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Fragmentation and filaments at the onset of star and cluster formation: SABOCA 350 $\mu$m view of ATLASGAL selected massive clumps

T0 review · 2 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read The most massive fragment inside a clump scales almost one-to-one with clump mass, a sign that self-gravity shapes small-scale structure.

desk verdict A careful, useful census with a genuinely new target list, but the headline self-gravity interpretation of the M_mmf–M_clump correlation is shakier than the abstract lets on. read the letter →

arxiv 1909.01011 v1 pith:ZT5ZOUBM submitted 2019-09-03 astro-ph.GA astro-ph.SR

Yuxin Lin , Timea Csengeri , Friedrich Wyrowski , James S. Urquhart , Frederic Schuller , Axel Weiss , Karl M. Menten This is my paper · ORCID
classification astro-ph.GAastro-ph.SR
keywords massivestarformationclumpfragmentationpre-stellarcoressubmillimeterobservationsSABOCA350micronATLASGALclumpsJeansfilamentarystructure
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 asks what sets the sub-parsec structure of massive star-forming clumps: why some clumps break into a few heavy cores while others fragment into many light ones. Using APEX/SABOCA 350 μm maps at 8.5 arcsecond resolution toward more than 200 ATLASGAL clumps, the authors resolve 1120 compact sources and derive dust temperatures, masses, and luminosities for 971 of them through pixel-by-pixel SED fitting with Herschel and ATLASGAL data. They find that the mass of the most massive fragment tracks the parent clump mass almost one-to-one, with $\log M_{\rm mmf} = 0.96 \log M_{\rm clump} - 1.18$ (about 30 percent of the clump mass), a strong correlation they read as evidence that clump self-gravity governs core-scale structure. They also report that fragment number correlates only moderately with gas density and with the Jeans-fragmentation prediction, and that fragments are 10-100 times the thermal Jeans mass. Finally, they identify 27 massive quiescent cores above 100 solar masses within 5 kpc as the best current candidates for massive pre-stellar cores, while cautioning that deeply embedded protostars could masquerade as quiescent.

What carries the argument

The load-bearing object is the empirical relation between the most massive fragment mass and the clump mass, $M_{\rm mmf} = 0.31\,M_{\rm clump}^{0.96}$, together with the data chain that produces it. That chain consists of SABOCA 350 μm maps at 8.5 arcsecond resolution, Fourier-combined with Herschel SPIRE 350 μm data to recover extended emission; pixel-by-pixel modified-blackbody SED fits at 10 arcsecond resolution that convert 70-870 μm fluxes into dust temperature, column density, and mass; Gaussclumps, a Gaussian-decomposition source extractor, to define the compact cores; and positional matching to 22-24 μm and 70 μm point-source catalogs to separate star-forming from quiescent cores. The relation carries the self-gravity argument because it ties the parent reservoir to its most massive product across two orders of magnitude in clump mass.

What would settle it

Image the 27 massive quiescent cores with sub-arcsecond interferometry and search for outflow wings, hot-core tracers, or compact continuum sources; widespread detections would falsify the pre-stellar interpretation. A complementary test would re-derive the $M_{\rm mmf}$-$M_{\rm clump}$ correlation after convolving every map to a common physical resolution, since a slope well below 0.96 would weaken the self-gravity claim.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a scaling law connecting the two ends of the fragmentation hierarchy: inside massive clumps, the most massive fragment obeys $M_{\rm mmf} = 0.31\,M_{\rm clump}^{0.96}$ in the 2-4 kpc distance-limited sample (Spearman coefficient 0.92, p < 0.001), meaning roughly 30 percent of the clump mass is already assembled in its single heaviest core. The same analysis finds that the number of fragments correlates only moderately with clump density (Spearman ~0.41) and with the number of thermal Jeans masses (0.31-0.40), and that typical fragment masses exceed the thermal Jeans mass by 10-100 times. The companion result is a census of 27 quiescent cores with $M > 100\,M_\odot$ at distances below 5 kpc that show no compact 22-24 μm or 70 μm counterparts; the paper presents them as promising candidates for massive pre-stellar cores, while explicitly leaving open the alternative that they are deeply embedded high-mass protostars.

Load-bearing premise

The 27 pre-stellar candidates are defined by the absence of a compact 24 or 70 micron infrared source, yet a deeply embedded massive protostar can be hidden at exactly those wavelengths, so a core without such a counterpart is not guaranteed to be starless.

Editorial extensions

If this is right

  • Because the most massive fragment holds roughly 30 percent of the clump mass, the clump mass sets the upper end of the core mass function and, plausibly, the mass of the cluster that forms.
  • The scarcity of massive quiescent cores above 100 solar masses (27 in the sample) implies that such dense structures either collapse quickly or quickly become visible in the mid-infrared.
  • Fragment spacing is broadly consistent with thermal Jeans lengths, but individual fragments are 10 to 100 times the thermal Jeans mass, so turbulent or self-gravitating support must set the fragment masses.
  • Quiescent and star-forming cores have similar masses but different surface densities and temperatures, supporting a picture where the quiescent cores are the same objects at an earlier stage along one evolutionary path.
  • The steepening of the mass-radius relation toward smaller scales (slope near 3 rather than 2) points to self-gravity becoming dominant from clump to core scales.

Reading between the lines

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

  • If the $M_{\rm mmf}$-$M_{\rm clump}$ relation holds at fixed physical resolution, it would give observers a predictor: the most massive star in a young cluster could be anticipated from the clump mass, much as the $m_{\rm max}$-$M_{\rm ecl}$ relation anticipates the most massive star from cluster mass; the paper compares the two but does not claim equality.
  • Because the sample is selected to be weak or dark at 24 μm, the correlations are measured on an early-stage population; extending the same analysis to more evolved, mid-infrared-bright clumps would show whether the 30 percent fraction changes with evolution.
  • Smoothing all maps to a common physical resolution before measuring $M_{\rm mmf}$ would test whether part of the correlation is simply a distance and resolution artifact, since more distant clumps are seen with worse linear resolution.
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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

2 major / 4 minor

Summary. This paper reports SABOCA 350 μm observations of 204 ATLASGAL massive clumps, extracts 1120 compact sources with Gaussclumps, and derives clump and core physical properties from pixel-by-pixel SED fitting using Herschel, LABOCA, and SABOCA data. The authors classify cores as star-forming or quiescent using 22/24 μm and 70 μm point-source associations, analyze fragmentation levels as a function of clump properties, and identify 27 massive (>100 M_sun) quiescent cores within 5 kpc as candidate massive pre-stellar cores or deeply embedded high-mass protostars. The main quantitative claims are a moderate correlation of fragment number with clump density and Jeans number, and a strong correlation between the most massive fragment mass and clump mass, log M_mmf = 0.96 log M_clump - 1.18 (Spearman rho = 0.92), which the authors interpret as evidence that self-gravity plays an important role in clump-scale structure formation.

Significance. The paper delivers a valuable observational dataset: a homogeneous 350 μm survey of 204 clumps, SED-based masses and luminosities for 971 cores, and a public machine-readable catalog. The empirical correlations are benchmarked against an independent dendrogram extraction in Appendix C, which strengthens the credibility of the source-extraction-dependent results. The 27 massive quiescent cores constitute a useful target list for high-resolution follow-up with ALMA. However, the physical interpretation of the headline M_mmf–M_clump correlation as evidence for self-gravity is not uniquely supported by the present analysis, because both masses share a common d^2 scaling and the most massive fragment is by construction a sub-structure of the clump. The quiescent classification of the massive cores also carries a contamination risk that the paper itself partly documents. These issues are addressable with additional null tests and more cautious wording, so the paper is suitable for major revision rather than rejection.

major comments (2)
  1. [Sec. 5.3.1, Fig. 15, Eq. (2), App. C] The headline correlation log M_mmf = 0.96 log M_clump - 1.18 with Spearman rho = 0.92 is interpreted as evidence that self-gravity sets the clump-to-core mass relation, but two built-in effects are not separated. First, Eq. (2) gives both M_clump and M_mmf as M = mu m_H N(H2) A d^2, so even within the 2-4 kpc distance bin both quantities share a common d^2 scaling; a factor of 2 in distance produces a spread of ~0.6 dex in log d^2 and will push a log-log correlation toward a slope near unity even if no intrinsic mass-mass relation exists. The Spearman coefficient is rank-based and does not remove this common scaling. Second, M_mmf is by construction the largest sub-structure inside M_clump; Appendix C itself notes that the relative size difference between parent and child structures 'together lead again to the tight correlation between the mass of the most massive fragment and the parental clump mass,' illustrating the definitional component. The dendrogram cross-check in App. C inherits both the distance scaling and the subset relation, so it does not serve as a null test. To support the self-gravity interpretation, please report a partial Spearman correlation controlling for distance, a narrow-distance-bin fit (e.g., 2.5-3.5 kpc), or a null model in which fragment masses are randomly assigned to clumps of the observed mass function. Without such a test, the claim should be stated as a correlation whose physical origin requires further investigation, not as direct evidence for self-gravity.
  2. [Sec. 4.4 and Sec. 5.4, Table 5] The identification of 27 massive quiescent cores as candidates for massive pre-stellar cores rests on the absence of compact 24/22 μm and 70 μm counterparts. This classification criterion is not sufficient to exclude deeply embedded high-mass protostars, which can be invisible at these wavelengths; the paper itself notes in Sec. 5.4 that deeply embedded high-mass protostars have been detected toward mid-infrared quiet massive cores. The ALMA follow-up of two candidates, G333.1298-0.5602 and G333.4659-0.1641, shows that the brightest sub-arcsecond fragments are high-mass protostellar objects rather than pre-stellar cores. If a comparable fraction of the remaining candidates are protostellar, the claim of a population of massive pre-stellar cores would be substantially weakened. Please quantify the possible contamination using the derived 70 μm opacities and the sensitivity limits of the mid-infrared catalogs, clearly mark the ALMA-confirmed sources in Table 5, and adjust the strength of the conclusion accordingly.
minor comments (4)
  1. [Fig. 15 and Fig. C.3 captions] The fitted relation is written as 'M_fragments = 0.96 log M_clumps - 1.18'; since the horizontal axis is logarithmic, the left-hand side should be log M_mmf (and similarly in Fig. C.3).
  2. [Sec. 5.3.1 and Fig. 14] The quantity Nmm is used repeatedly but never defined; please define the acronym or replace it with a descriptive symbol such as N_frag.
  3. [Table 5] The symbols in the SiO and HCO+/H13CO+ columns (a, b, c, d, e, y, '-') are only partially explained in the table notes; please give a complete legend so the table can stand alone.
  4. [Sec. 3.1] The statement that 67% of fields exhibit filamentary(-like) morphology is based on visual inspection; please state this explicitly and, if possible, provide a simple quantitative criterion or acknowledge that the fraction is a visual estimate.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's correlations are observational measurements, not fitted inputs renamed as predictions.

full rationale

The paper's headline results are measurements, not fits masquerading as predictions. Clump and fragment masses are independently computed from SED fits to different-resolution maps (Secs. 4.1 and 4.2) using Eq. 2, and the M_mmf-M_clump correlation (Sec. 5.3.1, Fig. 15) is then compared, not used as an input, to theoretical expectations. The paper does not fit a parameter to a subset of data and call the result a prediction; no result is defined in terms of another result; and no load-bearing claim rests on a self-citation. The two Csengeri et al. citations used in Sec. 5.4 actually provide ALMA evidence that some quiescent-core candidates are protostellar, which weakens rather than forces the paper's candidate claim, so self-citation is not being used to impose the conclusion. The distance-limited 2-4 kpc subsample and the independent dendrogram benchmark in App. C are genuine attempts to address resolution, distance, and extraction-method biases. The shared d^2 term in Eq. 2 and the fact that the most massive fragment is a sub-region of the parent clump are legitimate statistical cautions about the physical interpretation of the correlation, but they are not circular derivation: no equation sets M_mmf equal to a fitted function of M_clump, and a Spearman rank correlation is not an algebraic identity forced by the common distance factor. Under the hard rules, this is a no-circularity finding.

Assumptions & free parameters 2 free parameters · 7 assumptions · 0 invented entities

The central claims rest on standard observational calibrations (dust opacity, distances) and on classification and source-extraction choices. No new physical entities are introduced. The 70 micron rescaling is the most paper-specific assumption, and the quiescence classification is the most fragile for the pre-stellar core candidates.

free parameters (2)
  • Gaussclumps stiffness parameters (s0, sc, sa) = (1, 1, 10)
    Chosen by tests to recover prominent structures while suppressing spurious low-S/N sources (Sec. 3.2). These affect the number and shape of identified cores, hence all derived fragmentation statistics.
  • Initial FWHM guess for Gaussclumps = 1.1 x beam (9.35 arcsec)
    Set to 1.1 times the 8.5 arcsec resolution (Sec. 3.2); this sets the minimum resolvable core size and influences the core flux and mass distributions.
assumptions (7)
  • domain assumption Emission from dust can be described by a single-temperature modified blackbody, I_nu = B_nu(T_d)(1 - exp(-tau_nu)) (Eq. 1).
    Assumes a single dust temperature and optically thin or moderate opacity along each line of sight; the authors note beta and T_d are anti-correlated, adding uncertainty to mass estimates (Sec. 4.1).
  • domain assumption Dust opacity law kappa_nu = 0.1 (nu/1000 GHz)^1.8 cm2/g, gas-to-dust ratio of 100, and mean molecular weight 2.8.
    Adopted from Herschel Gould Belt, HOBYS, and Hi-Gal programs; the authors state mass estimates may vary by a factor 2-3 with opacity and beta (Sec. 4.1).
  • ad hoc to paper The four bands 160, 250, 350, and 870 micron trace the cold dust component, so the 70 micron flux can be rescaled by the ratio of observed to extrapolated 70 micron flux.
    This 70 micron scaling procedure (Sec. 4.2) is introduced specifically to correct for a hot component; the paper shows the correction changes column densities by up to about 50 percent.
  • domain assumption Absence of a compact 24/22 micron or 70 micron counterpart identifies a quiescent core.
    Used for the star-forming versus quiescent classification (Sec. 4.4); the authors check extinction but note deeply embedded protostars can evade detection, and ALMA follow-up of two candidates found protostars.
  • domain assumption Kinematic distances from Urquhart et al. (2018) are accurate.
    All physical sizes, masses, and luminosities scale with distance squared; distance errors directly propagate into the correlations (Sec. 2.1).
  • domain assumption Thermal Jeans fragmentation estimates computed from clump-averaged temperature and density are the relevant predictor of the observed number of fragments.
    Used to compute N_Jeans = M_clump/M_Jeans; observed core masses exceed the thermal Jeans mass by 10-100, so the comparison is only about counts, not masses (Sec. 5.3.1).
  • domain assumption The fragmentation temperature is assumed to be 20 K when computing the Jeans number for the stronger correlation.
    A fixed temperature of 20 K is used as the assumed clump temperature when fragmentation happened, yielding a stronger correlation with the observed fragment number (Sec. 5.3.1, Fig. 14).

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Cite this review

Pith. "Pith review of Fragmentation and filaments at the onset of star and cluster formation: SABOCA 350 $\mu$m view of ATLASGAL selected massive clumps." pith.science (2026). https://pith.science/paper/ZT5ZOUBM

@misc{pith2026190901011,
  author       = {Pith},
  title        = {Pith review of: Fragmentation and filaments at the onset of star and cluster formation: SABOCA 350 $\mu$m view of ATLASGAL selected massive clumps},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZT5ZOUBM}},
  note         = {Machine review of arXiv:1909.01011}
}
abstract

The structure formation of the dense interstellar material and the fragmentation of clumps into cores is a fundamental step to understand how stars and stellar clusters form. We aim to establish a statistical view of clump fragmentation at sub-parsec scales based on a large sample of massive clumps selected from the ATLASGAL survey. We used the APEX/SABOCA camera at 350 $\mu$m to image clumps at a resolution of 8.$''$5. The majority of the sample consists of massive clumps that are weak or in absorption at 24 $\mu$m. We resolve rich filamentary structures and identify the population of compact sources. We use association with mid-infrared 22-24 $\mu$m and 70 $\mu$m point sources to pin down the star formation activity of the cores. We then statistically assess their physical properties, and the fragmentation characteristics of massive clumps. We find a moderate correlation between the clump fragmentation levels with the clump gas density and the predicted number of fragments with pure Jeans fragmentation scenario; we find a strong correlation between the mass of the most massive fragment and the total clump mass, suggesting that the self-gravity may play an important role in the clumps' small scale structure formation. We identify 27 massive quiescent cores with $M_{\rm core}>100$ M$_{\odot}$ within 5 kpc; these are massive enough to be self-gravitating but do not yet show any sign of star-formation. This sample comprises, therefore, promising candidates of massive pre-stellar cores, or deeply embedded high-mass protostars.

Figures

Figures reproduced from arXiv: 1909.01011 by the authors.

Figure 1
Figure 1. Distribution of the integrated flux density and bolomet￾ric luminosity-to-mass ratios (L/M) of all ATLASGAL sources (in blue filled contours, the darker region corresponds to higher density) (Urquhart et al. 2018), and the target sources observed with SABOCA (gray pluses and dots) at different distance ranges. The histograms in the right side and top panel show the 1-dimensional distributions of in￾tegrated flux den… view at source ↗
Figure 2
Figure 2. Distance distribution of the observed ATLASGAL clumps. The vertical dashed line indicates the 5 kpc distance. The filled green region indicates 2-4 kpc range. We show the distance distribution of all sources in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. 350 µm peak flux density versus the 1σ rms noise level observed with SABOCA towards all fields. The histogram on the right side, and the top panel show the distribution of peak flux densities and the noise levels, respectively. We complement the HiGAL data with the combined ATLAS￾GAL and Planck-HFI 870 µm data (Csengeri et al. 2016b). This data product has a considerably larger spatial dynamic range than the ATLASGA… view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Top panels: Examples of 350 µm emission towards ATLASGAL selected massive clumps obtained with APEX/SABOCA. Contour levels start at 5σ and show 5 uniformly spaced intervals on a logarithmic scale up to the peak flux density in each field. The green crosses show the pos…
Figure 6
Figure 6. Figure 6: An additional complication is that 10 of the brightest sources are saturated in the SPIRE 350 µm images. We correct for this by interpolating between the saturated pixels assuming a 2- dimensional Gaussian flux distribution around the center. We find that the interpola…
Figure 5
Figure 5. Figure 5: Top panels: Dust temperature (Td) and H2 column density (NH2 ) maps obtained with a pixel-by-pixel SED fitting of PACS 160 µm, SPIRE 250, 350 µm and combined LABOCA and Planck 870 µm data at 2500 resolution. Bottom panels: As top panels but using the PACS 70 µm and the…
Figure 6
Figure 6. Figure 6: Top panel: Transmission of the Herschel/SPIRE and the APEX/SABOCA instruments. Bottom panel: Color correction factors for SABOCA (dashed line) and SPIRE (solid line) as a function of the spectral index of the source. The dash-dotted line shows the conversion factor to …
Figure 7
Figure 7. Figure 7: Distribution of the physical properties of all the observed ATLASGAL clumps and compact sources identified at 350 µm with SABOCA. Top left: The mass distribution of clumps and SABOCA compact sources. The vertical lines show mass limits of 40 M and 650 M . Top right: Di…
Figure 8
Figure 8. Figure 8: Left: Distribution of angular distances between SABOCA sources and their MIR compact source or/and Hii region associations. In each bin, the probability of dN/N is normalised by the annular area defined by adjacent angular bins as dN/(N×Area); Right: Distribution of di…
Figure 9
Figure 9. Figure 9: Distribution of the physical properties of star-forming and quiescent SABOCA compact sources in the 2-4 kpc distance bin, in red and blue color, respectively. Top left: The temperature distribution of two categories. Top right: The mass distribution. Bottom left: The l…
Figure 10
Figure 10. Figure 10: Mass-radius diagram of SABOCA compact sources. The quies￾cent cores and star-forming cores are indicated with blue and red colors, in 2-4 kpc (hollow markers) and 1-2 kpc (filled markers) distance bins. The dashed lines show the constant volume density of 104 , 105 an…
Figure 11
Figure 11. Figure 11: Luminosity-mass diagram of SABOCA compact sources. The quiescent cores and star-forming cores are indicated with blue and red colors, in 2-4 kpc (hollow markers) and 1-2 kpc (filled markers) distance bins. The empirical evolutionary tracks for massive clumps of envelo…
Figure 12
Figure 12. Figure 12: Fragment mass distribution of all SABOCA sources. The fit￾ted power-law index of the all the sources at < 5 kpc above our mass completeness (> 41.4 M ) is indicated by the green vertical line. Article number, page 13 of 53 [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: Example of fragment allocation criterion towards an example SABOCA mapping field. The left panel shows the 870 µm LABOCA emission in grayscale, overlaid with ATLASGAL clumps in green dashed ellipses. The middle panel shows the 350 µm SABOCA emission in grayscale with …
Figure 14
Figure 14. Figure 14: Fragmentation level as a function of clump properties of sources located at a distance in 2-4 kpc. In each plot, data points are color￾coded according to the clump size. Gray triangles show the exact fragment number exact Nmm; colored dots show the rounding fragment n…
Figure 15
Figure 15. Figure 15: Most massive fragment mass as a function of clump properties for sources at a distance 2-4 kpc (dots) and 1-2 kpc (triangles). Left: Mass of the most massive fragment as a function of clump luminosity-to-mass ratio. Vertical lines mark the luminosity-to-mass ratio of …
Figure 16
Figure 16. Figure 16: Example of a clump hosting massive quiescent cores. White crosses mark the >100 M quiescent cores and blue cross marks the star-forming cores. The masses for quiescent cores are indicated in the figure. Contour levels are from 7σ to the peak flux with 6 logarithmic li…

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Works this paper leans on

202 extracted references · 68 canonical work pages

  1. [1]

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

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    E., Ginsburg , A

    Aguirre , J. E., Ginsburg , A. G., Dunham , M. K., et al. 2011, The Astrophysical Journal Supplement Series, 192, 4

    2011

  4. [4]

    2014, Protostars and Planets VI, 27

    Andr \'e , P., Di Francesco , J., Ward-Thompson , D., et al. 2014, Protostars and Planets VI, 27

    2014

  5. [5]

    2010, , 518, L102

    Andr \'e , P., Men'shchikov , A., Bontemps , S., et al. 2010, , 518, L102

    2010

  6. [6]

    2008, , 490, L27

    Andr \'e , P., Minier , V., Gallais , P., et al. 2008, , 490, L27

    2008

  7. [7]

    2016, , 592, A54

    Andr \'e , P., Rev \'e ret , V., K \"o nyves , V., et al. 2016, , 592, A54

    2016

  8. [8]

    M., Sip o cz , B

    Astropy Collaboration , Price-Whelan , A. M., Sip o cz , B. M., et al. 2018, , 156, 123

    2018

Show all 202 references
  1. [9]

    & Mac Low , M.-M

    Ballesteros-Paredes , J. & Mac Low , M.-M. 2002, , 570, 734

    2002

  2. [10]

    C., Ahmadi , A., et al

    Beuther , H., Mottram , J. C., Ahmadi , A., et al. 2018, , 617, A100

    2018

  3. [11]

    2010, , 524, A18

    Bontemps , S., Motte , F., Csengeri , T., & Schneider , N. 2010, , 524, A18

    2010

  4. [12]

    Butler , M. J. & Tan , J. C. 2012, , 754, 5

    2012

  5. [13]

    2016, , 833, 113

    Camacho , V., V \'a zquez-Semadeni , E., Ballesteros-Paredes , J., et al. 2016, , 833, 113

    2016

  6. [14]

    F., Harvey , P

    Campbell , M. F., Harvey , P. M., Lester , D. F., & Clark , D. M. 2004, , 600, 254

    2004

  7. [15]

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

    Carey , S. J., Noriega-Crespo , A., Mizuno , D. R., et al. 2009, , 121, 76

    2009

  8. [16]

    V., Zhang , Q., Wright , M

    Chen , H.-R. V., Zhang , Q., Wright , M. C. H., et al. 2019, , 875, 24

    2019

  9. [17]

    C., Liu , M., et al

    Cheng , Y., Tan , J. C., Liu , M., et al. 2018, , 853, 160

    2018

  10. [18]

    1999, , 343, 585

    Codella , C., Bachiller , R., & Reipurth , B. 1999, , 343, 585

    1999

  11. [19]

    C., Kritsuk , A

    Collins , D. C., Kritsuk , A. G., Padoan , P., et al. 2012, , 750, 13

    2012

  12. [20]

    2011, , 740, L5

    Csengeri , T., Bontemps , S., Schneider , N., et al. 2011, , 740, L5

    2011

  13. [21]

    2018, , 617, A89

    Csengeri , T., Bontemps , S., Wyrowski , F., et al. 2018, , 617, A89

    2018

  14. [22]

    2017 a , , 601, A60

    Csengeri , T., Bontemps , S., Wyrowski , F., et al. 2017 a , , 601, A60

    2017

  15. [23]

    2017 b , , 600, L10

    Csengeri , T., Bontemps , S., Wyrowski , F., et al. 2017 b , , 600, L10

    2017

  16. [24]

    2016 a , , 586, A149

    Csengeri , T., Leurini , S., Wyrowski , F., et al. 2016 a , , 586, A149

    2016

  17. [25]

    S., Schuller , F., et al

    Csengeri , T., Urquhart , J. S., Schuller , F., et al. 2014, , 565, A75

    2014

  18. [26]

    2016 b , , 585, A104

    Csengeri , T., Weiss , A., Wyrowski , F., et al. 2016 b , , 585, A104

    2016

  19. [27]

    M., Wright , E

    Cutri , R. M., Wright , E. L., Conrow , T., et al. 2012, Explanatory Supplement to the WISE All-Sky Data Release Products , Tech. rep

    2012

  20. [28]

    Donoho, D. L. & Johnstone, I. M. 1994, Biometrika, 81, 425

    1994

  21. [29]

    2013, , 558, A125

    Duarte-Cabral , A., Bontemps , S., Motte , F., et al. 2013, , 558, A125

    2013

  22. [30]

    M., Crapsi , A., Evans , Neal J., I., et al

    Dunham , M. M., Crapsi , A., Evans , Neal J., I., et al. 2008, The Astrophysical Journal Supplement Series, 179, 249

    2008

  23. [31]

    J., Moore , T

    Eden , D. J., Moore , T. J. T., Plume , R., et al. 2017, , 469, 2163

    2017

  24. [32]

    2017, , 471, 100

    Elia , D., Molinari , S., Schisano , E., et al. 2017, , 471, 100

    2017

  25. [33]

    & Klessen , R

    Federrath , C. & Klessen , R. S. 2013, , 763, 51

    2013

  26. [34]

    2016, , 828, 100

    Feng , S., Beuther , H., Zhang , Q., et al. 2016, , 828, 100

    2016

  27. [35]

    B., Rathborne , J

    Foster , J. B., Rathborne , J. M., Sanhueza , P., et al. 2013, , 30, e038

    2013

  28. [36]

    2018, , 476, 1445

    Galliano , F. 2018, , 476, 1445

    2018

  29. [37]

    2009, , 706, 1036

    Galv \'a n-Madrid , R., Keto , E., Zhang , Q., et al. 2009, , 706, 1036

    2009

  30. [38]

    2017, , 603, A33

    Giannetti , A., Leurini , S., Wyrowski , F., et al. 2017, , 603, A33

    2017

  31. [39]

    2014, , 570, A65

    Giannetti , A., Wyrowski , F., Brand , J., et al. 2014, , 570, A65

    2014

  32. [40]

    2015, , 573, A106

    Ginsburg , A., Bally , J., Battersby , C., et al. 2015, , 573, A106

    2015

  33. [41]

    & Mirocha , J

    Ginsburg , A. & Mirocha , J. 2011, PySpecKit: Python Spectroscopic Toolkit , Astrophysics Source Code Library

    2011

  34. [42]

    , Schilke , P., et al

    G \"u sten , R., Nyman , L. ., Schilke , P., et al. 2006, , 454, L13

    2006

  35. [43]

    Gutermuth , R. A. & Heyer , M. 2015, , 149, 64

    2015

  36. [44]

    2012, , 545, A147

    Hennebelle , P. 2012, , 545, A147

    2012

  37. [45]

    W., Gutermuth , R., et al

    Heyer , M., Wilson , G. W., Gutermuth , R., et al. 2018, , 473, 2222

    2018

  38. [46]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90

    2007

  39. [47]

    2001, SciPy : Open source scientific tools for Python

    Jones, E., Oliphant, T., Peterson, P., et al. 2001, SciPy : Open source scientific tools for Python

    2001

  40. [48]

    2013, , 556, A63

    Juvela , M., Montillaud , J., Ysard , N., & Lunttila , T. 2013, , 556, A63

    2013

  41. [49]

    E., Hoare , M

    Kalcheva , I. E., Hoare , M. G., Urquhart , J. S., et al. 2018, , 615, A103

    2018

  42. [50]

    & Pillai, T

    Kauffmann, J. & Pillai, T. 2010, The Astrophysical Journal Letters, 723, L7

    2010

  43. [51]

    S., Wyrowski , F., Menten , K

    Kim , W.-J., Urquhart , J. S., Wyrowski , F., Menten , K. M., & Csengeri , T. 2018, , 616, A107

    2018

  44. [52]

    S., Menten , K

    Kim , W.-J., Wyrowski , F., Urquhart , J. S., Menten , K. M., & Csengeri , T. 2017, , 602, A37

    2017

  45. [53]

    S., Csengeri , T., et al

    K \"o nig , C., Urquhart , J. S., Csengeri , T., et al. 2017, , 599, A139

    2017

  46. [54]

    Lada , C. J. & Lada , E. A. 2003, Annual Review of Astronomy and Astrophysics, 41, 57

    2003

  47. [55]

    Larson , R. B. 1981, , 194, 809

    1981

  48. [56]

    S., Leurini , S., et al

    Li , G.-X., Urquhart , J. S., Leurini , S., et al. 2016, , 591, A5

    2016

  49. [57]

    B., Dale , J

    Lin , Y., Liu , H. B., Dale , J. E., et al. 2017, , 840, 22

    2017

  50. [58]

    B., Li , D., et al

    Lin , Y., Liu , H. B., Li , D., et al. 2016, , 828, 32

    2016

  51. [59]

    B., Chen , H.-R

    Liu , H. B., Chen , H.-R. V., Rom \'a n-Z \'u \ n iga , C. G., et al. 2018, arXiv e-prints, arXiv:1808.07702

    2018 arXiv

  52. [60]

    B., Galv \'a n-Madrid , R., Jim \'e nez-Serra , I., et al

    Liu , H. B., Galv \'a n-Madrid , R., Jim \'e nez-Serra , I., et al. 2015, , 804, 37

    2015

  53. [61]

    B., Ho , P

    Liu , H. B., Ho , P. T. P., & Zhang , Q. 2010, , 725, 2190

    2010

  54. [62]

    B., Jim \'e nez-Serra , I., Ho , P

    Liu , H. B., Jim \'e nez-Serra , I., Ho , P. T. P., et al. 2012 a , , 756, 10

    2012

  55. [63]

    B., Quintana-Lacaci , G., Wang , K., et al

    Liu , H. B., Quintana-Lacaci , G., Wang , K., et al. 2012 b , , 745, 61

    2012

  56. [64]

    P., et al

    Lo , N., Wiles , B., Redman , M. P., et al. 2015, , 453, 3245

    2015

  57. [65]

    L., Hoare , M

    Lumsden , S. L., Hoare , M. G., Urquhart , J. S., et al. 2013, , 208, 11

    2013

  58. [66]

    C., & Barnes , P

    Ma , B., Tan , J. C., & Barnes , P. J. 2013, , 779, 79

    2013

  59. [67]

    C., Tafalla , M., et al

    Mardones , D., Myers , P. C., Tafalla , M., et al. 1997, , 489, 719

    1997

  60. [68]

    2010, in Proceedings of the 9th Python in Science Conference, ed

    McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. S. van der Walt & J. Millman, 51 -- 56

    2010

  61. [69]

    J., Shirley , Y

    Merello , M., Evans , II, N. J., Shirley , Y. L., et al. 2015, , 218, 1

    2015

  62. [70]

    2009, , 501, L1

    Minier , V., Andr \'e , P., Bergman , P., et al. 2009, , 501, L1

    2009

  63. [71]

    2008, , 481, 345

    Molinari , S., Pezzuto , S., Cesaroni , R., et al. 2008, , 481, 345

    2008

  64. [72]

    2016, , 591, A149

    Molinari , S., Schisano , E., Elia , D., et al. 2016, , 591, A149

    2016

  65. [73]

    2010, , 518, L100

    Molinari , S., Swinyard , B., Bally , J., et al. 2010, , 518, L100

    2010

  66. [74]

    Moore , T. J. T., Plume , R., Thompson , M. A., et al. 2015, , 453, 4264

    2015

  67. [75]

    2018, , 56, 41

    Motte , F., Bontemps , S., & Louvet , F. 2018, , 56, 41

    2018

  68. [76]

    2007, , 476, 1243

    Motte , F., Bontemps , S., Schilke , P., et al. 2007, , 476, 1243

    2007

  69. [77]

    2014, , 571, A32

    Motte , F., Nguy \^e n Luong , Q., Schneider , N., et al. 2014, , 571, A32

    2014

  70. [78]

    Motte , F., Schilke , P., & Lis , D. C. 2003, , 582, 277

    2003

  71. [79]

    2010, , 518, L77

    Motte , F., Zavagno , A., Bontemps , S., et al. 2010, , 518, L77

    2010

  72. [80]

    & Chang , P

    Murray , N. & Chang , P. 2015, , 804, 44

    2015

  73. [81]

    Myers , P. C. 2009, , 706, 1341

    2009

  74. [82]

    C., Mardones , D., Tafalla , M., Williams , J

    Myers , P. C., Mardones , D., Tafalla , M., Williams , J. P., & Wilner , D. J. 1996, , 465, L133

    1996

  75. [83]

    Naranjo-Romero , R., V \'a zquez-Semadeni , E., & Loughnane , R. M. 2015, , 814, 48

    2015

  76. [84]

    V., et al

    Ohashi , S., Sanhueza , P., Chen , H.-R. V., et al. 2016, , 833, 209

    2016

  77. [85]

    Oliphant, T. E. 2006, A guide to NumPy, Vol. 1 (Trelgol Publishing USA)

    2006

  78. [86]

    & Henning , T

    Ossenkopf , V. & Henning , T. 1994, , 291, 943

    1994

  79. [87]

    2015, , 453, 3785

    Palau , A., Ballesteros-Paredes , J., V \'a zquez-Semadeni , E., et al. 2015, , 453, 3785

    2015

  80. [88]

    M., et al

    Palau , A., Fuente , A., Girart , J. M., et al. 2013, , 762, 120

    2013

  81. [89]

    & Fuller , G

    Peretto , N. & Fuller , G. A. 2010, , 723, 555

    2010

  82. [90]

    A., Duarte-Cabral , A., et al

    Peretto , N., Fuller , G. A., Duarte-Cabral , A., et al. 2013, , 555, A112

    2013

  83. [91]

    S., Mac Low , M.-M., & Banerjee , R

    Peters , T., Klessen , R. S., Mac Low , M.-M., & Banerjee , R. 2010, , 725, 134

    2010

  84. [92]

    Planck Collaboration , Abergel , A., Ade , P. A. R., et al. 2011, , 536, A25

    2011

  85. [93]

    E., Henning , T., & Beuther , H

    Ragan , S. E., Henning , T., & Beuther , H. 2013, , 559, A79

    2013

  86. [94]

    Rayner , T. S. M., Griffin , M. J., Schneider , N., et al. 2017, , 607, A22

    2017

  87. [95]

    2019, APLpy v2.0: The Astronomical Plotting Library in Python

    Robitaille, T. 2019, APLpy v2.0: The Astronomical Plotting Library in Python

    2019

  88. [96]

    W., Pineda , J

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

    2008

  89. [97]

    J., Teuben , P

    Sault , R. J., Teuben , P. J., & Wright , M. C. H. 1995, in Astronomical Society of the Pacific Conference Series, Vol. 77, Astronomical Data Analysis Software and Systems IV, ed. R. A. Shaw , H. E. Payne , & J. J. E. Hayes , 433

    1995

  90. [98]

    M., Pineau des Forets , G., & Flower , D

    Schilke , P., Walmsley , C. M., Pineau des Forets , G., & Flower , D. R. 1997, , 321, 293

    1997

  91. [99]

    2012, in , Vol

    Schuller , F. 2012, in , Vol. 8452, Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VI, 84521T

    2012

  92. [100]

    M., Contreras , Y., et al

    Schuller , F., Menten , K. M., Contreras , Y., et al. 2009, , 504, 415

    2009

  93. [101]

    2010, The Messenger, 139, 20

    Siringo , G., Kreysa , E., De Breuck , C., et al. 2010, The Messenger, 139, 20

    2010

  94. [102]

    2009, , 497, 945

    Siringo , G., Kreysa , E., Kov \'a cs , A., et al. 2009, , 497, 945

    2009

  95. [103]

    J., Shetty , R., Beuther , H., Klessen , R

    Smith , R. J., Shetty , R., Beuther , H., Klessen , R. S., & Bonnell , I. A. 2013, , 771, 24

    2013

  96. [104]

    & Silk , J

    Spaans , M. & Silk , J. 2000, , 538, 115

    2000

  97. [105]

    W., Palla , F., & Ho , P

    Stahler , S. W., Palla , F., & Ho , P. T. P. 2000, Protostars and Planets IV, 327

    2000

  98. [106]

    & Guesten , R

    Stutzki , J. & Guesten , R. 1990, , 356, 513

    1990

  99. [107]

    E., Shirley , Y

    Svoboda , B. E., Shirley , Y. L., Battersby , C., et al. 2016, , 822, 59

    2016

  100. [108]

    C., Beltr \'a n , M

    Tan , J. C., Beltr \'a n , M. T., Caselli , P., et al. 2014, Protostars and Planets VI, 149

    2014

  101. [109]

    Tanaka , K. E. I., Tan , J. C., & Zhang , Y. 2017, , 835, 32

    2017

  102. [110]

    S., K \"o nig , C., Giannetti , A., et al

    Urquhart , J. S., K \"o nig , C., Giannetti , A., et al. 2018, , 473, 1059

    2018

  103. [111]

    S., Moore , T

    Urquhart , J. S., Moore , T. J. T., Csengeri , T., et al. 2014, , 443, 1555

    2014

  104. [112]

    S., Thompson, M

    Urquhart, J. S., Thompson, M. A., Moore, T. J. T., et al. 2013, Monthly Notices of the Royal Astronomical Society, 435, 400

    2013

  105. [113]

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

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

    2014

  106. [114]

    2017, , 467, 1313

    V \'a zquez-Semadeni , E., Gonz \'a lez-Samaniego , A., & Col \' n , P. 2017, , 467, 1313

    2017

  107. [115]

    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, arXiv e-prints, arXiv:1903.11247

    2019 arXiv

  108. [116]

    2014, , 439, 3275

    Wang , K., Zhang , Q., Testi , L., et al. 2014, , 439, 3275

    2014

  109. [117]

    2011, , 735, 64

    Wang , K., Zhang , Q., Wu , Y., & Zhang , H. 2011, , 735, 64

    2011

  110. [118]

    2010, , 709, 27

    Wang , P., Li , Z.-Y., Abel , T., & Nakamura , F. 2010, , 709, 27

    2010

  111. [119]

    & Kroupa , P

    Weidner , C. & Kroupa , P. 2006, , 365, 1333

    2006

  112. [120]

    2013, , 434, 84

    Weidner , C., Kroupa , P., & Pflamm-Altenburg , J. 2013, , 434, 84

    2013

  113. [121]

    2012, , 544, A146

    Wienen , M., Wyrowski , F., Schuller , F., et al. 2012, , 544, A146

    2012

  114. [122]

    P., Blitz , L., & McKee , C

    Williams , J. P., Blitz , L., & McKee , C. F. 2000, Protostars and Planets IV, 97

    2000

  115. [123]

    Wood , D. O. S. & Churchwell , E. 1989, , 340, 265

    1989

  116. [124]

    & Evans , II, N

    Wu , J. & Evans , II, N. J. 2003, , 592, L79

    2003

  117. [125]

    2013, , 559, A133

    Ysard , N., Abergel , A., Ristorcelli , I., et al. 2013, , 559, A133

    2013

  118. [126]

    2009, , 696, 268

    Zhang , Q., Wang , Y., Pillai , T., & Rathborne , J. 2009, , 696, 268

    2009

  119. [127]

    E., Ginsburg, A

    Aguirre, J. E., Ginsburg, A. G., Dunham, M. K., et al.\ 2011, , 192, 4

    2011

  120. [128]

    Andre, P., Ward-Thompson, D., & Barsony, M.\ 2000, Protostars and Planets IV, 59

    2000

  121. [129]

    Andr \'e , P., Men'shchikov, A., Bontemps, S., et al.\ 2010, , 518, L102

    2010

  122. [130]

    Andr \'e , P., Di Francesco, J., Ward-Thompson, D., et al.\ 2014, Protostars and Planets VI, 27

    2014

  123. [131]

    Andre, P., & Montmerle, T.\ 1994, , 420, 837

    1994

  124. [132]

    Bontemps, S., Motte, F., Csengeri, T., & Schneider, N.\ 2010, , 524, A18

    2010

  125. [133]

    R., & Bodenheimer, P.\ 1997, , 289, 497

    Burkert, A., Bate, M. R., & Bodenheimer, P.\ 1997, , 289, 497

    1997

  126. [134]

    J., & Tan, J

    Butler, M. J., & Tan, J. C.\ 2012, , 754, 5

    2012

  127. [135]

    F., Harvey, P

    Campbell, M. F., Harvey, P. M., Lester, D. F., & Clark, D. M.\ 2004, , 600, 254

    2004

  128. [136]

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

    Carey, S. J., Noriega-Crespo, A., Mizuno, D. R., et al.\ 2009, , 121, 76

    2009

  129. [137]

    Wood, D. O. S., & Churchwell, E.\ 1989, , 340, 265

    1989

  130. [138]

    Compi \`e gne, M., Verstraete, L., Jones, A., et al.\ 2011, , 525, A103

    2011

  131. [139]

    S., et al.\ 2013, , 549, A45

    Contreras, Y., Schuller, F., Urquhart, J. S., et al.\ 2013, , 549, A45

    2013

  132. [140]

    S., Schuller, F., et al.\ 2014, , 565, A75

    Csengeri, T., Urquhart, J. S., Schuller, F., et al.\ 2014, , 565, A75

    2014

  133. [141]

    Csengeri, T., Weiss, A., Wyrowski, F., et al.\ 2016, , 585, A104

    2016

  134. [142]

    Csengeri, T., Bontemps, S., Wyrowski, F., et al.\ 2017, , 601, A60

    2017

  135. [143]

    Csengeri, T., Bontemps, S., Wyrowski, F., et al.\ 2017, , 600, L10

    2017

  136. [144]

    Csengeri, T., Bontemps, S., Wyrowski, F., et al.\ 2018, arXiv:1804.06482

    2018 arXiv

  137. [145]

    M., Wright, E

    Cutri, R. M., Wright, E. L., Conrow, T., et al.\ 2012, Explanatory Supplement to the WISE All-Sky Data Release Products

    2012

  138. [146]

    M., Crapsi, A., Evans, N

    Dunham, M. M., Crapsi, A., Evans, N. J., II, et al.\ 2008, , 179, 249-282

    2008

  139. [147]

    J., Moore, T

    Eden, D. J., Moore, T. J. T., Plume, R., et al.\ 2017, , 469, 2163

    2017

  140. [148]

    Elia, D., Molinari, S., Schisano, E., et al.\ 2017, , 471, 100

    2017

  141. [149]

    M., Krumholz, M

    Fall, S. M., Krumholz, M. R., & Matzner, C. D.\ 2010, , 710, L142

    2010

  142. [150]

    S., & Schmidt, W.\ 2008, , 688, L79

    Federrath, C., Klessen, R. S., & Schmidt, W.\ 2008, , 688, L79

    2008

  143. [151]

    J., Megeath, S

    Fischer, W. J., Megeath, S. T., Furlan, E., et al.\ 2017, , 840, 69

    2017

  144. [152]

    Ginsburg, A., Bally, J., Battersby, C., et al.\ 2015, , 573, A106

    2015

  145. [153]

    J., Abergel, A., Abreu, A., et al.\ 2010, , 518, L3

    Griffin, M. J., Abergel, A., Abreu, A., et al.\ 2010, , 518, L3

    2010

  146. [154]

    S.\ 2011, , 413, 2741

    Girichidis, P., Federrath, C., Banerjee, R., & Klessen, R. S.\ 2011, , 413, 2741

    2011

  147. [155]

    A., & Heyer, M.\ 2015, , 149, 64

    Gutermuth, R. A., & Heyer, M.\ 2015, , 149, 64

    2015

  148. [156]

    Hennebelle, P.\ 2012, , 545, A147

    2012

  149. [157]

    W., Gutermuth, R., et al.\ 2018, , 473, 2222

    Heyer, M., Wilson, G. W., Gutermuth, R., et al.\ 2018, , 473, 2222

    2018

  150. [158]

    M., Stanke, T., et al.\ 2017, , 600, A141

    Kainulainen, J., Stutz, A. M., Stanke, T., et al.\ 2017, , 600, A141

    2017

  151. [159]

    D., Moriarty-Schieven, G., et al.\ 2000, , 545, 327

    Johnstone, D., Wilson, C. D., Moriarty-Schieven, G., et al.\ 2000, , 545, 327

    2000

  152. [160]

    Kauffmann, J., & Pillai, T.\ 2010, , 723, L7

    2010

  153. [161]

    C., & Goodman, A

    Kauffmann, J., Pillai, T., Shetty, R., Myers, P. C., & Goodman, A. A.\ 2010, , 712, 1137

    2010

  154. [162]

    M., Di Francesco, J., et al.\ 2017, , 838, 114

    Kirk, H., Dunham, M. M., Di Francesco, J., et al.\ 2017, , 838, 114

    2017

  155. [163]

    S., Csengeri, T., et al.\ 2017, , 599, A139

    K \"o nig, C., Urquhart, J. S., Csengeri, T., et al.\ 2017, , 599, A139

    2017

  156. [164]

    J., & Lada, E

    Lada, C. J., & Lada, E. A.\ 2003, , 41, 57

    2003

  157. [165]

    Lee, Y.-N., & Hennebelle, P.\ 2016, , 591, A31

    2016

  158. [166]

    C.\ 2014, , 780, L29

    Lim, W., & Tan, J. C.\ 2014, , 780, L29

    2014

  159. [167]

    B., Li, D., et al.\ 2016, , 828, 32

    Lin, Y., Liu, H. B., Li, D., et al.\ 2016, , 828, 32

    2016

  160. [168]

    B., Ho, P

    Liu, H. B., Ho, P. T. P., & Zhang, Q.\ 2010, , 725, 2190

    2010

  161. [169]

    P., et al.\ 2015, , 453, 3245

    Lo, N., Wiles, B., Redman, M. P., et al.\ 2015, , 453, 3245

    2015

  162. [170]

    C., & Barnes, P

    Ma, B., Tan, J. C., & Barnes, P. J.\ 2013, , 779, 79

    2013

  163. [171]

    C., Lunttila, T., & Padoan, P.\ 2011, , 530, A101

    Malinen, J., Juvela, M., Collins, D. C., Lunttila, T., & Padoan, P.\ 2011, , 530, A101

    2011

  164. [172]

    F., & Tan, J

    McKee, C. F., & Tan, J. C.\ 2003, , 585, 850

    2003

  165. [173]

    E., & Pudritz, R

    McLaughlin, D. E., & Pudritz, R. E.\ 1997, , 476, 750

    1997

  166. [174]

    J., II, Shirley, Y

    Merello, M., Evans, N. J., II, Shirley, Y. L., et al.\ 2015, , 218, 1

    2015

  167. [175]

    Molinari, S., Pezzuto, S., Cesaroni, R., et al.\ 2008, , 481, 345

    2008

  168. [176]

    Molinari, S., Swinyard, B., Bally, J., et al.\ 2010, , 518, L100

    2010

  169. [177]

    Molinari, S., Schisano, E., Elia, D., et al.\ 2016, , 591, A149

    2016

  170. [178]

    Motte, F., Bontemps, S., Schilke, P., et al.\ 2007, , 476, 1243

    2007

  171. [179]

    Motte, F., Nguy \^e n Luong, Q., Schneider, N., et al.\ 2014, , 571, A32

    2014

  172. [180]

    Motte, F., Bontemps, S., & Louvet, F.\ 2017, arXiv:1706.00118

    2017 arXiv

  173. [181]

    R., Hoare, M

    Purcell, C. R., Hoare, M. G., Cotton, W. D., et al.\ 2013, , 205, 1

    2013

  174. [182]

    Schuller, F.\ 2012, , 8452, 84521T

    2012

  175. [183]

    A., & Ercolano, B.\ 2009, , 696, 2234

    Shetty, R., Kauffmann, J., Schnee, S., Goodman, A. A., & Ercolano, B.\ 2009, , 696, 2234

    2009

  176. [184]

    Siringo, G., Kreysa, E., De Breuck, C., et al.\ 2010, The Messenger, 139, 20

    2010

  177. [185]

    Stutzki, J., & Guesten, R.\ 1990, , 356, 513

    1990

  178. [186]

    V., et al.\ 2016, , 833, 209

    Ohashi, S., Sanhueza, P., Chen, H.-R. V., et al.\ 2016, , 833, 209

    2016

  179. [187]

    M., et al.\ 2013, , 762, 120

    Palau, A., Fuente, A., Girart, J. M., et al.\ 2013, , 762, 120

    2013

  180. [188]

    M., et al.\ 2014, , 785, 42

    Palau, A., Estalella, R., Girart, J. M., et al.\ 2014, , 785, 42

    2014

  181. [189]

    Planck Collaboration, Abergel, A., Ade, P. A. R., et al.\ 2011, , 536, A25

    2011

  182. [190]

    Ragan, S., Henning, T., Krause, O., et al.\ 2012, , 547, A49

    2012

  183. [191]

    Schaller, G., Schaerer, D., Meynet, G., & Maeder, A.\ 1992, , 96, 269

    1992

  184. [192]

    M., Contreras, Y., et al.\ 2009, , 504, 415

    Schuller, F., Menten, K. M., Contreras, Y., et al.\ 2009, , 504, 415

    2009

  185. [193]

    W., Palla, F., & Ho, P

    Stahler, S. W., Palla, F., & Ho, P. T. P.\ 2000, Protostars and Planets IV, 327

    2000

  186. [194]

    W., Shu, F

    Stahler, S. W., Shu, F. H., & Taam, R. E.\ 1980, , 241, 637

    1980

  187. [195]

    Toci, C., Galli, D., Verdini, A., Del Zanna, L., & Landi, S.\ 2018, , 474, 1288

    2018

  188. [196]

    S., Moore, T

    Urquhart, J. S., Moore, T. J. T., Csengeri, T., et al.\ 2014, , 443, 1555

    2014

  189. [197]

    S., K \"o nig, C., Giannetti, A., et al.\ 2018, , 473, 1059

    Urquhart, J. S., K \"o nig, C., Giannetti, A., et al.\ 2018, , 473, 1059

    2018

  190. [198]

    , K., Zhang, Q., Wu, Y., & Zhang, H.\ 2011, , 735, 64

    2011

  191. [199]

    N.,& Stewart, J

    Cox, A. N.,& Stewart, J. N. 1969,

    1969

  192. [200]

    Tscharnuter W. M. 1987,

    1987

  193. [201]

    1992, in ASP Conf

    Terlevich, R. 1992, in ASP Conf. Ser. 31,

    1992

  194. [202]

    /appendix1_sm/

    Zheng, W., Davidsen, A. F., Tytler, D. & Kriss, G. A. appendix SABOCA maps app:sab_maps_all python import os directory = r"./appendix1_sm/" extension = ".pdf" files = [file for file in os.listdir(directory) if file.lower().endswith(extension)] for i, file in enumerate(files[::...

    arXiv

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Reviewed August 14, 2026 · model on record in the stance chip above.