrep.2014.02.001

    doi:10.1016/j.physrep.2014.02.001 2014

  32. [32]

    O., Thompson, M

    Langa, M. O., Thompson, M. A., Rigby, A. J., et al. 2026, MNRAS, 545, staf2037, doi: 10.1093/mnras/staf2037

    work page doi:10.1093/mnras/staf2037 2026

  33. [33]

    2015, ApJS, 219, 20, doi: 10.1088/0067-0049/219/2/20

    Li, H., Li, D., Qian, L., et al. 2015, ApJS, 219, 20, doi: 10.1088/0067-0049/219/2/20

    doi:10.1088/0067-0049/219/2/20 2015

  34. [34]

    2025, ApJL, 992, L15, doi: 10.3847/2041-8213/ae0579

    Lin, S., Feng, S., Xu, F., et al. 2025, ApJL, 992, L15, doi: 10.3847/2041-8213/ae0579

    work page doi:10.3847/2041-8213/ae0579 2025

  35. [35]

    2024, ApJ, 964, 93, doi: 10.3847/1538-4357/ad24e0

    Liu, D., Xu, Y., Li, Y., et al. 2024, ApJ, 964, 93, doi: 10.3847/1538-4357/ad24e0

    work page doi:10.3847/1538-4357/ad24e0 2024

  36. [36]

    F., & Ostriker, E

    McKee, C. F., & Ostriker, E. C. 2007, ARA&A, 45, 565, doi: 10.1146/annurev.astro.45.051806.110602

    Pith/arXiv arXiv 2007

  37. [37]

    A., Williams, G

    Mutale, M., Thompson, M. A., Williams, G. M., et al. 2025, MNRAS, doi: 10.1093/mnras/staf1849

    doi:10.1093/mnras/staf1849 2025

  38. [38]

    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

    doi:10.1093/pasj/psaf008 2025

  39. [39]

    A new view of the giant molecular cloud M16 (Eagle Nebula) in 12CO J=1-0 and 2-1 transitions with NANTEN2

    Nishimura, A., Costes, J., Inaba, T., et al. 2017, arXiv e-prints, arXiv:1706.06002, doi: 10.48550/arXiv.1706.06002

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1706.06002 2017

  40. [40]

    Oliphant, T. E. 2007, Computing in Science and Engineering, 9, 10, doi: 10.1109/MCSE.2007.58

    doi:10.1109/mcse.2007.58 2007

  41. [41]

    Pabst, C. H. M., Hacar, A., Goicoechea, J. R., et al. 2021, A&A, 651, A111, doi: 10.1051/0004-6361/202140804

    doi:10.1051/0004-6361/202140804 2021

  42. [42]

    Pabst, C. H. M., Goicoechea, J. R., Hacar, A., et al. 2022, A&A, 658, A98, doi: 10.1051/0004-6361/202140805

    doi:10.1051/0004-6361/202140805 2022

  43. [43]

    2011, Journal of Machine Learning Research, 12, 2825

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825

    2011

  44. [44]

    E., Arzoumanian, D., Andre, P., et al

    Pineda, J. E., Arzoumanian, D., Andre, P., 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, 233, doi: 10.48550/arXiv.2205.03935

    Pith/arXiv arXiv doi:10.48550/arxiv.2205.03935 2023

  45. [45]

    H., Young, E

    Rieke, G. H., Young, E. T., Engelbracht, C. W., et al. 2004, ApJS, 154, 25, doi: 10.1086/422717

    doi:10.1086/422717 2004

  46. [46]

    2020, PASP, 132, 104301, doi: 10.1088/1538-3873/aba840

    Schneider, N., Simon, R., Guevara, C., et al. 2020, PASP, 132, 104301, doi: 10.1088/1538-3873/aba840

    doi:10.1088/1538-3873/aba840 2020

  47. [47]

    2012, IEEE Transactions on Terahertz Science and Technology, 2, 593, doi: 10.1109/TTHZ.2012.2213818 BWFields: Bubble Identification and Analysis17

    Shan, W., Yang, J., Shi, S., et al. 2012, IEEE Transactions on Terahertz Science and Technology, 2, 593, doi: 10.1109/TTHZ.2012.2213818 BWFields: Bubble Identification and Analysis17

    arXiv 2012

  48. [48]

    Sherman, R. A. 2012, ApJ, 760, 58, doi: 10.1088/0004-637X/760/1/58

    work page doi:10.1088/0004-637x/760/1/58 2012

  49. [49]

    J., Povich, M

    Simpson, R. J., Povich, M. S., Kendrew, S., et al. 2012, MNRAS, 424, 2442, doi: 10.1111/j.1365-2966.2012.20770.x

    arXiv 2012

  50. [50]

    2019, ApJS, 240, 9, doi: 10.3847/1538-4365/aaf1c8 van der Walt, S., Sch¨ onberger, J

    Su, Y., Yang, J., Zhang, S., et al. 2019, ApJS, 240, 9, doi: 10.3847/1538-4365/aaf1c8 van der Walt, S., Sch¨ onberger, J. L., Nunez-Iglesias, J., et al. 2014, PeerJ, 2, e453, doi: 10.7717/peerj.453

    doi:10.3847/1538-4365/aaf1c8 2019

  51. [51]

    E., et al

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

    doi:10.1038/s41592-019-0686-2 2020

  52. [52]

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

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

    doi:10.1051/0004-6361/202346075 2023

  53. [53]

    Xu, D., & Offner, S. S. R. 2017, ApJ, 851, 149, doi: 10.3847/1538-4357/aa9a42

    work page doi:10.3847/1538-4357/aa9a42 2017

  54. [54]

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

    doi:10.3847/1538-4357/ab6607 2020

  55. [55]

    2019, A&A, 627, A27, doi: 10.1051/0004-6361/201935024

    Xu, J.-L., Zavagno, A., Yu, N., et al. 2019, A&A, 627, A27, doi: 10.1051/0004-6361/201935024

    work page doi:10.1051/0004-6361/201935024 2019

  56. [56]

    2016, AJ, 152, 117, doi: 10.3847/0004-6256/152/5/117

    Yan, Q.-z., Xu, Y., Zhang, B., et al. 2016, AJ, 152, 117, doi: 10.3847/0004-6256/152/5/117

    work page doi:10.3847/0004-6256/152/5/117 2016

  57. [57]

    2026, ApJS, 282, 65, doi: 10.3847/1538-4365/ae29e7

    Yang, J., Yan, Q.-Z., Su, Y., et al. 2026, ApJS, 282, 65, doi: 10.3847/1538-4365/ae29e7

    doi:10.3847/1538-4365/ae29e7 2026

  58. [58]

    2016, Research in Astronomy and Astrophysics, 16, 56, doi: 10.1088/1674-4527/16/4/056

    Song, C. 2016, Research in Astronomy and Astrophysics, 16, 56, doi: 10.1088/1674-4527/16/4/056

    work page doi:10.1088/1674-4527/16/4/056 2016

  59. [59]

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

    Zhou, J. W., & Han, A. A. 2026, arXiv e-prints, arXiv:2605.30533, doi: 10.48550/arXiv.2605.30533 18Jiang et al. APPENDIX A.VERIFIED BUBBLE CASES The verified bubbles presented here serve as methodological benchmarks for assessing the reliability of the BWFields. These examples show that the combined use of the bubble-weight field, emission-defined intensi...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2605.30533 2026

This paper was first reviewed by deepseek-v4-flash on August 6, 2026.