Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Global and Local Infall in the ASHES Sample (GLASHES). I. Pilot Study in G337.541

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Pith's one-line read This paper claims that cores inside a 70-micron-dark massive clump are accreting gas fast enough to grow from intermediate to high mass within a free-fall time, supporting the clump-fed core-growth scenario for high-mass star formation.

desk verdict A solid pilot study with a real new sample, but the quantitative infall rates rest on a model assumption that isn't tested; the qualitative conclusion of core-scale infall is plausible, the exact numbers are not yet. read the letter →

arxiv 2412.17901 v1 pith:IF4WAIYV submitted 2024-12-23 astro-ph.GA

classification astro-ph.GA
keywords infrareddarkcloudsstarformationformingregionshigh-masscoregrowthinfallblueasymmetryALMA
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 uses ALMA observations of the massive, 70-micron-dark clump G337.541-00.082 to measure how fast gas falls into the dense cores embedded in it. Combining a north-south velocity gradient in N$_2$H$^+$ with blue-asymmetric HNC ($J=3-2$) line profiles, the authors find infall in nine of seventeen cores, with speeds from 0.28 to 1.45 km s$^{-1}$ and mass infall rates of order $10^{-4}$ to $10^{-3}$ $M_\odot$ yr$^{-1}$. These speeds are more than a factor of five higher, and the rates an order of magnitude higher, than those measured in low-mass star-forming cores; that matters because it shows intermediate-mass cores in such clumps can accumulate enough mass within a free-fall time to become high-mass stars. The pilot study is the first in the GLASHES program, which aims to map infall across the ASHES sample of infrared dark clouds.

What carries the argument

The Hill5 model, a two-layer slab radiative transfer model in which excitation temperature rises linearly toward a peak at the slab boundary and falls back to $2.3$ K at the edges, is fitted to the HNC ($J=3-2$) spectra of nine cores to extract the infall velocity from the blue-asymmetric, self-absorbed double-peaked profile. Five parameters (optical depth, systemic velocity, infall velocity, velocity dispersion, and peak excitation temperature) are explored with an affine-invariant MCMC sampler. Supporting that machinery is the position-velocity diagram of N$_2$H$^+$ ($J=1-0$), whose velocity gradients around individual cores provide independent infall timescales comparable to the free-fall time.

What would settle it

Take the HNC ($J=3-2$) spectra of the cores whose red and blue peaks have nearly equal brightness (ALMA1, ALMA4, ALMA5) and subtract the outflow components identified in CO and SiO at the same velocities; if the blue-brighter peak disappears or the self-absorption dip no longer lines up with the optically thin DCO$^+$ centroid velocity, then the blue asymmetry is not an infall signature.

Watch

Extended reading notes

Core claim

Using the isolated hyperfine component of N$_2$H$^+$ ($J=1-0$), the paper maps a clump-scale velocity gradient along the declination axis and smaller-scale gradients around three cores, interpreting these as accretion flows rather than rotation after finding no rotation signatures in optically thin tracers and after comparison with published velocity-gradient measurements in low-mass cores. The more direct evidence is the blue asymmetry in HNC ($J=3-2$): nine of seventeen cores show the blue-brighter, self-absorbed double-peaked profile, and fitting these with the Hill5 two-layer slab model yields infall velocities of 0.28 to 1.45 km s$^{-1}$, an infall rate for the most massive core of $2.9 \times 10^{-3}$ $M_\odot$ yr$^{-1}$, and a strong correlation ($\rho_s = 0.93$) between infall velocity and nonthermal velocity dispersion. The authors conclude that the nonthermal line width is contaminated by infall and that the cores are collapsing on free-fall timescales, with higher infall rates at larger core masses and closer to the clump center, matching clump-fed expectations.

Load-bearing premise

The blue-asymmetric HNC line profile is assumed to be produced by gas falling into a collapsing core; if the asymmetry instead comes from outflows, an unrelated temperature gradient, or a lopsided core seen from a particular angle, the quoted infall speeds and rates would not measure collapse.

Editorial extensions

If this is right

  • If these infall rates persist for a free-fall time, the most massive core in G337 can gain about 30 $M_\odot$ of additional gas, turning an intermediate-mass core into a high-mass star.
  • The blue-asymmetry infall signature appears in 53% of the cores, suggesting that core-scale infall is common in the earliest, feedback-free stages of high-mass star formation.
  • Because infall velocity correlates with nonthermal velocity dispersion ($\rho_s = 0.93$), line-width-based estimates of turbulent support in such clumps must account for a substantial infall contribution.
  • Mass infall rate anticorrelates with inverse mass-weighted distance from the clump center, so the most massive, central cores grow fastest, as clump-fed models predict.
  • Comparison with low-mass star-forming regions shows that same-mass cores accrete more slowly in lower-density environments, implying the local environment sets the infall rate and the potential to form high-mass stars.

Reading between the lines

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

  • If the infall rates measured here are typical of the ASHES sample, dust-continuum core masses in infrared dark clouds systematically underestimate the mass available for star formation, since cores grow significantly during the collapse phase.
  • Extending this pilot to the full GLASHES sample would test whether the G337 pattern (central cores infalling fastest) is universal; a direct check is whether clump-scale specific angular momentum correlates with infall rate across clumps.
  • The correlation between infall velocity and Mach number implies that gravitational collapse, not just turbulence, contributes to the line broadening used to compute virial parameters, so virial masses in such regions may be overestimated.
  • A testable extension is to compare HNC ($J=3-2$) blue-asymmetry infall velocities with independent estimates from HCO$^+$ ($J=3-2$) and from dust-kinematic measurements, to check whether the two-layer slab geometry biases the derived velocities.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. This paper presents ALMA observations (12m + ACA + TP) of the 70 micron dark massive clump G337.541-00.082, using N2H+ (J=1-0), HNC (J=3-2), and HCO+ (J=3-2). From the N2H+ position-velocity diagram the authors identify a clump-scale north-south velocity gradient and smaller-scale gradients around ALMA1, ALMA2, and ALMA3, which they interpret as infall rather than rotation. From HNC spectra they report blue-asymmetric, double-peaked profiles in nine of seventeen dust cores; these are fitted with the Hill5 two-layer radiative transfer model to obtain infall velocities of 0.28-1.45 km/s and mass infall rates of roughly 3e-5 to 2.9e-3 M_sun/yr. The paper reports a strong correlation between infall velocity and nonthermal velocity dispersion, a weaker correlation with core mass, and an anti-correlation between mass infall rate and inverse mass-weighted distance. Comparing with low-mass star-forming region samples, it argues that infall speeds and rates in G337 are several times and an order of magnitude higher, respectively, supporting clump-fed core growth as a route to high-mass star formation.

Significance. If the quantitative results are upheld, this is a valuable pilot study: it provides one of the first multi-core, core-scale infall measurements in an early-stage, 70 micron dark massive clump, and it strengthens the observational case for clump-fed core growth. The paper's strengths include the combination of 12m, 7m, and total-power data to recover extended emission, the use of an optically thin tracer (DCO+) to locate self-absorption dips, the identification of N2H+ emission associated with outflow entrainment, and the authors' explicit acknowledgment of several model limitations. The main scientific claims, however, depend on the fidelity of the Hill5 model for the HNC line profiles and on the statistical robustness of small-sample correlations, both of which need attention before the headline infall velocities, infall rates, and comparison with low-mass regions can be accepted.

major comments (4)
  1. [Section 4.2, Figure 8, Table 2] The quantitative infall velocities and infall rates rest entirely on the Hill5 model, which assumes a two-layer slab with edge excitation temperature T0=2.3 K and a prescribed linear excitation-temperature profile. The paper does not demonstrate that a static core with a near-side/far-side temperature gradient, or a model with only velocity structure, is unable to reproduce the observed blue-bright double-peaked HNC profiles; such alternatives are known to produce similar line shapes. The authors themselves note in Section 4.2 that the infall signature is viewing-angle dependent for non-spherical cores and that Hill5 'may underestimate the infall velocity in some cases', with reliability improving only when separate blue and red components are present. Nevertheless, the headline range 0.28-1.45 km/s includes ALMA1, ALMA4, and ALMA5, whose red and blue peaks are described as having comparable brightness. The MCMC uncertainties in Table 2 (+/-0.01-0.03 km/s) quantify only statistical scatter and exclude this model uncertainty. Because the factor-of-five velocity excess and the order-of-magnitude infall-rate excess in Section 5.1 and the abstract derive from these fits, these quantitative claims are not yet established. The authors should either restrict the quantitative analysis to cores with well-separated blue and red components, or add explicit tests against alternative excitation/velocity structures, or present the values as model-dependent estimates.
  2. [Table 2 versus Figures 8 and 9] There is an inconsistency in the nine-core sample. Table 2 lists ALMA6 (M=0.96 M_sun, vin=0.77 km/s), but Figure 8 shows a fitted spectrum for ALMA5 with vin=0.77 km/s, and the legend of Figure 9 includes ALMA5 and not ALMA6. Section 4.2 also names ALMA1, ALMA4, and ALMA5 as the cores with comparable red and blue peak brightness. The authors need to clarify which cores constitute the nine-core blue-asymmetry sample and correct the table, figure, and legend accordingly, because the masses, radii, and infall velocities in Table 2 enter the mass infall rates and the correlation analyses in Section 5.1.
  3. [Section 5.1, Figure 10, and abstract] The paper calls the relation between mass infall rate and inverse mass-weighted distance a 'strong anti-correlation', but the reported Spearman coefficient is rho=-0.52 with p=0.15 for n=9, which is not statistically significant at the 95% level. Similarly, the correlation between infall velocity and core mass in Figure 9(a) is rho=0.47 with p=0.21. The abstract's statement that 'the mass infall rate is larger for larger core masses and shorter distances to the clump center' therefore overstates the evidence. These should be described as tentative trends that are consistent with, but do not strongly confirm, clump-fed scenarios.
  4. [Section 5.1, Figure 11] The comparison with low-mass star-forming regions combines different molecular tracers, different telescope configurations (single-dish versus interferometric), and different angular resolutions. The paper acknowledges some of these differences but still presents the factor-of-five and order-of-magnitude excesses as robust results. Given that the Hill5-derived velocities are model-dependent and the comparison samples are heterogeneous, the quantitative excess claimed in the abstract should be softened, or the comparison should be made more controlled (for example, by applying the same fitting procedure to homogenized data).
minor comments (5)
  1. [Figure 5 caption] The caption refers to 'the left panel (a)', but the figure appears to be a single panel; the red square should be identified by reference to Figure 4 instead.
  2. [Section 3.1] The statement that N2H+ tracing entrained gas is 'a first report' in high-mass star-forming regions should be softened or supported by a more thorough literature search, since similar N2H+ wing or outflow features may have been reported elsewhere.
  3. [Equation (1)] The definition of the inverse mass-weighted distance uses a normalization by the total mass of the fitted cores, so it is not a purely geometric distance; the text should state this explicitly when interpreting Figure 10.
  4. [Table 2] The column header for the mass infall rate is incomplete; it should read 10^-4 M_sun yr^-1, not just 10^-4 M_sun.
  5. [Figure 8] For ALMA7 and ALMA16, the fitted velocity dispersions (0.18 and 0.17 km/s) are below the HNC channel width of 0.27 km/s; the authors should comment on the reliability of fitting sub-channel-line dispersions.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: infall velocities are fitted with an external radiative-transfer model, comparisons use independent literature samples, and the key correlation uses an independent optically thin tracer (DCO+).

full rationale

The central quantitative results are the Hill5 fits in Section 4.2. Hill5 is an external model (De Vries & Myers 2005) with explicitly stated free parameters; the infall velocity is a fitted parameter, not a prediction derived from the model's own assumptions. The comparison with low-mass star-forming regions uses published Hill5 results from independent catalogs (Lee et al. 2001; Campbell et al. 2016; Kim et al. 2021), so the claimed factor-of-five velocity excess and order-of-magnitude infall-rate excess are empirical comparisons, not identities. The strong correlation between infall velocity and nonthermal velocity dispersion uses DCO+ line widths from Li et al. (2023), an optically thin tracer independent of the HNC line used for the infall fits; the correlation is therefore not a fit-to-fit artifact or a tautology. Core masses and radii are taken from Morii et al. (2023); these are input physical measurements from a companion paper, not the target result, and they do not determine the fitted infall velocities. The skeptic concern that a static temperature-gradient or velocity-gradient model could reproduce the same HNC profiles is a model-degeneracy and systematic-error risk, not a circular reduction of output to input; the paper does not claim uniqueness for the Hill5 interpretation. No circular step can be exhibited from the text.

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

No new entities are introduced. The only new derived quantity is the inverse mass-weighted distance tilde{d} (Eq. 1), a dimensionless coordinate defined from measured positions and masses, not an independent physical entity.

free parameters (4)
  • Inclination angle i = 45 deg
    Assumed for converting PV-gradient-derived infall rates to true rates (Section 4.1, following Chen et al. 2019). The choice affects Mdot by tan(i).
  • N2H+ tail integration velocity range = 3 km/s
    Chosen to maximize the wing range while avoiding neighboring hyperfine components (Section 3.1).
  • Hill5 model prior bounds = tau 0.1-30, vin 0.1-4 km/s, sigma 0.1-1.5 km/s, Tpeak 2-100 K
    Bounds for the MCMC fit (Section 4.2); they constrain the derived infall velocities.
  • PV diagram slit width = 10 arcsec
    Chosen to cover most of the continuum emission in the position-velocity diagram (Section 4.1).
assumptions (4)
  • domain assumption The blue asymmetry profile of an optically thick line traces infall in a collapsing core (Evans 1999)
    Section 4.2 relies on this standard interpretation to convert HNC line shapes into infall velocities; the paper notes it depends on viewing angle for non-spherical or filamentary cores.
  • domain assumption Kinematic distance to G337 is 4.0 kpc (Whitaker et al. 2017)
    All linear scales, radii, densities, and rates depend on this distance (Sections 1, 3).
  • domain assumption Core masses, radii, and densities from Morii et al. 2023 are accurate
    Used in Mdot = 4 pi R^2 rho vin and in the free-fall velocity comparison (Sections 4.2, 5.1); these depend on dust opacity and temperature assumptions.
  • domain assumption HNC J=3-2 is optically thick with self-absorption, while DCO+ is optically thin
    Section 4.2 uses the DCO+ peak to locate the dip and the HNC self-absorption to infer infall.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Global and Local Infall in the ASHES Sample (GLASHES). I. Pilot Study in G337.541." pith.science (2026). https://pith.science/paper/IF4WAIYV

@misc{pith2026241217901,
  author       = {Pith},
  title        = {Pith review of: Global and Local Infall in the ASHES Sample (GLASHES). I. Pilot Study in G337.541},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IF4WAIYV}},
  note         = {Machine review of arXiv:2412.17901}
}
abstract

Recent high-angular-resolution observations indicate the need for core growth to form high-mass stars. To understand the gas dynamics at the core scale in the very early evolutionary stages before being severely affected by feedback, we have conducted Atacama Large Millimeter/submillimeter Array (ALMA) observations toward a 70 $\mu$m dark massive clump, G337.541-00.082 as part of the Global and Local infall in the ASHES sample (GLASHES) program. Using dense gas tracers such as N$_2$H$^+$ ($J = 1-0$) and HNC ($J = 3-2$), we find signs of infall from the position-velocity diagram and more directly from the blue asymmetry profile in addition to the clump-scale velocity gradient. We estimate infall velocities from intermediate and low-mass cores to be 0.28-1.45 km s$^{-1}$, and infall rates to be on the order of 10$^{-4}$ to 10$^{-3}$ $M_\odot$ yr$^{-1}$, both are higher than those measured in low-mass star-forming regions by more than a factor of five and an order of magnitude, respectively. We find a strong correlation of the infall velocity with the nonthermal velocity dispersion, suggesting that infall may contribute significantly to the observed line width. Consistent with clump-fed scenarios, we show that the mass infall rate is larger for larger core masses and shorter distances to the clump center. Such high infall rates in cores embedded in IRDCs can be considered as strong signs of core growth, allowing high-mass star formation from intermediate-mass cores that would not initially form high-mass stars at their current mass.

Figures

Figures reproduced from arXiv: 2412.17901 by the authors.

Figure 1
Figure 1. The integrated intensity (mom0) map and the line center map of N2H + (J = 1 − 0, F1, F = 0, 1 − 1, 2). The white contours show 1.3 mm continuum emission at levels of 3, 5, 10, 20, and 40 sigma, where 1 sigma = 0.07 Jy beam−1 . The synthesized beamsize and the scale bar are plotted in the bottom of the left panel. In the left panel, orange crosses represent the continuum peak positions of ALMA1, ALMA2, and ALMA3. In … view at source ↗
Figure 2
Figure 2. Channel map of N2H + (J = 1 − 0, F1, F = 0, 1 − 1, 2). The white crosses are plotted at the peaks in the continuum of the identified cores in Morii et al. (2023) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. (a) Example of the N2H + (J = 1 − 0) spectra showing tail components (a reference frequency of 93.17626 GHz). The velocity range used to make the tail maps is highlighted as the shaded zone. (b) The integrated intensity maps of (left) the blue-shifted and (right) the red-shifted wing components in the raster, CO (J = 2 − 1) in red and light-blue contours and SiO (J = 5 − 4) in white contours. The integration velocit… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: The mom0 maps of HCO+ (J = 3 − 2) and HNC (J = 3 − 2), overlaid with continuum emission as white contours. The contour levels are the same as in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Line spectra of HCO+ (blue), HNC (green), and DCO+ (orange), averaged within the red square in the left panel (a). The orange crosses and the numbers represent the continuum peak positions of the cores and their id, which are discussed in Section 4.2 [PITH_FULL_IMAGE:…
Figure 6
Figure 6. Figure 6: The position-velocity diagram of N2H + (J = 1 − 0, F1, F = 0, 1 − 1, 2) along the declination with a width of 10 arcsec, centered on the continuum peak position of ALMA1. The contour levels are 3, 5, 7, 10, 15, 20 σ, where 1 σ = 2.5 mJy beam−1 . The vertical orange lin…
Figure 7
Figure 7. Figure 7: Schematic picture of the gas dynamics in G337 and observations (e.g., Wu et al. 2010; Xie et al. 2021) [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Line spectra of HNC (green) and DCO+ (orange) of nine cores averaged inside cores. The thick gray lines represent the results of the Hill5 model fit. The best-fit parameters are shown on the right of each panel. core of R = 3250 au (Morii et al. 2023). With vin es￾tima…
Figure 9
Figure 9. Figure 9: Scatter plots of infall velocity with (a) core mass, (b) nonthermal velocity dispersion, (c) Mach number, and (d) free-fall time. The velocity width was derived from DCO+, but for two cores without DCO+ detection, we measured them from N2H + and highlighted them as tri…
Figure 10
Figure 10. Figure 10: A scatter plot of mass infall rate with inverse mass-weighted distance, d˜(Mi). As same with [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: Histogram of infall velocity and infall rate com￾pared with low-mass star forming regions. in low-mass star-forming regions) and temperature are similar within the sample, but our cores are a few times smaller and more than one order of magnitude denser. We conclude t…
Figure 12
Figure 12. Figure 12: Multiple gaussian fitting [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Challenges in probing turbulent and magnetic support in cores: the W43-MM1 protocluster case study

    astro-ph.GA 2026-07 conditional novelty 6.0 of 10

    Simplified virial analyses of W43-MM1 cores overestimate non-thermal support because linewidths include organized motions of 1–3 km/s and surface terms are omitted, producing unexpectedly high stability fractions.

Reference graph

Works this paper leans on

70 extracted references · 7 canonical work pages · cited by 1 Pith paper

  1. [1]

    2023, A&A, 677, A171, doi: 10.1051/0004-6361/202245580 ´Alvarez-Guti´ errez, R

    Ahmadi, A., Beuther, H., Bosco, F., et al. 2023, A&A, 677, A171, doi: 10.1051/0004-6361/202245580 ´Alvarez-Guti´ errez, R. H., Stutz, A. M., Sandoval-Garrido, N., et al. 2024, arXiv e-prints, arXiv:2404.07363, doi: 10.48550/arXiv.2404.07363 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322...

  2. [2]

    T., Liu, J., Zhang, Q., et al

    Barnes, A. T., Liu, J., Zhang, Q., et al. 2023, A&A, 675, A53, doi: 10.1051/0004-6361/202245668

  3. [3]

    K., & Brinch, C

    Bjerkeli, P., Jørgensen, J. K., & Brinch, C. 2016, A&A, 587, A145, doi: 10.1051/0004-6361/201527310

  4. [4]

    L., Friesen, R

    Campbell, J. L., Friesen, R. K., Martin, P. G., et al. 2016, ApJ, 819, 143, doi: 10.3847/0004-637X/819/2/143 CASA Team, Bean, B., Bhatnagar, S., et al. 2022, PASP, 134, 114501, doi: 10.1088/1538-3873/ac9642

  5. [5]

    V., Zhang, Q., Wright, M

    Chen, H.-R. V., Zhang, Q., Wright, M. C. H., et al. 2019, ApJ, 875, 24, doi: 10.3847/1538-4357/ab0f3e

  6. [6]

    2014, MNRAS, 444, 874, doi: 10.1093/mnras/stu1497

    Shetty, R. 2014, MNRAS, 444, 874, doi: 10.1093/mnras/stu1497

  7. [7]

    2018, Automatic Line Clean, 1.0, Zenodo, doi: 10.5281/zenodo.1216881

    Contreras, Y. 2018, Automatic Line Clean, 1.0, Zenodo, doi: 10.5281/zenodo.1216881

  8. [8]

    M., et al

    Contreras, Y., Sanhueza, P., Jackson, J. M., et al. 2018, ApJ, 861, 14, doi: 10.3847/1538-4357/aac2ec AASTeX v6.3.1 15

Show all 70 references
  1. [9]

    2011, A&A, 527, A135, doi: 10.1051/0004-6361/201014984 De Vries, C

    Dib, S. 2011, A&A, 527, A135, doi: 10.1051/0004-6361/201014984 De Vries, C. H., & Myers, P. C. 2005, ApJ, 620, 800, doi: 10.1086/427141

  2. [10]

    1999, ARA&A, 37, 311, doi: 10.1146/annurev.astro.37.1.311

    Evans, Neal J., I. 1999, ARA&A, 37, 311, doi: 10.1146/annurev.astro.37.1.311

  3. [11]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

  4. [12]

    A., Williams, S

    Fuller, G. A., Williams, S. J., & Sridharan, T. K. 2005, A&A, 442, 949, doi: 10.1051/0004-6361:20042110

  5. [13]

    A., Benson, P

    Goodman, A. A., Benson, P. J., Fuller, G. A., & Myers, P. C. 1993, ApJ, 406, 528, doi: 10.1086/172465 Guzm´ an, A. E., Sanhueza, P., Contreras, Y., et al. 2015, ApJ, 815, 130, doi: 10.1088/0004-637X/815/2/130

  6. [14]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

  7. [15]

    D., Caselli, P., Fontani, F., Jim´ enez-Serra, I., & Tan, J

    Henshaw, J. D., Caselli, P., Fontani, F., Jim´ enez-Serra, I., & Tan, J. C. 2014, MNRAS, 440, 2860, doi: 10.1093/mnras/stu446

  8. [16]

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

  9. [17]

    M., et al

    Izumi, N., Sanhueza, P., Koch, P. M., et al. 2024, ApJ, 963, 163, doi: 10.3847/1538-4357/ad18c6

  10. [18]

    M., Whitaker, J

    Jackson, J. M., Whitaker, J. S., Rathborne, J. M., et al. 2019, ApJ, 870, 5, doi: 10.3847/1538-4357/aaef84

  11. [19]

    2021, ApJ, 910, 112, doi: 10.3847/1538-4357/abe4d3

    Kim, G. 2021, ApJ, 910, 112, doi: 10.3847/1538-4357/abe4d3

  12. [20]

    C., Caselli, P., et al

    Kong, S., Tan, J. C., Caselli, P., et al. 2017, ApJ, 834, 193, doi: 10.3847/1538-4357/834/2/193

  13. [21]

    W., Myers, P

    Lee, C. W., Myers, P. C., & Tafalla, M. 2001, ApJS, 136, 703, doi: 10.1086/322534

  14. [22]

    2021, ApJL, 912, L7, doi: 10.3847/2041-8213/abf64f

    Li, S., Lu, X., Zhang, Q., et al. 2021, ApJL, 912, L7, doi: 10.3847/2041-8213/abf64f

  15. [23]

    2020, ApJ, 903, 119, doi: 10.3847/1538-4357/abb81f

    Li, S., Sanhueza, P., Zhang, Q., et al. 2020, ApJ, 903, 119, doi: 10.3847/1538-4357/abb81f

  16. [24]

    2022, ApJ, 939, 102, doi: 10.3847/1538-4357/ac94d4

    Li, S., Sanhueza, P., Lu, X., et al. 2022, ApJ, 939, 102, doi: 10.3847/1538-4357/ac94d4

  17. [25]

    2023, ApJ, 949, 109, doi: 10.3847/1538-4357/acc58f

    Li, S., Sanhueza, P., Zhang, Q., et al. 2023, ApJ, 949, 109, doi: 10.3847/1538-4357/acc58f

  18. [26]

    2019, A&A, 622, A99, doi: 10.1051/0004-6361/201732282

    Louvet, F., Neupane, S., Garay, G., et al. 2019, A&A, 622, A99, doi: 10.1051/0004-6361/201732282

  19. [27]

    2015, ApJ, 805, 171, doi: 10.1088/0004-637X/805/2/171

    Lu, X., Zhang, Q., Wang, K., & Gu, Q. 2015, ApJ, 805, 171, doi: 10.1088/0004-637X/805/2/171

  20. [28]

    B., et al

    Lu, X., Zhang, Q., Liu, H. B., et al. 2018, ApJ, 855, 9, doi: 10.3847/1538-4357/aaad11

  21. [29]

    2024, ApJL, 961, L35, doi: 10.3847/2041-8213/ad19c3

    Mai, X., Liu, T., Liu, X., et al. 2024, ApJL, 961, L35, doi: 10.3847/2041-8213/ad19c3

  22. [30]

    2019, A&A, 626, A132, doi: 10.1051/0004-6361/201935497

    Molet, J., Brouillet, N., Nony, T., et al. 2019, A&A, 626, A132, doi: 10.1051/0004-6361/201935497

  23. [31]

    2021, ApJ, 923, 147, doi: 10.3847/1538-4357/ac2365 —

    Morii, K., Sanhueza, P., Nakamura, F., et al. 2021, ApJ, 923, 147, doi: 10.3847/1538-4357/ac2365 —. 2023, ApJ, 950, 148, doi: 10.3847/1538-4357/acccea

  24. [32]

    2024, ApJ, 966, 171, doi: 10.3847/1538-4357/ad32d0

    Morii, K., Sanhueza, P., Zhang, Q., et al. 2024, ApJ, 966, 171, doi: 10.3847/1538-4357/ad32d0

  25. [33]

    2018, A&A, 618, L5, doi: 10.1051/0004-6361/201833863

    Nony, T., Louvet, F., Motte, F., et al. 2018, A&A, 618, L5, doi: 10.1051/0004-6361/201833863

  26. [34]

    V., et al

    Ohashi, S., Sanhueza, P., Chen, H.-R. V., et al. 2016, ApJ, 833, 209, doi: 10.3847/1538-4357/833/2/209

  27. [35]

    A., Sanhueza, P., Chen, H.-R

    Olguin, F. A., Sanhueza, P., Chen, H.-R. V., et al. 2023, ApJL, 959, L31, doi: 10.3847/2041-8213/ad1100

  28. [36]

    A., Andr´ e, P., et al

    Peretto, N., Fuller, G. A., Andr´ e, P., et al. 2014, A&A, 561, A83, doi: 10.1051/0004-6361/201322172

  29. [37]

    2006, A&A, 445, 979, doi: 10.1051/0004-6361:20053324

    Peretto, N., Andr´ e, Ph., & Belloche, A. 2006, A&A, 445, 979, doi: 10.1051/0004-6361:20053324

  30. [38]

    2019, A&A, 622, A54, doi: 10.1051/0004-6361/201732570

    Pillai, T., Kauffmann, J., Zhang, Q., et al. 2019, A&A, 622, A54, doi: 10.1051/0004-6361/201732570

  31. [39]

    2023, A&A, 674, A76, doi: 10.1051/0004-6361/202244776

    Pouteau, Y., Motte, F., Nony, T., et al. 2023, A&A, 674, A76, doi: 10.1051/0004-6361/202244776

  32. [40]

    M., Whitaker, J

    Rathborne, J. M., Whitaker, J. S., Jackson, J. M., et al. 2016, PASA, 33, e030, doi: 10.1017/pasa.2016.23

  33. [41]

    2022, ApJ, 936, 169, doi: 10.3847/1538-4357/ac85b4

    Redaelli, E., Bovino, S., Sanhueza, P., et al. 2022, ApJ, 936, 169, doi: 10.3847/1538-4357/ac85b4

  34. [42]

    L., Wu, J., et al

    Reiter, M., Shirley, Y. L., Wu, J., et al. 2011, ApJ, 740, 40, doi: 10.1088/0004-637X/740/1/40

  35. [43]

    J., Peretto, N., Anderson, M., et al

    Rigby, A. J., Peretto, N., Anderson, M., et al. 2024, MNRAS, 528, 1172, doi: 10.1093/mnras/stae030

  36. [44]

    Rygl, K. L. J., Wyrowski, F., Schuller, F., & Menten, K. M. 2013, A&A, 549, A5, doi: 10.1051/0004-6361/201219574

  37. [45]

    2010, ApJ, 715, 18, doi: 10.1088/0004-637X/715/1/18

    Sanhueza, P., Garay, G., Bronfman, L., et al. 2010, ApJ, 715, 18, doi: 10.1088/0004-637X/715/1/18

  38. [46]

    M., Foster, J

    Sanhueza, P., Jackson, J. M., Foster, J. B., et al. 2012, ApJ, 756, 60, doi: 10.1088/0004-637X/756/1/60 —. 2013, ApJ, 773, 123, doi: 10.1088/0004-637X/773/2/123

  39. [47]

    M., Zhang, Q., et al

    Sanhueza, P., Jackson, J. M., Zhang, Q., et al. 2017, ApJ, 841, 97, doi: 10.3847/1538-4357/aa6ff8

  40. [48]

    2019, ApJ, 886, 102, doi: 10.3847/1538-4357/ab45e9

    Sanhueza, P., Contreras, Y., Wu, B., et al. 2019, ApJ, 886, 102, doi: 10.3847/1538-4357/ab45e9

  41. [49]

    M., Padovani, M., et al

    Sanhueza, P., Girart, J. M., Padovani, M., et al. 2021, ApJL, 915, L10, doi: 10.3847/2041-8213/ac081c

  42. [50]

    2010, A&A, 520, A49, doi: 10.1051/0004-6361/201014481

    Schneider, N., Csengeri, T., Bontemps, S., et al. 2010, A&A, 520, A49, doi: 10.1051/0004-6361/201014481

  43. [51]

    J., Shetty, R., Stutz, A

    Smith, R. J., Shetty, R., Stutz, A. M., & Klessen, R. S. 2012, ApJ, 750, 64, doi: 10.1088/0004-637X/750/1/64

  44. [52]

    E., Shirley, Y

    Svoboda, B. E., Shirley, Y. L., Traficante, A., et al. 2019, ApJ, 886, 36, doi: 10.3847/1538-4357/ab40ca 16 Morii et al

  45. [53]

    2018, Research Notes of the American Astronomical Society, 2, 173, doi: 10.3847/2515-5172/aae265

    Teague, R., & Foreman-Mackey, D. 2018, Research Notes of the American Astronomical Society, 2, 173, doi: 10.3847/2515-5172/aae265

  46. [54]

    J., Hartmann, L., Chiang, H.-F., et al

    Tobin, J. J., Hartmann, L., Chiang, H.-F., et al. 2011, ApJ, 740, 45, doi: 10.1088/0004-637X/740/1/45

  47. [55]

    A., Billot, N., et al

    Traficante, A., Fuller, G. A., Billot, N., et al. 2017, MNRAS, 470, 3882, doi: 10.1093/mnras/stx1375

  48. [56]

    A., Smith, R

    Traficante, A., Fuller, G. A., Smith, R. J., et al. 2018, MNRAS, 473, 4975, doi: 10.1093/mnras/stx2672

  49. [57]

    M., Avison, A., et al

    Traficante, A., Jones, B. M., Avison, A., et al. 2023, MNRAS, 520, 2306, doi: 10.1093/mnras/stad272

  50. [58]

    E., et al

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

  51. [59]

    Waskom, M. L. 2021, Journal of Open Source Software, 6, 3021, doi: 10.21105/joss.03021

  52. [60]

    Wells, M. R. A., Beuther, H., Molinari, S., et al. 2024, arXiv e-prints, arXiv:2408.08299, doi: 10.48550/arXiv.2408.08299

  53. [61]

    S., Jackson, J

    Whitaker, J. S., Jackson, J. M., Rathborne, J. M., et al. 2017, AJ, 154, 140, doi: 10.3847/1538-3881/aa86ad

  54. [62]

    L., & Knez, C

    Wu, J., Evans, Neal J., I., Shirley, Y. L., & Knez, C. 2010, ApJS, 188, 313, doi: 10.1088/0067-0049/188/2/313

  55. [63]

    M., et al

    Wyrowski, F., G¨ usten, R., Menten, K. M., et al. 2016, A&A, 585, A149, doi: 10.1051/0004-6361/201526361

  56. [64]

    A., et al

    Xie, J.-J., Wu, J.-W., Fuller, G. A., et al. 2021, Research in Astronomy and Astrophysics, 21, 208, doi: 10.1088/1674-4527/21/8/208

  57. [65]

    2023a, ApJS, 269, 38, doi: 10.3847/1538-4365/acfee2

    Xu, F., Wang, K., He, Y., et al. 2023a, ApJS, 269, 38, doi: 10.3847/1538-4365/acfee2

  58. [66]

    2024, Research in Astronomy and Astrophysics, 24, 065011, doi: 10.1088/1674-4527/ad3dc3

    Xu, F., Wang, K., Liu, T., et al. 2024, Research in Astronomy and Astrophysics, 24, 065011, doi: 10.1088/1674-4527/ad3dc3

  59. [67]

    2023b, MNRAS, 520, 3259, doi: 10.1093/mnras/stad012

    Xu, F.-W., Wang, K., Liu, T., et al. 2023b, MNRAS, 520, 3259, doi: 10.1093/mnras/stad012

  60. [68]

    2021, ApJ, 922, 144, doi: 10.3847/1538-4357/ac22ab

    Yang, Y., Jiang, Z., Chen, Z., Ao, Y., & Yu, S. 2021, ApJ, 922, 144, doi: 10.3847/1538-4357/ac22ab

  61. [69]

    2022, Research in Astronomy and Astrophysics, 22, 095014, doi: 10.1088/1674-4527/ac7d9d

    Yu, S., Jiang, Z., Yang, Y., Chen, Z., & Feng, H. 2022, Research in Astronomy and Astrophysics, 22, 095014, doi: 10.1088/1674-4527/ac7d9d

  62. [70]

    Zhang, Q., & Wang, K. 2011, ApJ, 733, 26, doi: 10.1088/0004-637X/733/1/26 AASTeX v6.3.1 17 62 60 58 56 54 52 50 48 LSR velocity [km s 1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0Tb [K] Data Fitted Curve Gaussian 1 Gaussian 2 Gaussian 3 (a) ALMA2 62 60 58 56 54 52 50 48 LSR velocity [km/s] 0...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.