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Tracking Star-Forming Cores as Mass Reservoirs in Clustered and Isolated Regions Using Numerical Passive Tracer Particles

T0 review · 5 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper argues that the filling factor of a convex hull around a protostar's accreted gas reservoir distinguishes isolated from clustered star-forming cores, with lower filling factors implying more protostars, larger mass, and larger…

desk verdict A useful tracer-based filling-factor diagnostic, but the 0.3 Myr core-definition window is untested and the summary swaps the bound fractions. read the letter →

arxiv 2501.02225 v1 pith:CGQUUPPT submitted 2025-01-04 astro-ph.GA astro-ph.SR

Shingo Nozaki , Hajime Fukushima , Kazuki Tokuda , Masahiro N. Machida This is my paper · ORCID
classification astro-ph.GAastro-ph.SR
keywords starformationmolecularcloudcorespassivetracerparticlesfillingfactorconvexhullturbulenceprotostarscoremassfunction
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

This paper aims to establish that the gas which will actually feed a protostar can be identified in simulations with passive tracer particles, and that the ``filling factor'' of the region enclosing that gas tells whether the protostar is forming in isolation or in a cluster. Using hydrodynamic simulations with two turbulence strengths, the authors identify 260 star-forming cores by tracing gas that falls onto a protostar within 0.3 million years. They find that lower filling factors always go with more protostars, larger masses, and larger sizes, so the filling factor is proposed as an indicator of clustered versus isolated star formation. They also find that most cores are gravitationally bound, but the stronger-turbulence run contains more low-mass unbound cores, consistent with the inertial-inflow picture. The result matters because observed core catalogs may miss the diffuse, fragmented gas that actually feeds stars in clustered regions.

What carries the argument

The central object is the star-forming core defined by passive tracer particles: three million tracer particles, each assigned a gas mass of $0.001\,M_\odot$, are advected with the flow and followed until they accrete onto a sink particle, and the gas traced back to the moment of protostar formation defines the mass reservoir. Around each star-forming core the authors build a convex hull, the smallest convex polyhedron enclosing the core, and define the filling factor $\phi_{\rm core}=V_{\rm core}/V_{\rm hull}$, the fraction of the hull volume that is actually accreting onto the protostar. This construction converts a dynamical accretion history into a geometric, porosity-like statistic that can be correlated with core mass, size, stellar content, and gravitational binding.

What would settle it

Re-run the core identification with lookahead windows of, say, 0.1 and 0.5 million years and check whether the filling-factor anticorrelations with protostar number, mass, and radius persist; if they weaken or reverse, the reported indicator is an artifact of the chosen window rather than a property of the mass reservoirs.

Watch

Extended reading notes

Core claim

The central claim is that star-forming cores, defined as the actual mass reservoirs of protostars rather than as high-density blobs, do not coincide with the dense regions selected by observational core-finding algorithms once nearby protostars are present. In clustered environments, gas selectively accretes onto several protostars, so a single star's reservoir is clumpy and fragmented. When each reservoir is enclosed in a convex hull, the ratio of reservoir volume to hull volume, the filling factor $\phi_{\rm core}$, is lower in cores that contain more protostars and have larger mass and radius, in both the $\mathcal{M}_{\rm rms}=2$ and $\mathcal{M}_{\rm rms}=10$ runs. No massive convex-hull core has a high filling factor, implying that massive cores feed multiple stars rather than a single star. Finally, 97% of the Mach 2 and 84% of the Mach 10 convex-hull cores are gravitationally bound, with the extra unbound cores in the Mach 10 run being low-mass and attributed to the inertial-inflow model, in which protostars grow by accretion from gas that is not itself self-gravitating.

Load-bearing premise

The load-bearing premise is the choice of 0.3 million years as the lookahead window for deciding which tracer particles will accrete onto a protostar; changing this window changes which gas is assigned to each core and would directly affect the computed filling factors, masses, sizes, and bound fractions.

Editorial extensions

If this is right

  • Filling factor can serve as a classification diagnostic: convex-hull cores with low filling factors are associated with clustered regions, while high-filling-factor cores are isolated.
  • Massive cores always have low filling factors and host multiple protostars, supporting competitive accretion or clump-fed star formation rather than one massive core forming one massive star.
  • Observed dense cores identified by density thresholds in clustered regions likely underestimate the true mass reservoir, because the reservoir includes diffuse gas and fragmented clumps that observations may miss.
  • Turbulence strength changes the core population: the Mach 10 run produces more low-mass and more unbound cores than the Mach 2 run, so stronger turbulence yields a higher fraction of gravitationally unbound reservoirs.
  • Most convex-hull cores are gravitationally bound as whole regions, meaning the reservoir plus surrounding gas is typically collapsing as a unit containing several forming stars.

Reading between the lines

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

  • The paper leaves implicit that the filling factor could be estimated from synthetic observations, for example from dendrogram leaf volumes or column-density filling within a bounding polygon, and used to classify observed cores without needing tracer particles.
  • Because the 0.3 million year lookahead window sets the reservoir size, the reported core masses are trajectory-based rather than instantaneous; an observed core mass function built from density-threshold cores may therefore differ systematically from the true stellar mass reservoir, especially in clusters.
  • A testable extension is to vary the lookahead window and check whether the filling-factor anticorrelations persist, and to test whether final stellar mass correlates more tightly with star-forming-core mass than with dense-core mass.
  • If the filling-factor trend survives in runs with magnetic fields or feedback, it could become a practical bridge between numerical reservoir definitions and observed core morphologies.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 8 minor

Summary. The paper uses three-dimensional adaptive-mesh-refinement hydrodynamics simulations with passive tracer particles to identify 'star-forming cores' as the gas that will accrete onto each protostar within 0.3 Myr after protostar formation. From two runs with initial turbulent Mach numbers 2 and 10, the authors identify 260 star-forming cores, enclose them in convex hulls, and define a filling factor as the ratio of core volume to hull volume. They report that low-filling-factor hulls contain more protostars, have larger masses and sizes, and that most hulls are gravitationally bound (97% at Mach 2 and 84% at Mach 10), with 16% unbound in the Mach 10 model. The filling factor is proposed as an observational indicator for distinguishing isolated from clustered star-forming regions, and the unbound cores are interpreted in the context of the inertial-inflow model.

Significance. The tracer-based core definition is a genuine methodological advance: it targets mass reservoirs directly instead of relying on density-threshold clump finding, and the 260-core sample across two turbulence strengths is substantial for a clump-scale study. The filling factor provides a simple geometric diagnostic that could, if robust, connect simulation-based mass reservoirs to observed core morphology and to the isolated-versus-clustered distinction. The paper also engages usefully with prior work, including Collins et al. (2023) on core filling fractions and Pelkonen et al. (2021) on progenitor cores. However, the central correlations rest on an untested lookahead timescale and on single realizations per Mach number, so at present the quantitative claims are suggestive rather than established.

major comments (5)
  1. [§2.4, step i] The 0.3 Myr accretion lookahead window is load-bearing and is not tested. The tracer particles selected in step i define the entire star-forming core, and all downstream quantities (convex hull geometry, filling factor, mass, R90, and virial ratio) are computed from that selection. A shorter window would select gas closer to the protostar and likely produce more compact, higher-filling-factor cores, while a longer window would include more distant gas and could stitch together multiple accretion streams in clustered regions, lowering the filling factor and increasing the number of protostars inside the hull. The authors should provide a sensitivity study over the window (e.g., 0.1, 0.2, 0.3, and 0.5 Myr) and show explicitly that the correlations in Figures 7-10, especially the low-filling-factor/multi-protostar trend, are unchanged. Without this test, the central claim may be an artifact of one timescale.
  2. [§4.2 vs. Summary item iv] The bound fractions are internally inconsistent. Section 4.2 and Figure 10 state that 97% of Mach 2 convex hull cores and 84% of Mach 10 cores are gravitationally bound, with 16% unbound at Mach 10. Summary item iv reverses these numbers, reporting '97% of the cores in the Mrms = 10 model and 84% in the Mrms = 2 model.' Because the abstract and discussion rely on 16% unbound at Mach 10, the Summary sentence is incorrect and must be corrected. The reversal is not merely typographical: Summary item iv is presented as the paper's conclusion and directly affects the interpretation of turbulence strength on core stability.
  3. [§4, Figures 7-10] The paper reports no error bars or statistical uncertainties, and each Mach number is represented by one simulation with one initial turbulent realization. Claims such as 'regardless of turbulence strength' in the abstract and Section 5.2 are supported only by two realizations. The authors should report bootstrap or jackknife uncertainties on the binned averages in Figures 7-10, or run additional turbulent realizations, to show that the filling-factor correlations are not produced by chance. This is especially important because the number of cores per filling-factor bin is small at the extremes (e.g., very low filling factors in Figures 7 and 8).
  4. [§4.1-4.2, filling factor definition] There is a potential circularity in using the filling factor as an indicator of clustering. The convex hull encloses the star-forming core plus surrounding non-accreting gas and other protostars; when another protostar lies inside the hull, the hull volume can be inflated and the star-forming core can be geometrically fragmented by competitive accretion. Thus low filling factor and a large number of embedded protostars are partly generated by the same construction. A concrete control test would be to compute a filling factor using hulls built only from the target protostar's tracer particles, or to compare against a null distribution obtained by randomly placing the same number of protostars inside a cluster. Without such a control, the manuscript does not fully establish that low filling factor carries information beyond being a direct consequence of how the hull and core were defined.
  5. [§3.1 and §5.2] The mass and radius of the convex hull core include other protostars and ambient gas inside the hull, so the stronger correlations in Figures 8 and 9 for low-filling-factor hulls are partly mechanical: a hull that contains more protostars will tend to have larger total mass and larger R90. The authors should separate the gas mass and protostar mass contributions, and report whether the filling-factor correlation with, e.g., gas mass alone or with protostar mass alone still holds. This would strengthen the physical interpretation that low filling factor traces clustered mass supply rather than merely hull size.
minor comments (8)
  1. [Figure 2 caption] The caption lists 'Mach2Mach5Mach10' but only Mrms = 2 and Mrms = 10 are presented in the paper; remove the stray 'Mach5'.
  2. [Figure 10 caption] The caption contains the typo 'represemt' for 'represent'.
  3. [Summary item iv] There is a typo, 'particluarly', that should read 'particularly'.
  4. [§3.2] The word 'inertia' is misspelled as 'intertia' in the sentence describing the moment of inertia tensor.
  5. [Equation (8)] Equation (8) for tracer advection is dimensionally awkward as written; please clarify the interpolation notation or refer explicitly to the corresponding equations in Koga et al. (2022).
  6. [References] The reference 'Smith et al. 2009' is cited for the virial-parameter definition, but the listed reference is to 'Environment and Planning A: Economy and Space', which appears to be a mismatched citation likely intended for a star-formation simulation paper; please correct it.
  7. [Appendix A, Figure A1] The y-axis label '10N' should likely read 'N' (number of protostars).
  8. [Figure 10] The exclusion of five convex hull cores with energy ratios below 0.05 is mentioned only in the caption; the text should describe this selection and state the affected sample sizes.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the filling-factor trends are empirical outputs of the simulations rather than identities built into the definitions.

full rationale

The paper's central chain is self-contained. Star-forming cores are selected in Section 2.4 by tracer particles that will accrete onto a given protostar within 0.3 Myr; convex hulls and filling factors are then computed geometrically (Eq. 12). The correlations in Figs. 7-10 (low filling factor -> more enclosed protostars, larger mass, larger R90) are measured from the simulation output, not fitted to those outputs. No parameter is tuned to reproduce the trend, and no prior result by the authors is used as the load-bearing justification for the correlation. The self-citations (Matsumoto et al. 2015 for the code and sink method; Koga et al. 2022 for tracer advection; Nozaki & Machida 2023 for isolated-core context) are methodological or contextual, not uniqueness theorems or ansatze smuggled in to force the claim. The 0.3 Myr look-ahead window is a free methodological parameter with no sensitivity test; that is a robustness caveat, not a circular reduction, because the window does not encode the filling-factor/protostar-count correlation. There is a mild built-in geometric coupling: filling factor uses the same hull volume that is later used to count protostars and mass, so large hulls tend to have low filling factor and contain more protostars; however, the paper's own examples show that low filling factor can occur without additional protostars and high-filling-factor multi-protostar hulls are not excluded by the definitions, so the trend is empirical rather than tautological. A separate proofreading error is noted: Section 6 item (iv) swaps the bound fractions (97% for M_rms=10 and 84% for M_rms=2) relative to Section 4.2 and the abstract; this does not affect circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a specific definition of star-forming cores via a 0.3 Myr accretion window and on tracer particles that are assumed to trace the gas perfectly. The two turbulence strengths are the only environmental parameters varied, and no fitted constants enter the analysis.

free parameters (3)
  • accretion lookahead window t_acc = 0.3 Myr
    Defines which tracer particles belong to a star-forming core. No sensitivity test is shown, and the results depend on this choice.
  • density floor for tracer volume construction = 100 cm^-3
    Cells below this density are excluded when converting tracer particles to volumes, affecting core volume and filling factor.
  • tracer particle mass M_particle = 0.001 M_sun
    Determined by total box mass divided by 3 million particles. This sets the spatial resolution scale of core identification.
assumptions (4)
  • standard math The hydrodynamic equations with sink particles and the chemical cooling/heating network are solved correctly by SFUMATO.
    The paper relies on the SFUMATO AMR code (Matsumoto et al. 2015) without presenting verification of the code itself.
  • domain assumption The initial conditions with uniform density and turbulent velocity field at Mach 2 or Mach 10 represent realistic molecular cloud clumps.
    The simulations use a 4 pc box with 3000 M_sun and no turbulence driving, which may not match all star-forming environments.
  • domain assumption Passive tracer particles perfectly follow the gas and are accreted by sink particles in the same way as the gas.
    The tracer advection scheme is second order, but numerical diffusion and the finite particle mass could cause slight deviations from true gas trajectories.
  • domain assumption Magnetic fields, protostellar outflows, and radiative feedback are negligible for the core properties studied.
    The authors state in Section 5.4 that these processes are ignored and that they may affect mass reservoirs and core stability.

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Pith. "Pith review of Tracking Star-Forming Cores as Mass Reservoirs in Clustered and Isolated Regions Using Numerical Passive Tracer Particles." pith.science (2026). https://pith.science/paper/CGQUUPPT

@misc{pith2026250102225,
  author       = {Pith},
  title        = {Pith review of: Tracking Star-Forming Cores as Mass Reservoirs in Clustered and Isolated Regions Using Numerical Passive Tracer Particles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CGQUUPPT}},
  note         = {Machine review of arXiv:2501.02225}
}
abstract

Understanding the physical properties of star-forming cores as mass reservoirs for protostars, and the impact of turbulence, is crucial in star formation studies. We implemented passive tracer particles in clump-scale numerical simulations with turbulence strengths of $\mathcal{M}_{\rm rms} = 2, 10$. Unlike core identification methods used in observational studies, we identified 260 star-forming cores using a new method based on tracer particles falling onto protostars. Our findings reveal that star-forming cores do not necessarily coincide with high-density regions when nearby stars are present, as gas selectively accretes onto protostars, leading to clumpy, fragmented structures. We calculated convex hull cores from star-forming cores and defined their filling factors. Regardless of turbulence strength, convex hull cores with lower filling factors tend to contain more protostars and have larger masses and sizes, indicating that cores in clustered regions are more massive and larger than those in isolated regions. Thus, the filling factor serves as a key indicator for distinguishing between isolated and clustered star-forming regions and may provide insights into the star formation processes within clustered regions. We also found that most convex hull cores are gravitationally bound. However, in the $\mathcal{M}_{\rm rms} = 10$ model, there are more low-mass, unbound convex hull cores compared to the $\mathcal{M}_{\rm rms} = 2$ model. In the $\mathcal{M}_{\rm rms} = 10$ model, 16% of the convex hull cores are unbound, which may be explained by the inertial-inflow model. These findings highlight the influence of turbulence strength on the mass and gravitational stability of cores.

Figures

Figures reproduced from arXiv: 2501.02225 by the authors.

Figure 1
Figure 1. Conceptual image of a star-forming core and a convex hull core. A star-forming core encloses a proto￾star that serves as the final destination for the gas within it. Convex hull cores and star-forming cores sometimes enclose more than one protostar. in the data output immediately after its formation was approximately 0.1 − 0.4 M⊙. ii. Calculate the radius ri,particle of a sphere repre￾senting the volume occupied by … view at source ↗
Figure 2
Figure 2. Column density of H2 molecules in each turbu￾lence strength. White dots mark the position of the pro￾tostars (or sink particles). The color represents the col￾umn density of H2 molecules, with brighter colors indicating higher densities. cores in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Column density around the protostar (cyan dot) (top) and corresponding column density of the identified star-forming cores (bottom). The color represents the column density of H2 molecules, with brighter colors indicating higher densities. The magenta crosses mark the position of surrounding protostars. When the other protostars are present in the regions, as seen in (d), (e) and (f), star-forming cores form numerou… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Column density around the protostar (cyan dot) and corresponding column density of the identified star￾forming cores, representing the gas accreting onto each pro￾tostar (cyan dot), displayed in panels (E1) and (E2). The color represents the column density of H2 molecu…
Figure 5
Figure 5. Figure 5: Histogram of the axis ratio γcore in the princi￾pal coordinates for 260 star-forming cores. The color indi￾cates the different density thresholds for the region where the axis ratios were calculated. The larger value of γcore(≡ λmax/λmin), the more asymmetric the struc…
Figure 6
Figure 6. Figure 6: Two examples of star-forming cores as mass reser￾voirs (pink dots) and their including convex hull cores’ edges (blue lines). The left panel shows a core with a low filling factor (11.3%), while the right panel shows a core with a high filling factor (63.3%). resent th…
Figure 8
Figure 8. Figure 8: Correlation between the mass within the convex hull volume (log10(M/M⊙)) and the filling factor (%). The left panel corresponds to the model with Mrms = 2, while the right panel corresponds to the model with Mrms = 10. The color scale indicates the number of convex hul…
Figure 9
Figure 9. Figure 9: Correlation between the radius enclosing 90% of the total mass of the convex hull core and the filling factor of convex hull cores. The blue dots correspond the model with Mrms = 2, and the red dots represent the model with Mrms = 10. In equation (13), α ≤ 1 means that…

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Works this paper leans on

64 extracted references · 19 canonical work pages

  1. [1]

    F., Lada, C

    Alves, J. F., Lada, C. J., & Lada, E. A. 2001, Nature, 409, 159 Andr´ e, P., Di Francesco, J., Ward-Thompson, D., et al. 2014, in Protostars and Planets VI, ed. H. Beuther, R. S

    work page 2001

  2. [2]

    Klessen, C. P. Dullemond, & T. Henning, 27, doi: 10.2458/azu uapress 9780816531240-ch002 Andr´ e, P., Men’shchikov, A., Bontemps, S., et al. 2010, A&A, 518, L102, doi: 10.1051/0004-6361/201014666

    doi:10.2458/azu 2010

  3. [3]

    2010, Testing IMF universality through the direct detection of low mass stars in starburst galaxies, NOAO Proposal ID 2010B-0189

    Bastian, N., Meyer, M., Greissl, J., & Seth, A. 2010, Testing IMF universality through the direct detection of low mass stars in starburst galaxies, NOAO Proposal ID 2010B-0189

    work page 2010

  4. [4]

    M., et al

    Battersby, C., Bally, J., Jackson, J. M., et al. 2010, ApJ, 721, 222, doi: 10.1088/0004-637X/721/1/222

    doi:10.1088/0004-637x/721/1/222 2010

  5. [6]

    2003, PASP, 115, 763, doi: 10.1086/376392

    Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392

    doi:10.1086/376392 2003

  6. [7]

    H.-H., Pineda, J

    Chen, H. H.-H., Pineda, J. E., Goodman, A. A., et al. 2019, ApJ, 877, 93, doi: 10.3847/1538-4357/ab1a40

    doi:10.3847/1538-4357/ab1a40 2019

  7. [8]

    C., Le, D., & Jimenez Vela, L

    Collins, D. C., Le, D., & Jimenez Vela, L. L. 2023, MNRAS, 520, 4194, doi: 10.1093/mnras/stac2834

    work page doi:10.1093/mnras/stac2834 2023

  8. [9]

    C., Le, D

    Collins, D. C., Le, D. K., & Jimenez Vela, L. L. 2024, MNRAS, 532, 681, doi: 10.1093/mnras/stae1493

    work page doi:10.1093/mnras/stae1493 2024

Show all 64 references
  1. [10]

    L., & Perault, M

    Falgarone, E., Puget, J. L., & Perault, M. 1992, A&A, 257, 715

    1992

  2. [11]

    Federrath, C., Krumholz, M., & Hopkins, P. F. 2017, in Journal of Physics Conference Series, Vol. 837, Journal of Physics Conference Series (IOP), 012007, doi: 10.1088/1742-6596/837/1/012007

    2017 doi

  3. [12]

    S., Schmidt, W., & Mac Low, M

    Federrath, C., Roman-Duval, J., Klessen, R. S., Schmidt, W., & Mac Low, M. M. 2010, A&A, 512, A81, doi: 10.1051/0004-6361/200912437

    2010 doi

  4. [13]

    2021, MNRAS, 506, 5512, doi: 10.1093MNRAS/stab2099 16

    Fukushima, H., & Yajima, H. 2021, MNRAS, 506, 5512, doi: 10.1093MNRAS/stab2099 16

    2021

  5. [14]

    2020, MNRAS, 497, 3830, doi: 10.1093/mnras/staa2062

    Fukushima, H., Yajima, H., Sugimura, K., et al. 2020, MNRAS, 497, 3830, doi: 10.1093/mnras/staa2062

    2020 doi

  6. [15]

    Gong, M., & Ostriker, E. C. 2015, ApJ, 806, 31, doi: 10.1088/0004-637X/806/1/31

    2015 doi

  7. [16]

    A., Rosolowsky, E

    Goodman, A. A., Rosolowsky, E. W., Borkin, M. A., et al. 2009, Nature, 457, 63, doi: 10.1038/nature07609 Haugbølle, T., Padoan, P., & Nordlund, ˚A. 2018, ApJ, 854, 35, doi: 10.3847/1538-4357/aaa432

    2009 doi

  8. [17]

    Hirano, S., Tsukamoto, Y., Basu, S., & Machida, M. N. 2020, ApJ, 898, 118, doi: 10.3847/1538-4357/ab9f9d

    2020 doi

  9. [18]

    2015, A&A, 580, A49, doi: 10.1051/0004-6361/201425584

    Inutsuka, S.-i., Inoue, T., Iwasaki, K., & Hosokawa, T. 2015, A&A, 580, A49, doi: 10.1051/0004-6361/201425584

    2015 doi

  10. [19]

    2005, AJ, 130, 2166, doi: 10.1086/444619

    Kandori, R., Nakajima, Y., Tamura, M., et al. 2005, AJ, 130, 2166, doi: 10.1086/444619

    2005 doi

  11. [20]

    2019, ApJ, 874, 147, doi: 10.3847/1538-4357/ab0c08

    Kerr, R., Kirk, H., Di Francesco, J., et al. 2019, ApJ, 874, 147, doi: 10.3847/1538-4357/ab0c08

    2019 doi

  12. [21]

    D., Marocco, F., Gelino, C

    Kirkpatrick, J. D., Marocco, F., Gelino, C. R., et al. 2024, ApJS, 271, 55, doi: 10.3847/1538-4365/ad24e2

    2024 doi

  13. [22]

    Koga, S., Kawasaki, Y., & Machida, M. N. 2022, MNRAS, 515, 6073, doi: 10.1093MNRAS/stac2115

    2022

  14. [23]

    C., Wakelam, V., & Sipil¨ a, O

    Kong, S., Caselli, P., Tan, J. C., Wakelam, V., & Sipil¨ a, O. 2015, ApJ, 804, 98, doi: 10.1088/0004-637X/804/2/98 K¨ onyves, V., Andr´ e, P., Men’shchikov, A., et al. 2010, A&A, 518, L106, doi: 10.1051/0004-6361/201014689 —. 2015, A&A, 584, A91, doi: 10.1051/0004-6361/2015258...

    2015 doi

  15. [24]

    2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x

    Kroupa, P. 2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x

    2001

  16. [25]

    Inutsuka, S. I. 2020, A&A, 642, A87, doi: 10.1051/0004-6361/202038232

    2020 doi

  17. [26]

    2020, A&A, 638, A74, doi: 10.1051/0004-6361/201936442 Mac Low, M.-M

    Ladjelate, B., Andr´ e, P., K¨ onyves, V., et al. 2020, A&A, 638, A74, doi: 10.1051/0004-6361/201936442 Mac Low, M.-M. 1999, ApJ, 524, 169, doi: 10.1086/307784 Mac Low, M.-M., & Klessen, R. S. 2004, Reviews of Modern Physics, 76, 125, doi: 10.1103/RevModPhys.76.125

    2020 doi

  18. [27]

    N., & Hosokawa, T

    Machida, M. N., & Hosokawa, T. 2013, MNRAS, 431, 1719, doi: 10.1093MNRAS/stt291

    2013

  19. [28]

    N., & Matsumoto, T

    Machida, M. N., & Matsumoto, T. 2012, MNRAS, 421, 588, doi: 10.1111/j.1365-2966.2011.20336.x

    2012

  20. [29]

    A., Kirk, J

    Marsh, K. A., Kirk, J. M., Andr´ e, P., et al. 2016, MNRAS, 459, 342, doi: 10.1093/mnras/stw301

    2016 doi

  21. [30]

    2007, PASJ, 59, 905, doi: 10.1093/pasj/59.5.905

    Matsumoto, T. 2007, PASJ, 59, 905, doi: 10.1093/pasj/59.5.905

    2007 doi

  22. [31]

    2015, ApJ, 801, 77, doi: 10.1088/0004-637X/801/2/77

    Matsumoto, T., Dobashi, K., & Shimoikura, T. 2015, ApJ, 801, 77, doi: 10.1088/0004-637X/801/2/77

    2015 doi

  23. [32]

    F., & Ostriker, J

    McKee, C. F., & Ostriker, J. P. 1977, ApJ, 218, 148, doi: 10.1086/155667

    1977 doi

  24. [33]

    F., & Tan, J

    McKee, C. F., & Tan, J. C. 2003, ApJj, 585, 850, doi: 10.1086/346149

    2003 doi

  25. [34]

    R., Teyssier, R., & Draine, B

    Moseley, E. R., Teyssier, R., & Draine, B. T. 2023, MNRAS, 518, 2825, doi: 10.1093MNRAS/stac3231

    2023

  26. [35]

    2001, A&A, 365, 440, doi: 10.1051/0004-6361:20000072

    Motte, F., & Andr´ e, P. 2001, A&A, 365, 440, doi: 10.1051/0004-6361:20000072

    2001 doi

  27. [36]

    1998, A&A, 336, 150

    Motte, F., Andre, P., & Neri, R. 1998, A&A, 336, 150

    1998

  28. [37]

    Nozaki, S., & Machida, M. N. 2023, MNRAS, 519, 5017, doi: 10.1093/mnras/stac3819

    2023 doi

  29. [38]

    2007, MNRAS, 374, 1413, doi: 10.1111/j.1365-2966.2006.11246.x

    Nutter, D., & Ward-Thompson, D. 2007, MNRAS, 374, 1413, doi: 10.1111/j.1365-2966.2006.11246.x

    2007

  30. [39]

    Offner, S. S. R., Taylor, J., Markey, C., et al. 2022, MNRAS, 517, 885, doi: 10.1093MNRAS/stac2734

    2022

  31. [40]

    2002, ApJ, 575, 950, doi: 10.1086/341347

    Fukui, Y. 2002, ApJ, 575, 950, doi: 10.1086/341347

    2002 doi

  32. [41]

    2014, ApJ, 797, 32, doi: 10.1088/0004-637X/797/1/32

    Padoan, P., Haugbølle, T., & Nordlund, ˚A. 2014, ApJ, 797, 32, doi: 10.1088/0004-637X/797/1/32

    2014 doi

  33. [42]

    2002, ApJ, 576, 870, doi: 10.1086/341790

    Padoan, P., & Nordlund, ˚A. 2002, ApJ, 576, 870, doi: 10.1086/341790

    2002 doi

  34. [43]

    G., Norman, M

    Padoan, P., Nordlund, ˚A., Kritsuk, A. G., Norman, M. L., & Li, P. S. 2007, ApJ, 661, 972, doi: 10.1086/516623

    2007 doi

  35. [44]

    2020, ApJ, 900, 82, doi: 10.3847/1538-4357/abaa47

    Padoan, P., Pan, L., Juvela, M., Haugbølle, T., & Nordlund, ˚A. 2020, ApJ, 900, 82, doi: 10.3847/1538-4357/abaa47

    2020 doi

  36. [45]

    2013, A&A, 550, A38, doi: 10.1051/0004-6361/201220500

    Palmeirim, P., Andr´ e, P., Kirk, J., et al. 2013, A&A, 550, A38, doi: 10.1051/0004-6361/201220500

    2013 doi

  37. [46]

    2016, ApJ, 825, 30, doi: 10.3847/0004-637X/825/1/30

    Pan, L., Padoan, P., Haugbølle, T., & Nordlund, ˚A. 2016, ApJ, 825, 30, doi: 10.3847/0004-637X/825/1/30

    2016 doi

  38. [47]

    M., Padoan, P., Haugbølle, T., & Nordlund, ˚A

    Pelkonen, V. M., Padoan, P., Haugbølle, T., & Nordlund, ˚A. 2021, MNRAS, 504, 1219, doi: 10.1093MNRAS/stab844

    2021

  39. [48]

    M., Simon, R., & Jackson, J

    Rathborne, J. M., Simon, R., & Jackson, J. M. 2007, ApJ, 662, 1082, doi: 10.1086/513178

    2007 doi

  40. [49]

    2022, ApJ, 941, 168, doi: 10.3847/1538-4357/ac9d8b

    Redaelli, E., Chac´ on-Tanarro, A., Caselli, P., et al. 2022, ApJ, 941, 168, doi: 10.3847/1538-4357/ac9d8b

    2022 doi

  41. [50]

    Salpeter, E. E. 1955, ApJ, 121, 161, doi: 10.1086/145971

    1955 doi

  42. [51]

    2023, ApJ, 944, 92, doi: 10.3847/1538-4357/aca7c9

    Sato, A., Takahashi, S., Ishii, S., et al. 2023, ApJ, 944, 92, doi: 10.3847/1538-4357/aca7c9

    2023 doi

  43. [52]

    2015, MNRAS, 452, 2410, doi: 10.1093/mnras/stv1458

    Seifried, D., & Walch, S. 2015, MNRAS, 452, 2410, doi: 10.1093/mnras/stv1458

    2015 doi

  44. [53]

    2019, A&A, 632, A83, doi: 10.1051/0004-6361/201935689

    Shimajiri, Y., Andr´ e, P., Ntormousi, E., et al. 2019, A&A, 632, A83, doi: 10.1051/0004-6361/201935689

    2019 doi

  45. [54]

    D., Friesen, R

    Singh, A., Matzner, C. D., Friesen, R. K., et al. 2021, ApJ, 922, 87, doi: 10.3847/1538-4357/ac20d2

    2021 doi

  46. [55]

    M., Clarke, G

    Smith, D. M., Clarke, G. P., & Harland, K. 2009, Environment and Planning A: Economy and Space, 41, 1251, doi: 10.1068/a4147

    2009 doi

  47. [56]

    A., Kratter, K

    Smullen, R. A., Kratter, K. M., Offner, S. S. R., Lee, A. T., & Chen, H. H.-H. 2020, MNRAS, 497, 4517, doi: 10.1093/mnras/staa2253

    2020 doi

  48. [57]

    2021a, ApJL, 910, L6, doi: 10.3847/2041-8213/abe7dd 17

    Takemura, H., Nakamura, F., Kong, S., et al. 2021a, ApJL, 910, L6, doi: 10.3847/2041-8213/abe7dd 17

    doi

  49. [58]

    2021b, PASJ, 73, 487, doi: 10.1093/pasj/psab014

    Takemura, H., Nakamura, F., Ishii, S., et al. 2021b, PASJ, 73, 487, doi: 10.1093/pasj/psab014

    doi

  50. [59]

    1993, ApJ, 404, 643, doi: 10.1086/172318

    Tatematsu, K., Umemoto, T., Kameya, O., et al. 1993, ApJ, 404, 643, doi: 10.1086/172318

    1993 doi

  51. [60]

    2020, ApJ, 899, 10, doi: 10.3847/1538-4357/ab9ca7

    Tokuda, K., Fujishiro, K., Tachihara, K., et al. 2020, ApJ, 899, 10, doi: 10.3847/1538-4357/ab9ca7

    2020 doi

  52. [61]

    2022, ApJ, 933, 20, doi: 10.3847/1538-4357/ac6b3c

    Tokuda, K., Minami, T., Fukui, Y., et al. 2022, ApJ, 933, 20, doi: 10.3847/1538-4357/ac6b3c

    2022 doi

  53. [62]

    Tokuda, K., Harada, N., Tanaka, K. E. I., et al. 2023, ApJ, 955, 52, doi: 10.3847/1538-4357/acefb7

    2023 doi

  54. [63]

    2010, ApJ, 709, 27, doi: 10.1088/0004-637X/709/1/27

    Wang, P., Li, Z.-Y., Abel, T., & Nakamura, F. 2010, ApJ, 709, 27, doi: 10.1088/0004-637X/709/1/27

    2010 doi

  55. [64]

    2007, in Protostars and Planets V, ed

    Ward-Thompson, D., Andr´ e, P., Crutcher, R., et al. 2007, in Protostars and Planets V, ed. B. Reipurth, D. Jewitt, & K. Keil, 33, doi: 10.48550/arXiv.astro-ph/0603474

    2007 doi arXiv

  56. [65]

    P., de Geus, E

    Williams, J. P., de Geus, E. J., & Blitz, L. 1994, ApJ, 428, 693, doi: 10.1086/174279

    1994 doi

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