Pith. sign in

REVIEW 3 major objections 4 minor 91 references

Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalogue

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A new algorithm identifies interstellar filaments in spectral-line cubes by requiring that neighboring clumps' directions and positions agree with a local filament axis.

desk verdict New open-source PPV filament finder with a genuinely new algorithmic idea, but the central 'better suited' claim needs quantitative validation before the catalog is used as ground truth. read the letter →

arxiv 2412.01238 v2 pith:F3NJT4D7 submitted 2024-12-02 astro-ph.GA

Yu Jiang This is my paper · ORCID
classification astro-ph.GA
keywords interstellarfilamentsmolecularcloudsposition-position-velocitydataclumpdetectiongraph-basedskeletonizationMWISPsurvey13COemissionstarformation
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 paper introduces DPConCFil, a suite of three algorithms for finding and measuring interstellar filaments in position-position-velocity (PPV) datacubes of molecular-line emission. Its identification method defines a filament as an elongated structure with spatial and velocity continuity, built from at least two neighboring clumps whose principal-axis directions are consistent with the line joining them, or that lie close to a local axis established by such clumps. Applied to the MWISP 13CO survey data, it produces 344 filaments in the 10–20 degree longitude band, with a catalog of lengths, aspect ratios, velocity gradients, profile symmetries, and Gaussian widths. The paper argues that because the method works directly in PPV space and inherits clump regions, it is better suited to filaments of various scales than tools that integrate over velocity (FilFinder), treat all axes equally (DisPerSE), or rely only on clump proximity (MST). The value of getting this right is that filaments are the sites where gas collects before forming stars, so a reliable PPV filament census changes how the Milky Way's star-forming structure is traced.

What carries the argument

The load-bearing mechanism is the consistency-based identification rule in Section 3.1: a filament is grown from a pair of neighboring clumps whose principal-axis directions are within a 30-degree angle tolerance of the line joining them, and then extended by clumps whose perpendicular distance to that local axis is within 4 pixels (2 arcminutes). Directional consistency is the primary signal, and positional consistency lets clumps join an established axis even when their own elongation is unreliable; edge-touching clumps are excluded from the direction test but included in the distance test. Around this rule, two graph algorithms do the analysis: the skeletonization method turns each pixel in the filament region into a node of a minimum spanning tree whose edge weights favour short, bright connections, then takes the heaviest shortest path between boundary leaves as the intensity skeleton; the substructuring method builds a clump-level tree with weights combining spatial distance, velocity-channel difference, and mean intensity, and recursively extracts the longest shortest paths to separate sub-filaments that share intersection clumps.

What would settle it

Take the clump catalog from the example field, add random orientation noise to each clump's principal axis using the scatter observed between PP and PPV fits in Appendix B, and rerun DPConCFil; if the recovered filaments fragment or merge substantially, the consistency signal is not robust enough to support the claimed advantage. Alternatively, measure the fraction of true filaments recovered from synthetic PPV cubes with known axes as signal-to-noise decreases.

Watch

Extended reading notes

Core claim

The central claim is that filamentary molecular structures can be identified in the full three-dimensional PPV datacube by exploiting a local consistency between clumps and filaments: dense clumps tend to be elongated along the filament axis, and their positions tend to lie on that axis. DPConCFil encodes this as a concrete rule — for two neighboring clumps, the angle between each clump's principal axis and the line connecting the clumps must be below 30 degrees, and additional clumps must fall within 4 pixels of that line — and then merges overlapping clump records into filaments, inheriting their regions. The same package then derives intensity skeletons from a graph-theoretic minimum spanning tree weighted by distance and integrated intensity, and decomposes complex filaments into sub-filaments through a recursive longest-shortest-path procedure. The paper reports that this recovers all visually identifiable filaments in the example field, separates filaments that overlap spatially but differ by about 11 km/s in velocity, and identifies 344 filaments, containing roughly half of all clumps, in the MWISP application region.

Load-bearing premise

The method depends on a single Gaussian fit to each clump's integrated-intensity map giving a reliable orientation for the local filament axis; if those directions are noisy or biased for a substantial fraction of clumps, real filaments will be fragmented or missed.

Editorial extensions

If this is right

  • Filaments that overlap on the sky but are separated in velocity, such as Filaments 1 and 7 in the example field, are kept distinct instead of being merged by velocity integration.
  • Filament regions are inherited from clump masks, so derived radial profiles and FWHM values do not depend on drawing arbitrary width boundaries around skeletons.
  • From MWISP 13CO data, DPConCFil yields 344 filaments (135 with five or more clumps); 80% lie within 5 km/s of a spiral-arm velocity, and filament angular length shows no significant correlation with spiral-arm separation.
  • Sub-filaments extracted by the recursive decomposition share intersection clumps; in the worked example, restricting profiles to each substructure raises the profile symmetry metric SIOU from 0.68 to 0.76.
  • The same pipeline applied to a synthetic PPV cube from a galactic ISM simulation recovers long filaments and places a higher fraction of clumps inside filaments (66.2%) than in the real data, suggesting the method transfers beyond MWISP.

Reading between the lines

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

  • Beyond the paper: a quantitative benchmark against simulated cubes with known axes would settle whether the 'better suited' claim holds, since the paper's comparison with FilFinder, DisPerSE, and MST is mainly visual and descriptive.
  • Beyond the paper: the default tolerances of 30 degrees and 4 pixels are tied to MWISP's angular resolution and velocity sampling, so higher-resolution or extragalactic data would likely require rescaling, and no calibration recipe is given.
  • Beyond the paper: the consistency premise implies a testable prediction that clumps whose elongation is genuinely perpendicular to the local filament axis should be rare; searching for such perpendicular-clump filaments in the catalog would bound how often the key assumption fails.
  • Beyond the paper: the sub-filament decomposition could serve as a quantitative definition of hubs in hub-filament systems, linking the method to core-formation studies; the paper notes intersection clumps are shared but does not exploit that connection.
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, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper introduces DPConCFil, a suite of three algorithms for filament work in PPV space: a consistency-based identification method that grows filaments from neighboring clumps whose principal-axis directions align with the connecting line (TolAngle) and that then adds clumps within TolDistance of the local axis, a graph-based skeletonization method using intensity-weighted minimum spanning trees, and a graph-based sub-structuring method that recursively extracts sub-filaments. The authors apply the pipeline to MWISP 13CO data toward 10 deg <= l <= 20 deg, -5.25 deg <= b <= 5.25 deg, and -200 to 200 km/s, producing a catalog of 344 filaments and statistics on Galactic distribution, velocity gradients, profile symmetry, FWHM, and orientation. They compare the results visually with FilFinder, DisPerSE, and MST, and also apply the method to one Cloud Factory/POLARIS synthetic cube in Appendix E.

Significance. DPConCFil addresses a genuine problem: filament identification in PPV space, where velocity integration can blur or merge distinct structures and where pure 3D geometric methods have difficulty treating the velocity axis. The algorithmic description is clear, Equations (1)-(4) define the graph weights explicitly, Table 1 gives the default parameters, and the code is publicly available on GitHub and Zenodo with a manual. The substructuring method appears to improve profile symmetry relative to treating a whole complex filament as a single object, and the MWISP catalog is a useful observational product. The method is not circular: it does not derive physical parameters from assumptions that already contain the answer. However, the central claim that DPConCFil is 'better suited for identifying and analyzing filaments of various scales in the PPV space' is not yet quantitatively supported; the simulation test in Appendix E, where ground truth is available, is used only for visual comparison. A quantitative recovery analysis and a sensitivity study of the tolerances are needed before the comparative claim can be accepted.

major comments (3)
  1. [Section 5; Appendix E] The paper's central comparative claim, that DPConCFil is 'better suited for identifying and analyzing filaments of various scales in the PPV space' (Section 5), is supported only by side-by-side visual inspection in Figures 9, 17-19, 21, and 23. In the one case with known ground truth, the Cloud Factory/POLARIS synthetic cube in Appendix E, no recovery metrics are reported: there is no completeness, purity, false-positive rate, skeleton-position error, or clump-membership accuracy for DPConCFil or for FilFinder, DisPerSE, and MST. Because the catalog and all statistics in Section 4 inherit the identification output, the absence of a quantitative recovery test leaves the core effectiveness claim unverified. I recommend adding a recovery analysis on the Appendix E simulation, using the known filamentary structure, and reporting the same metrics for all four methods with identical parameter settings.
  2. [Section 3.1.2; Appendix B] The identification method's seed step requires two neighboring clumps whose principal-axis directions both lie within TolAngle = 30 degrees of the connecting line (Section 3.1.2). Appendix B shows that in the velocity-integrated PP map, blending of different velocity components can shift clump positions and orientations substantially; the same concern applies to PPV-space clump axes. The paper asserts that consistency is 'prevalent' in PPV space, but it does not quantify the distribution of angular offsets between clump principal axes and the true local filament axis. If a non-negligible fraction of clumps have noisy or biased axes, seed pairs will fail to form and genuine filaments will be fragmented or missed. A quantitative test on the simulated cube, such as the distribution of clump-axis offsets relative to the known local filament orientation, plus a sensitivity scan of TolAngle and TolDistance, would determine whether this failure mode is important. This is load-bearing because the entire identification criterion depends on the reliability of the clump principal-axis estimates.
  3. [Section 4.2; Table 3] The headline catalog statistics, including 344 filaments, 52.3% of clumps inside filaments, and the medians in Table 3, depend on three free parameters: TolAngle = 30 degrees, TolDistance = 4 pixels, and LW Ratio = 2.5. Section 3.4 states that the defaults are based on 'extensive experimental results' but gives no stability or sensitivity analysis. Since the catalog is a central deliverable, the paper should show how the filament count, clump-membership fraction, and key median properties respond to reasonable variations of these parameters. Without this, the reader cannot distinguish robust trends from threshold-dependent artifacts.
minor comments (4)
  1. [Section 3.2.1; Equations (1)-(2)] The graph containing all points of a filament region is named G1 in the text, but Equation (1) refers to 'the graph G2'; Section 3.3 later uses G2 for the substructure graph. Please align the notation.
  2. [Figure 14] The figure has duplicate and inconsistent panel labels: the second row uses (d), (e), (h) and the third row uses (d), (e), (f), while the caption says panels (a)-(f). Please re-letter all panels and update the caption and text references accordingly.
  3. [Throughout] There are several typographical errors: 'wight' in Equations (1)-(4), 'methonds' in the first paragraph of Section 4.1, and 'flaments' in the note to Table 2. Please proofread carefully.
  4. [Section 4.1; Table 2] The text notes that not all filaments in Table 2 appear in the application-data catalog because of differences in edge clumps, but the catalog table itself does not carry this caveat. A brief note in the machine-readable catalog would help users avoid misinterpreting the sample selection.

Circularity Check

2 steps flagged · score 2.0 of 10

Minor circularity in peripheral validation statements; central algorithm is an explicit operational definition and is not circular.

  1. self definitional [Appendix E, final paragraph]
    "These experiments support the widespread consistency observed across various simulated data (Hennebelle 2013)."

    The 'experiments' are DPConCFil runs on a synthetic cube. DPConCFil's identification criterion (Section 3.1.1) defines a filament as clumps whose directions and positions are consistent with the local filament axis, and Section 3.1.2 records exactly those clump pairs. Hence any filament output by DPConCFil exhibits clump-filament consistency by construction; running the algorithm cannot independently corroborate the prevalence of that consistency. The only independent support is the cited Hennebelle (2013) result. This is a peripheral overstatement rather than the paper's central method claim.

  2. self definitional [Appendix B, final paragraph]
    "The clumps in Figure 2 and Figure 21 indicate that consistency between the direction and position of clumps and the local axis of the filaments in PPV space is prevalent, regardless of whether the filaments are large scale or small scale."

    The filaments shown in Figure 2 (example data) and Figure 21 (GMF B and C) are those identified by DPConCFil, whose membership rule already requires directional or positional consistency with a local filament axis. Therefore the 'prevalent consistency' is guaranteed by construction of the identified sample; the figure cannot independently indicate its prevalence. The statement is used as an observational remark, not to support the central 'better suited' comparative claim, but it is circular as written.

full rationale

The paper's central contribution is an algorithm whose filament definition is explicitly operational: Section 3.1.1 defines a filament as elongated, velocity-continuous structure made of clumps whose directions or positions are consistent with a local axis, and Section 3.1.2 builds filaments by applying exactly that rule. This is a detection algorithm implementing its own stated definition, not a hidden derivation of a result from an input that contains it. The tunable parameters (TolAngle, TolDistance, LW Ratio) are thresholds, not quantities fitted to a target and then renamed as predictions; the Section 4 statistics describe the output sample and are therefore parameter-sensitive but not circular. The comparative claim that DPConCFil is 'better suited for identifying and analyzing filaments of various scales in the PPV space' (Section 5) rests on qualitative side-by-side images and lacks quantitative recovery metrics such as completeness or purity; that is a validation gap and a correctness risk, not a circular reduction. Self-citations to FacetClumps (Jiang et al. 2023) supply the input clump catalog; FacetClumps is a separately published algorithm, and using it as input is a modeling choice rather than a circular derivation. The only genuine circular steps are the two peripheral statements above, where DPConCFil's own output is used to claim support for the very consistency premise that defines its filaments. These do not undermine the central algorithmic content, so the overall circularity score is low.

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

The central method depends on three user-tuned tolerances (Table 1) and on the assumption that clump orientations track filament orientation in PPV space. No new physical entities are introduced, and the graph-theoretic steps rely on standard mathematics.

free parameters (3)
  • TolAngle = 30 degrees
    Angle tolerance for directional consistency; tuned to data per Section 3.4, not derived; affects which clumps join a filament.
  • TolDistance = 4 pixels (2 arcmin)
    Distance tolerance for positional consistency; tuned per Section 3.4.
  • LW_Ratio = 2.5
    Minimum aspect ratio for a structure to count as a filament; tuned per Section 3.4.
assumptions (3)
  • domain assumption Clump principal-axis directions are reliable tracers of local filament orientation in PPV space.
    Invoked in Section 3.1.1 (criteria for directional consistency) and Section 3.1.2; the identification method would fail if orientation noise were large.
  • domain assumption Neighboring-clump connectivity, as defined by FacetClumps masks, defines the candidate graph for filament assembly.
    Section 3.1.2; the algorithm only considers clumps that are connected in the FacetClumps mask.
  • standard math Minimum spanning tree methods yield the optimal skeleton and sub-structure paths for a filament region.
    Sections 3.2.1 and 3.3.1 use GMST; standard graph theory, no proof needed here.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalogue." pith.science (2026). https://pith.science/paper/F3NJT4D7

@misc{pith2026241201238,
  author       = {Pith},
  title        = {Pith review of: Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalogue},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F3NJT4D7}},
  note         = {Machine review of arXiv:2412.01238}
}
abstract

Filaments play a crucial role in providing the necessary environmental conditions for star formation, actively participating in the process. To facilitate the identification and analysis of filaments, we introduce DPConCFil (Directional and Positional Consistency between Clumps and Filaments), a suite of algorithms comprising one identification method and two analysis methods. The first method, the consistency-based identification approach, uses directional and positional consistency among neighboring clumps and local filament axes to identify filaments in the PPV datacube. The second method employs a graph-based skeletonization technique to extract the filament intensity skeletons. The third method, a graph-based substructuring approach, allows the decomposition of complex filaments into simpler sub-filaments. We demonstrate the effectiveness of DPConCFil by applying the identification method to the clumps detected in the Milky Way Imaging Scroll Painting (MWISP) survey dataset by FacetClumps, successfully identifying a batch of filaments across various scales within $10^{\circ} \leq l \leq 20^{\circ}$, $-5.25^{\circ} \leq b \leq 5.25^{\circ}$ and -200 km s$^{-1}$ $\leq v \leq$ 200 km s$^{-1}$. Subsequently, we apply the analysis methods to the identified filaments, presenting a catalog with basic parameters and conducting statistics of their galactic distribution and properties. DPConCFil is openly available on GitHub, accompanied by a manual.

Figures

Figures reproduced from arXiv: 2412.01238 by the authors.

Figure 1
Figure 1. An example data to illustrate DPConCFil. The data is the 13CO emission of MWISP within 17.7 ◦ ≤ l ≤ 18.5 ◦ , 0◦ ≤ b ≤ 0.8 ◦ and 5 km s−1 ≤ v ≤ 30 km s−1 . 10 15 20 25 km s 1 2 4 6 8 10 12 14 K k m s 1 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The direction and position of clumps. The to￾tal number of clumps is 126, with 88 of them not touching the edge. The asterisks denote the spatial positions of the clumps, and the different colors of the asterisks denote differ￾ent velocity positions. The red lines illustrate the direction of the principal axis of the clumps at the untouched edges. for those clumps that do not touch the edges. The pa￾rameters of Face… view at source ↗
Figure 3
Figure 3. provides an illustrative example of how to as￾sess the consistencies. To begin, start with any clump A (e.g., clump 1 in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (20 more)
Figure 4
Figure 4. Figure 4: Integrated intensity maps of a filament in different directions. Left panel: velocity-integrated intensity map. The central position of the filament, denoted by a red asterisk, has coordinates (l, b, v) = (18.11◦ , 0.41◦ , 22.91 km s−1 ), and its principal axis derived…
Figure 5
Figure 5. Figure 5: Intensity skeleton analysis. (a) The initial intensity skeleton extracted using the graph-based skeletonization method; (b) the final thinned intensity skeleton; (c) the fitted intensity skeleton and profiles. The thicker red curve represents the smoothed intensity ske…
Figure 6
Figure 6. Figure 6: Substructures of the filament. The numbered green and red circles connected by green lines represent the positions of clumps within the first sub-filament, while the green contour represents the boundary of this substructure. Similarly, the numbered blue and red circle…
Figure 7
Figure 7. Figure 7: (a) The fitted intensity skeleton and profiles of the first sub-filament. The green contour outlines the region of this sub-filament. (b) The fitted intensity skeleton and profiles of the second sub-filament. The blue contour outlines the region of this sub-filament. (…
Figure 8
Figure 8. Figure 8: (a) Intensity profiles of [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: All filaments identified from [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Distribution of all filaments in the Galactic longitude-latitude and Galactic longitude-velocity planes in the appli￾cation data. The background map illustrates the distribution of molecular gas traced by the integrated 13CO emission. Dark blue circles denote filament…
Figure 11
Figure 11. Figure 11: Cumulative distribution of the velocity separa￾tion between filaments identified by DPConCFil and their nearest spiral arm along the line of sight. The spiral arm velocities are depicted in [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: The angular length of filament, as certified by DPConCFil, in relation to the velocity separation between filaments and their nearest spiral arm. Points of different colors represent varying density estimates from the kernel density estimation, with the red line indic…
Figure 13
Figure 13. Figure 13: The histogram statistics of Galactic distribution. Panels (a)-(c) display the distribution of filament positions, including Galactic longitude, Galactic latitude, and velocity. Dark blue denotes filaments identified by DPConCFil, while royal blue indicates those with …
Figure 14
Figure 14. Figure 14: The histogram statistics of properties. Panels (a)-(c) display the number of clumps within the filament, the angle, and the angular area for DPConCFil and MST. Panels (d)-(f) show the velocity gradient, SIOU, and angular FWHM from Gaussian fitting for DPConCFil [PITH…
Figure 15
Figure 15. Figure 15: Velocity-integrated intensity images of filaments in [PITH_FULL_IMAGE:figures/full_fig_p020_15.png]
Figure 16
Figure 16. Figure 16: The clumps detected by FacetClumps in the velocity-integrated map. The intensity threshold is 1.6 K km s−1 . The asterisks denote the positions of the clumps, the lines denote the direction of the principal axis of the clumps that have not touched the edge. Clumps 1, …
Figure 17
Figure 17. Figure 17: The structures isolated by the FilFinder algorithm. The left panel shows the skeletons extracted from the mask obtained by FilFinder on the velocity-integrated map, with a global threshold of 0.8 K km s−1 . The right panel shows the skeletons extracted from the mask o…
Figure 18
Figure 18. Figure 18: The structures isolated by the DisPerSE algorithm. Left panel: velocity-integrated intensity map. Curves in different colors denote different skeletons, each labeled with a corresponding number in the same color. Middle panel: latitude￾integrated intensity map. Right …
Figure 19
Figure 19. Figure 19: The structures isolated by the MST algorithm. In the left panel, lines of various colors interconnect distinct coherent structures, while circles denote the spatial positions of clumps within these structures. The background is the velocity-integrated intensity map of…
Figure 20
Figure 20. Figure 20: The area of the largest molecular cloud and filaments identified by DPConCFil in the application data. The map is the 13CO emission of MWISP within 10◦ ≤ l ≤ 20◦ , −1.6 ◦ ≤ b ≤ 1.8 ◦ and -25 km s−1 ≤ v ≤ 90 km s−1 , and is preprocessed employing the signal region extr…
Figure 21
Figure 21. Figure 21: Giant structures isolated by DPConCFil and MST from the application data. DPConCFil-B, DPConCFil-C, and MST-B refer to the same filaments marked in [PITH_FULL_IMAGE:figures/full_fig_p025_21.png]
Figure 22
Figure 22. Figure 22: Filaments identified by DPConCFil from the simulated molecular clouds. The background shows the integrated intensity of all clumps detected by FacetClumps. The red, blue, and green asterisks and line segments denote the position and direction of clumps within the fila…
Figure 23
Figure 23. Figure 23: The structures isolated by various algorithms in the simulated molecular clouds. Different colors indicate distinct structures. DPConCFil: The background shows the integrated intensity of all clumps, and the curves denote the longest skeletons of filaments. FilFinder:…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

91 extracted references · 10 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    - [1] #1 = = ^ ^ ^ .\!\!^ d .\!\!^ h .\!\!^ m .\!\!^ s .\!\!^ @mss

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

    2021

  4. [4]

    2016, , 590, A131, 10.1051/0004-6361/201527674

    Abreu-Vicente , J., Ragan , S., Kainulainen , J., et al. 2016, , 590, A131, 10.1051/0004-6361/201527674

    doi:10.1051/0004-6361/201527674 2016

  5. [5]

    2014, in Protostars and Planets VI, ed

    Andr \'e , P., Di Francesco , J., Ward-Thompson , D., et al. 2014, in Protostars and Planets VI, ed. H. Beuther , R. S. Klessen , C. P. Dullemond , & T. Henning , 27--51, 10.2458/azu_uapress_9780816531240-ch002

    doi:10.2458/azu_uapress_9780816531240-ch002 2014

  6. [6]

    2010, , 518, L102, 10.1051/0004-6361/201014666

    Andr \'e , P., Men'shchikov , A., Bontemps , S., et al. 2010, , 518, L102, 10.1051/0004-6361/201014666

    doi:10.1051/0004-6361/201014666 2010

  7. [7]

    2013, , 553, A119, 10.1051/0004-6361/201220822

    Arzoumanian , D., Andr \'e , P., Peretto , N., & K \"o nyves , V. 2013, , 553, A119, 10.1051/0004-6361/201220822

    doi:10.1051/0004-6361/201220822 2013

  8. [8]

    2011, , 529, L6, 10.1051/0004-6361/201116596

    Arzoumanian , D., Andr \'e , P., Didelon , P., et al. 2011, , 529, L6, 10.1051/0004-6361/201116596

    doi:10.1051/0004-6361/201116596 2011

Show all 91 references
  1. [9]

    2019, , 621, A42, 10.1051/0004-6361/201832725

    Arzoumanian , D., Andr \'e , P., K \"o nyves , V., et al. 2019, , 621, A42, 10.1051/0004-6361/201832725

    2019 doi

  2. [10]

    M., Sip o cz , B

    Astropy Collaboration , Price-Whelan , A. M., Sip o cz , B. M., et al. 2018, , 156, 123, 10.3847/1538-3881/aabc4f

    2018 doi

  3. [11]

    N., Offner , S

    Beaumont , C. N., Offner , S. S. R., Shetty , R., Glover , S. C. O., & Goodman , A. A. 2013, , 777, 173, 10.1088/0004-637X/777/2/173

    2013 doi

  4. [12]

    Berry , D. S. 2015, Astronomy and Computing, 10, 22, 10.1016/j.ascom.2014.11.004

    2015 doi

  5. [13]

    2017, , 601, A90, 10.1051/0004-6361/201629001

    Brauer , R., Wolf , S., Reissl , S., & Ober , F. 2017, , 601, A90, 10.1051/0004-6361/201629001

    2017 doi

  6. [14]

    M., et al

    Bresnahan , D., Ward-Thompson , D., Kirk , J. M., et al. 2018, , 615, A125, 10.1051/0004-6361/201730515

    2018 doi

  7. [15]

    S., Montier , L., Ferri \`e re , K., & Ristorcelli , I

    Carri \`e re , J. S., Montier , L., Ferri \`e re , K., & Ristorcelli , I. 2022, , 668, A41, 10.1051/0004-6361/202243506

    2022 doi

  8. [17]

    2020 b , , 891, 84, 10.3847/1538-4357/ab7378

    ---. 2020 b , , 891, 84, 10.3847/1538-4357/ab7378

    2020 doi

  9. [18]

    2023 a , , 165, 16, 10.3847/1538-3881/ac9ea2

    Chen , X., Sun , L., Feng , J., et al. 2023 a , , 165, 16, 10.3847/1538-3881/ac9ea2

    2023 doi

  10. [19]

    2023 b , , 525, 107, 10.1093/mnras/stad2259

    Chen , Z., Sefako , R., Yang , Y., et al. 2023 b , , 525, 107, 10.1093/mnras/stad2259

    2023 doi

  11. [20]

    J., Debattista , V

    Clarke , A. J., Debattista , V. P., Ro s kar , R., & Quinn , T. 2017, , 465, L79, 10.1093/mnrasl/slw214

    2017 doi

  12. [21]

    D., Whitworth , A

    Clarke , S. D., Whitworth , A. P., Spowage , R. L., et al. 2018, , 479, 1722, 10.1093/mnras/sty1675

    2018 doi

  13. [22]

    D., Williams , G

    Clarke , S. D., Williams , G. M., & Walch , S. 2020, , 497, 4390, 10.1093/mnras/staa2298

    2020 doi

  14. [23]

    Cox , N. L. J., Arzoumanian , D., Andr \'e , P., et al. 2016, , 590, A110, 10.1051/0004-6361/201527068

    2016 doi

  15. [24]

    J., Hacar , A., Clark , S

    Feng , J., Smith , R. J., Hacar , A., Clark , S. E., & Seifried , D. 2024, , 528, 6370, 10.1093/mnras/stae407

    2024 doi

  16. [25]

    2022, , 259, 36, 10.3847/1538-4365/ac4a76

    Ge , Y., & Wang , K. 2022, , 259, 36, 10.3847/1538-4365/ac4a76

    2022 doi

  17. [26]

    2023, , 675, A119, 10.1051/0004-6361/202245784

    Ge , Y., Wang , K., Duarte-Cabral , A., et al. 2023, , 675, A119, 10.1051/0004-6361/202245784

    2023 doi

  18. [27]

    1974, , 189, 441, 10.1086/152821

    Goldreich , P., & Kwan , J. 1974, , 189, 441, 10.1086/152821

    1974 doi

  19. [28]

    2017, , 834, 202, 10.3847/1538-4357/834/2/202

    Gritschneder , M., Heigl , S., & Burkert , A. 2017, , 834, 202, 10.3847/1538-4357/834/2/202

    2017 doi

  20. [29]

    E., Heitsch , F., et al

    Hacar , A., Clark , S. E., Heitsch , 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 , 153, 10.48550/arXiv.2203.09562

    2023 doi arXiv

  21. [30]

    2013, , 554, A55, 10.1051/0004-6361/201220090

    Hacar , A., Tafalla , M., Kauffmann , J., & Kov \'a cs , A. 2013, , 554, A55, 10.1051/0004-6361/201220090

    2013 doi

  22. [31]

    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

    2008

  23. [32]

    2007, , 654, 988, 10.1086/509321

    Hartmann , L., & Burkert , A. 2007, , 654, 988, 10.1086/509321

    2007 doi

  24. [33]

    2013, , 556, A153, 10.1051/0004-6361/201321292

    Hennebelle , P. 2013, , 556, A153, 10.1051/0004-6361/201321292

    2013 doi

  25. [34]

    2019, Frontiers in Astronomy and Space Sciences, 6, 5, 10.3389/fspas.2019.00005

    Hennebelle , P., & Inutsuka , S.-i. 2019, Frontiers in Astronomy and Space Sciences, 6, 5, 10.3389/fspas.2019.00005

    2019

  26. [35]

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

    2007 doi

  27. [36]

    2016, , 833, 10, 10.3847/0004-637X/833/1/10

    Inoue , T., & Inutsuka , S.-i. 2016, , 833, 10, 10.3847/0004-637X/833/1/10

    2016 doi

  28. [37]

    F., Smith , R

    Izquierdo , A. F., Smith , R. J., Glover , S. C. O., et al. 2021, , 500, 5268, 10.1093/mnras/staa3470

    2021 doi

  29. [38]

    M., Finn , S

    Jackson , J. M., Finn , S. C., Chambers , E. T., Rathborne , J. M., & Simon , R. 2010, , 719, L185, 10.1088/2041-8205/719/2/L185

    2010 doi

  30. [39]

    2024, DPConCFil: A Collection of Filament Identification and Analysis Algorithms , Zenodo, 10.5281/zenodo.13853675

    Jiang, Y. 2024, DPConCFil: A Collection of Filament Identification and Analysis Algorithms , Zenodo, 10.5281/zenodo.13853675

    2024 doi

  31. [40]

    2023, , 267, 32, 10.3847/1538-4365/acda89

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

    2023 doi

  32. [41]

    2022, Astronomy and Computing, 100613, https://doi.org/10.1016/j.ascom.2022.100613

    Jiang, Y., Zheng, S., Jiang, Z., et al. 2022, Astronomy and Computing, 100613, https://doi.org/10.1016/j.ascom.2022.100613

    2022

  33. [42]

    W., & Rosolowsky , E

    Koch , E. W., & Rosolowsky , E. W. 2015, , 452, 3435, 10.1093/mnras/stv1521

    2015 doi

  34. [43]

    Koda , J., Sawada , T., Hasegawa , T., & Scoville , N. Z. 2006, , 638, 191, 10.1086/498640

    2006 doi

  35. [44]

    2015, , 584, A91, 10.1051/0004-6361/201525861

    K \"o nyves , V., Andr \'e , P., Men'shchikov , A., et al. 2015, , 584, A91, 10.1051/0004-6361/201525861

    2015 doi

  36. [46]

    2020 b , , 635, A34, 10.1051/0004-6361/201834753

    ---. 2020 b , , 635, A34, 10.1051/0004-6361/201834753

    2020 doi

  37. [47]

    2020, , 638, A74, 10.1051/0004-6361/201936442

    Ladjelate , B., Andr \'e , P., K \"o nyves , V., et al. 2020, , 638, A74, 10.1051/0004-6361/201936442

    2020 doi

  38. [48]

    S., Leurini , S., et al

    Li , G.-X., Urquhart , J. S., Leurini , S., et al. 2016, , 591, A5, 10.1051/0004-6361/201527468

    2016 doi

  39. [49]

    S., & Klein , R

    Li , P. S., & Klein , R. I. 2019, , 485, 4509, 10.1093/mnras/stz653

    2019 doi

  40. [50]

    S., Lopez-Rodriguez , E., Soam , A., & Klein , R

    Li , P. S., Lopez-Rodriguez , E., Soam , A., & Klein , R. I. 2022, , 514, 3024, 10.1093/mnras/stac1527

    2022 doi

  41. [51]

    2018, , 619, A166, 10.1051/0004-6361/201833406

    Mattern , M., Kauffmann , J., Csengeri , T., et al. 2018, , 619, A166, 10.1051/0004-6361/201833406

    2018 doi

  42. [52]

    2013, , 560, A63, 10.1051/0004-6361/201321885

    Men'shchikov , A. 2013, , 560, A63, 10.1051/0004-6361/201321885

    2013 doi

  43. [53]

    2010, , 518, L103, 10.1051/0004-6361/201014668

    Men'shchikov , A., Andr \'e , P., Didelon , P., et al. 2010, , 518, L103, 10.1051/0004-6361/201014668

    2010 doi

  44. [54]

    2018, , 56, 41, 10.1146/annurev-astro-091916-055235

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

    2018 doi

  45. [55]

    Myers , P. C. 2009, , 700, 1609, 10.1088/0004-637X/700/2/1609

    2009 doi

  46. [56]

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

    2007 doi

  47. [57]

    2016, , 455, 1134, 10.1093/mnras/stv2380

    Pakmor , R., Springel , V., Bauer , A., et al. 2016, , 455, 1134, 10.1093/mnras/stv2380

    2016 doi

  48. [58]

    V., Psaradaki , I., Skalidis , R., Tassis , K., & Andrews , J

    Panopoulou , G. V., Psaradaki , I., Skalidis , R., Tassis , K., & Andrews , J. J. 2017, , 466, 2529, 10.1093/mnras/stw3060

    2017 doi

  49. [59]

    V., Tassis , K., Goldsmith , P

    Panopoulou , G. V., Tassis , K., Goldsmith , P. F., & Heyer , M. H. 2014, , 444, 2507, 10.1093/mnras/stu1601

    2014 doi

  50. [60]

    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

  51. [61]

    2012, , 541, A63, 10.1051/0004-6361/201118663

    Peretto , N., Andr \'e , P., K \"o nyves , V., et al. 2012, , 541, A63, 10.1051/0004-6361/201118663

    2012 doi

  52. [62]

    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, 10.48550/arXiv.2205.03935

    2023 doi arXiv

  53. [63]

    E., Henning , T., Tackenberg , J., et al

    Ragan , S. E., Henning , T., Tackenberg , J., et al. 2014, , 568, A73, 10.1051/0004-6361/201423401

    2014 doi

  54. [64]

    J., Menten , K

    Reid , M. J., Menten , K. M., Brunthaler , A., et al. 2019, , 885, 131, 10.3847/1538-4357/ab4a11

    2019 doi

  55. [65]

    2016, , 593, A87, 10.1051/0004-6361/201424930

    Reissl , S., Wolf , S., & Brauer , R. 2016, , 593, A87, 10.1051/0004-6361/201424930

    2016 doi

  56. [66]

    K., Ginsburg , A., et al

    Rosolowsky , E., Dunham , M. K., Ginsburg , A., et al. 2010, , 188, 123, 10.1088/0067-0049/188/1/123

    2010 doi

  57. [67]

    W., Pineda , J

    Rosolowsky , E. W., Pineda , J. E., Kauffmann , J., & Goodman , A. A. 2008, , 679, 1338, 10.1086/587685

    2008 doi

  58. [68]

    J., Richer , J

    Salji , C. J., Richer , J. S., Buckle , J. V., et al. 2015, , 449, 1782, 10.1093/mnras/stv369

    2015 doi

  59. [69]

    Schisano , E., Rygl , K. L. J., Molinari , S., et al. 2014, , 791, 27, 10.1088/0004-637X/791/1/27

    2014 doi

  60. [70]

    L., van der Tak , F

    Sch \"o ier , F. L., van der Tak , F. F. S., van Dishoeck , E. F., & Black , J. H. 2005, , 432, 369, 10.1051/0004-6361:20041729

    2005 doi

  61. [71]

    Z., & Solomon , P

    Scoville , N. Z., & Solomon , P. M. 1974, , 187, L67, 10.1086/181398

    1974 doi

  62. [72]

    2019, , 632, A83, 10.1051/0004-6361/201935689

    Shimajiri , Y., Andr \'e , P., Ntormousi , E., et al. 2019, , 632, A83, 10.1051/0004-6361/201935689

    2019 doi

  63. [73]

    J., Glover , S

    Smith , R. J., Glover , S. C. O., & Klessen , R. S. 2014, , 445, 2900, 10.1093/mnras/stu1915

    2014 doi

  64. [74]

    J., Glover , S

    Smith , R. J., Glover , S. C. O., Klessen , R. S., & Fuller , G. A. 2016, , 455, 3640, 10.1093/mnras/stv2559

    2016 doi

  65. [75]

    J., Tre , R

    Smith , R. J., Tre , R. G., Sormani , M. C., et al. 2020, , 492, 1594, 10.1093/mnras/stz3328

    2020 doi

  66. [77]

    2011, , 414, 384, 10.1111/j.1365-2966.2011.18395.x

    Sousbie , T., Pichon , C., & Kawahara , H. 2011, , 414, 384, 10.1111/j.1365-2966.2011.18395.x

    2011

  67. [78]

    2010, , 401, 791, 10.1111/j.1365-2966.2009.15715.x

    Springel , V. 2010, , 401, 791, 10.1111/j.1365-2966.2009.15715.x

    2010

  68. [79]

    2019, , 240, 9, 10.3847/1538-4365/aaf1c8

    Su , Y., Yang , J., Zhang , S., et al. 2019, , 240, 9, 10.3847/1538-4365/aaf1c8

    2019 doi

  69. [80]

    2019, , 623, A142, 10.1051/0004-6361/201834049

    Suri , S., S \'a nchez-Monge , \'A ., Schilke , P., et al. 2019, , 623, A142, 10.1051/0004-6361/201834049

    2019 doi

  70. [81]

    2015, , 574, A104, 10.1051/0004-6361/201424576

    Tafalla , M., & Hacar , A. 2015, , 574, A104, 10.1051/0004-6361/201424576

    2015 doi

  71. [82]

    P., Fuente , A., S \'a nchez-Monge , \'A ., et al

    Trevi \ n o-Morales , S. P., Fuente , A., S \'a nchez-Monge , \'A ., et al. 2019, , 629, A81, 10.1051/0004-6361/201935260

    2019 doi

  72. [83]

    L., Nunez-Iglesias , J., et al

    van der Walt, S., S ch\"onberger, J. L., Nunez-Iglesias , J., et al. 2014, PeerJ, 2, e453, 10.7717/peerj.453

    2014 doi

  73. [84]

    C., & Zamora-Avil \'e s , M

    V \'a zquez-Semadeni , E., Palau , A., Ballesteros-Paredes , J., G \'o mez , G. C., & Zamora-Avil \'e s , M. 2019, , 490, 3061, 10.1093/mnras/stz2736

    2019 doi

  74. [85]

    2024, , 686, L11, 10.1051/0004-6361/202450296

    Wang , K., Ge , Y., & Baug , T. 2024, , 686, L11, 10.1051/0004-6361/202450296

    2024 doi

  75. [86]

    2016, , 226, 9, 10.3847/0067-0049/226/1/9

    Wang , K., Testi , L., Burkert , A., et al. 2016, , 226, 9, 10.3847/0067-0049/226/1/9

    2016 doi

  76. [87]

    J., Falle , S

    Wareing , C. J., Falle , S. A. E. G., & Pittard , J. M. 2019, , 485, 4686, 10.1093/mnras/stz768

    2019 doi

  77. [88]

    2021, , 257, 51, 10.3847/1538-4365/ac242a

    Yuan , L., Yang , J., Du , F., et al. 2021, , 257, 51, 10.3847/1538-4365/ac242a

    2021 doi

  78. [89]

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

    Zhan , X.-L., Jiang , Z.-B., Chen , Z.-W., Zhang , M.-M., & Song , C. 2016, Research in Astronomy and Astrophysics, 16, 56, 10.1088/1674-4527/16/4/056

    2016 doi

  79. [90]

    2024, , 689, A3, 10.1051/0004-6361/202449853

    Zhang , G.-Y., Andr \'e , P., Men'shchikov , A., & Li , J.-Z. 2024, , 689, A3, 10.1051/0004-6361/202449853

    2024 doi

  80. [91]

    2020, , 642, A76, 10.1051/0004-6361/202037721

    Zhang , G.-Y., Andr \'e , P., Men'shchikov , A., & Wang , K. 2020, , 642, A76, 10.1051/0004-6361/202037721

    2020 doi

  81. [92]

    2018, , 864, 153, 10.3847/1538-4357/aacc66

    Zucker , C., Battersby , C., & Goodman , A. 2018, , 864, 153, 10.3847/1538-4357/aacc66

    2018 doi

  82. [93]

    Zucker , C., & Chen , H. H.-H. 2018, , 864, 152, 10.3847/1538-4357/aad3b5

    2018 doi

  83. [94]

    2019, , 887, 186, 10.3847/1538-4357/ab517d

    Zucker , C., Smith , R., & Goodman , A. 2019, , 887, 186, 10.3847/1538-4357/ab517d

    2019 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.