Pith. sign in

REVIEW 2 major objections 4 minor 85 references

The Young Ages of 70 {\mu}m-dark Clumps Inferred from Carbon Chain Chemistry

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

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

Pith's one-line read The paper claims that eleven 70-micron-dark clumps are chemically younger than about 1 million years, so their lack of high-mass protostars reflects youth rather than incapacity.

desk verdict A genuinely new VLA carbon-chain dataset with a plausible but conditional <1 Myr age claim; the conclusion that the clumps are young rather than inefficient rests on an admitted assumption about outflow chemistry that the current data cannot yet rule out. read the letter →

arxiv 2502.04283 v1 pith:TWLE6OQG submitted 2025-02-06 astro-ph.GA astro-ph.SR

Kadin Worthen , Brian E. Svoboda , David S. Meier , Juergen Ott , Rachel Friesen , Jennifer Patience , Yancy Shirley This is my paper · ORCID
classification astro-ph.GAastro-ph.SR
keywords carbonchainmolecules70microndarkclumpshigh-massstarformationchemicalageHC5NCCSHC7Ncloudchemistrymodels
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

Using the Very Large Array at K band, the paper observes three carbon-chain molecules (HC5N, CCS, and HC7N) toward twelve massive clumps that are dark at 70 microns. It detects HC5N and CCS in eleven of the twelve, and it detects HC7N only after stacking the spectra. Comparing measured HC5N abundances and the HC5N-to-HC7N column-density ratio to dark-cloud chemistry models, the paper finds a chemical evolutionary age below about 1 million years at the clumps' median density of $n(\mathrm{H}_2)\approx 2\times 10^{4}\,\mathrm{cm}^{-3}$. The paper concludes that these clumps lack high-mass protostars because they are young, not because they are incapable of forming them.

What carries the argument

Carbon-chain chemistry functions as an early-time chemical clock. In cold dense gas, HC5N and related chains form from reactions of carbon atoms and ions before carbon freezes into CO, peak near $10^5$ years, and then are destroyed or depleted, so their abundances flag gas that has not yet reached chemical equilibrium at about 1 Myr. The paper runs the UMIST 13 dark-cloud chemistry network, a standard gas-phase reaction model, at the measured clump density and temperature to get predicted HC5N abundance and the HC5N/HC7N ratio as functions of time, then reads off the age where the observed values cross the model curves; the column-density ratio is the cleaner clock because it does not depend on the H2 column density.

What would settle it

Observe one of the detected clumps with a known low-mass outflow at sub-arcsecond resolution and compare the spatial distribution and kinematics of HC5N and CCS with the outflow. If the carbon-chain emission is concentrated in knots along the outflow cavity walls, peaks at the protostar, or shows velocity offsets and line widths (roughly $3$-$5$ km s$^{-1}$) like those seen in genuine shock chemistry, then the emission would trace outflow chemistry rather than the quiescent clump gas, and the under-1-Myr age would not date the clump as a whole.

Watch

Extended reading notes

Core claim

The central claim is that the eleven clumps with detected carbon chains are chemically younger than roughly 1 Myr, and that this youth is what explains the absence of high-mass protostars. The measured HC5N abundances are three to four orders of magnitude above what the models predict at ages beyond 1 Myr, and the HC5N/HC7N column-density ratio from the stacked spectra intersects the model curves at an age below about 1 Myr. Because all eleven sources cluster in this young window while an old, inefficient clump population would be spread over tens of millions of years, the paper argues that these clumps should be regarded as early-stage, still capable objects rather than sterile ones.

Load-bearing premise

The load-bearing premise is that the carbon-chain emission comes from the cold, quiescent bulk gas of the clumps, rather than being created or enhanced in the outflow cavities or warm regions around the low-mass protostars that are already present in ten of the eleven detected sources.

Editorial extensions

If this is right

  • If the clumps are younger than about 1 Myr, their missing high-mass star indicators are expected: a high-mass protostar takes a comparable or longer time to appear.
  • The clumps then qualify as genuine pre-high-mass-star objects, meaning their physical and chemical states can serve as the initial conditions for high-mass star formation.
  • The single non-detection, G23605, is naturally interpreted as a clump that has already passed the carbon-chain-bright phase and may be at a later, possibly less capable stage.
  • The derived youth is consistent with previous chemical ages for similar dark clumps and with models of the starless phase lifetime, strengthening the picture that the sample has not had time to form high-mass stars.

Reading between the lines

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

  • Beyond the paper: if the emission instead traces outflow or shock chemistry around the embedded low-mass protostars, the age would apply only to those localized regions, and the paper's own VLA data cannot currently exclude this.
  • A direct extension would be to measure carbon-chain-derived ages for a much larger sample of 70-micron-dark clumps; a broad spread of ages would argue against the youth interpretation, while a tight clustering below 1 Myr would support it.
  • The clump-to-clump scatter in HC5N abundance (roughly $10^{-11}$ to $10^{-10}$ in this sample) may encode a finer age gradient within the under-1-Myr window that higher signal-to-noise observations could resolve.
  • Pairing the carbon-chain clock with a depletion-based clock on the same clumps would give an independent cross-check of the under-1-Myr age without relying on a single chemical network.
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

2 major / 4 minor

Summary. The paper reports VLA K-band observations of HC5N, CCS, and HC7N toward 12 high-mass 70 micron-dark clumps. It detects HC5N and CCS in 11 of 12 sources, obtains no individual HC7N detections but a stacked HC7N detection, derives column densities and H2-normalized abundances using LTE with Tex = 4-6 K, and compares the HC5N abundance and the stacked HC5N/HC7N ratio to UMIST 13 dark cloud chemistry models. The models imply a chemical age less than about 1 Myr at n(H2) = 2 x 10^4 cm^-3. The paper concludes that these clumps lack high-mass protostars because they are young rather than because they are intrinsically inefficient at high-mass star formation.

Significance. If the age inference is correct, the result is important: it places these massive 70 micron-dark clumps in the earliest pre-high-mass-star phase and supports their use as initial conditions for high-mass star formation. The observational analysis is careful and has several strengths: two independent spectral extraction methods agree; uncertainties are propagated through the Gaussian fits, the Tex range, and the H2 column; the age is read from published UMIST curves without fitting a free parameter; and the conclusion is robust to tested variations in density, temperature, UV field, and extinction. The main risk is not internal inconsistency but an explicit, unresolved degeneracy between early-time quiescent carbon-chain chemistry and outflow/shock/photochemical carbon-chain production in sources that already contain low-mass protostars.

major comments (2)
  1. [5.3] Section 5.3 concedes that carbon-chain formation in outflow regions of low-mass protostars cannot be ruled out with the current data. This concession is load-bearing because 10 of the 11 detected clumps already contain low-mass protostars with CO outflows (Section 2.2; Svoboda et al. 2019), and the spectra are summed over the full NH3 mask (Section 3). Mendoza et al. (2018) measured HC5N abundances of ~1.2e-9 in the L1157-B1 shock outflow, an order of magnitude above the values reported in Table 5, so a small mass fraction of outflow-affected gas included in the aperture would raise the aperture-averaged abundance to the observed ~1e-10 and mimic a young bulk chemical age. The arguments from line velocities, narrow line widths, and NH3 kinetic temperatures below 25 K are suggestive, but the manuscript explicitly stops short of excluding the contamination. I ask the authors to quantify the maximum allowed mass fraction of outflow/shock/photochemistry-dominated gas consistent with the observed line parameters, or to obtain or present data that separate the quiescent gas from outflow cavities; without one of these, the statement that the HC5N abundance dates the bulk clump gas is not established.
  2. [5.4] The statistical argument against inefficiency assumes that if the clumps were inefficient at forming high-mass stars, their ages would be uniformly distributed between 0 and 21 Myr. This uniform prior is introduced ad hoc and is not derived from any observed cloud-age distribution or from the dynamical history of these particular clumps; galactic shear sets an upper limit on cloud lifetime, not a uniform age distribution. Under a prior that weights young ages more heavily (for example because clouds are destroyed or evolve on shorter timescales), the claimed probability of ~3e-15 would be very different. Since this argument is the second leg of the 'young, not inefficient' conclusion, the authors should present the result as a likelihood ratio under several explicit priors, or replace the uniform-age assumption with an empirical age distribution for quiescent clumps.
minor comments (4)
  1. [Section 3; Figures 3 and 4] The text and figure captions are inconsistent about which spectra used the velocity-registration method: Section 3 says HC5N and CCS for G22695 and CCS for G30120 and G30660, while the Figure 3 caption says G30660 is the only one and the Figure 4 caption says G30660 and G29601; please reconcile these statements and mark the extraction method consistently.
  2. [Figures 3 and 4; Appendix A] There are several small typos: 'The only non-detection of HC5N in on 12 sources' should read 'in one of 12 sources,' and the Appendix labels G29558 as 'G9558'.
  3. [5.5] The text says 'Using equation 10 and the mean density we calculate for our SMDCs,' but the free-fall timescale is given in equation (11); equation (10) is the N(H2)-Av relation, so this citation appears to be a typo.
  4. [Table 2] The peak surface density entry for G23297 appears as '0760 (0.019)' rather than '0.0760 (0.019)', which is likely a formatting typo.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the ages are read from an external UMIST chemical model, self-citations supply sample context but not the chemical clock, and the conceded outflow/WCCC contamination is a physical limitation rather than a circular reduction.

full rationale

I walked the derivation chain from the VLA line observations to the inferred clump ages. The central inference compares measured HC5N abundances and the stacked HC5N/HC7N ratio with published UMIST 13 dark-cloud chemistry model curves. No parameter in this paper is fitted to force the <1 Myr result; the model input density is the measured median with robustness checks at the median absolute deviation, and the paper explicitly tests temperature, UV field, extinction, and alternative chemical networks (Section 5.2). The self-citations to Svoboda et al. (2016, 2019) supply the target sample, clump masses, distances, and the presence of low-mass protostars, but they do not provide the chemical evolution curves or the age conclusion. The most serious caveat, discussed in Section 5.3, is that warm carbon-chain chemistry or outflow/shock/photochemistry around the embedded low-mass protostars could in principle contribute to the observed carbon-chain emission; the authors explicitly concede 'we ultimately cannot rule out the possibility of carbon chain formation in the formation of outflow regions of low-mass protostars' and 'The current VLA data cannot rule out this possibility.' That is a genuine observational limitation and a threat to the astrophysical interpretation, but it is not circular: it does not make the age estimate equivalent to an input by construction, and it does not involve fitting a parameter to a subset of data and then renaming it a prediction. The statistical argument in Section 5.4 assumes a uniform age prior for inefficient clumps, but that is an assumption about the prior, not a circular reduction of the age measurement itself. I therefore find no step in which a 'prediction' reduces, by the paper's own equations or by self-citation, to its own inputs.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

The paper's conclusions rest on a handful of external benchmarks and adopted values rather than on internally fitted parameters. The key adopted quantities are Tex = 5 K, the dust opacity model, and the gas-to-dust ratio, all drawn from literature and propagated as uncertainties. The UMIST 13 model provides the chemical clock, with the authors acknowledging order-of-magnitude rate uncertainties. The most fragile inputs are the assumptions that carbon chains are cospatial with NH3 and trace quiescent gas, and that inefficient clumps would have a uniform age distribution; both are stated and discussed.

free parameters (4)
  • Excitation temperature Tex = 5 K (adopted range 4-6 K)
    Chosen from literature values for NH3 and carbon chains in other dark clouds (Pillai et al. 2006; Vastel et al. 2018; Bianchi et al. 2023); not measured in these sources. Directly scales all carbon chain column densities via Eq. 2 and the partition function.
  • Dust opacity kappa_nu = 1.85 cm^2 g^-1 at 870 micron
    Ossenkopf and Henning (1994) model for coagulated thin-ice-mantle grains at 1e5 cm^-3 for 1e5 yr. Scales the H2 column density and therefore the HC5N abundance relative to H2.
  • Gas-to-dust ratio = 100
    Assumed standard value used to convert dust mass to H2 mass in Section 4.3.
  • Clump volume (spherical approximation) = V = (4/3) pi R^3 with R = sqrt(A/pi)
    The 3D volume of each clump is estimated from the 2D mask area assuming a sphere; this sets the volume density n(H2) used for the fiducial UMIST model. A geometric approximation rather than a fit.
assumptions (7)
  • domain assumption The observed carbon chain lines are optically thin and the level populations are in LTE at a single excitation temperature (Eqs. 1-3 and Eq. 2).
    Column densities depend on this; with only one transition per species, Tex cannot be verified, and at n ~ 1e4 cm^-3 sub-thermal excitation is expected (Section 4.2).
  • domain assumption The adopted excitation temperature range 4-6 K is representative of these clumps.
    No direct excitation measurement; values are borrowed from other IRDCs, L1544, and TMC-1 (Section 4.2).
  • domain assumption UMIST 13 Dark Cloud chemistry models correctly describe the time evolution of HC5N and HC7N abundances in these clumps.
    The age bounds are read off these model curves; the model is gas-phase only, and the authors estimate order-of-magnitude abundance uncertainties from the chemistry (Sections 4.5 and 5.2.3).
  • domain assumption The carbon chain molecules are cospatial with the NH3 emission used as the extraction mask and with the dust continuum.
    Spatial matching is supported by moment 0 maps, but stratification cannot be excluded; the authors note potential flux loss or abundance normalization changes (Sections 4.3 and 5.2.4).
  • domain assumption Clump density and temperature are uniform and time-invariant in the model comparison.
    The UMIST models are run at fixed n(H2), Tk, and Av; real clumps evolve and are internally structured (Section 4.5).
  • ad hoc to paper If the clumps were inefficient at forming high-mass stars, their ages would be uniformly distributed between 0 and 21 Myr.
    This prior underlies the probability ~3e-15 that all 11 sources are <1 Myr by chance (Section 5.4); it is a modeling choice, not an observed distribution.
  • standard math Standard rigid-rotor molecular physics and partition functions from CDMS.
    Used in Eqs. 1-3 for column densities; standard molecular spectroscopy.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Young Ages of 70 {\mu}m-dark Clumps Inferred from Carbon Chain Chemistry." pith.science (2026). https://pith.science/paper/TWLE6OQG

@misc{pith2026250204283,
  author       = {Pith},
  title        = {Pith review of: The Young Ages of 70 \mum-dark Clumps Inferred from Carbon Chain Chemistry},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TWLE6OQG}},
  note         = {Machine review of arXiv:2502.04283}
}
abstract

The physical conditions of the earliest environment of high-mass star formation are currently poorly understood. To that end, we present observations of the carbon chain molecules HC$_5$N , CCS, and HC$_7$N in the 22-25 GHz band towards 12 high-mass 70 micron-dark clumps (SMDC) with the Jansky Very Large Array (VLA). We detect HC$_5$N and CCS towards 11 of these SMDC sources. We calculate column densities and abundances relative to H$_2$ for HC$_5$N and CCS. We do not find any clear HC$_7$N detections in the 11 sources individually, but by stacking the HC$_7$N spectra, we do detect HC$_7$N on average in these sources. We also calculate the ratio of the column densities of HC$_5$N to HC$_7$N using the stacked spectra of both species. We compare our measured abundances of HC$_5$N and our measured ratio of HC$_5$N to HC$_7$N to the UMIST dark cloud chemistry models to constrain an age for the gas assuming a fixed volume density and temperature. The chemical models favor a chemical evolutionary age less than 1 Myr at densities of n(H2) = 2 x 10$^4$ cm$^{-3}$. The consistent carbon-chain detections and young model-derived ages support the conclusion that these 11 70 micron-dark clumps lack high mass protostars because they are young and not because they are inefficient and incapable of high mass star formation.

Figures

Figures reproduced from arXiv: 2502.04283 by the authors.

Figure 1
Figure 1. Left: moment 0 map of HC5N for the source G24051 with NH3 contours overlaid. In this source, the NH3 contour encloses the emission from HC5N. Right: moment 0 map of NH3 (1,1) for the same source as left figure, G24051. The moment calculation was restricted to include only positive values. The red contour line indicates the brightness threshold that was used for the carbon chain extraction aperture in the masking met… view at source ↗
Figure 2
Figure 2. ATLASGAL images at 870µm for all 12 sources with the 3σ NH3 contours overlaid. These contours show the masks used for the extraction of the carbon chain spectra as well as the aperture used to measure the flux from the ATLASGAL images for the calculation of H2 column density. In each of the sources, the NH3 and the dust continuum emission appear to be co-spatial. Note that the coordinates for these images are Galact… view at source ↗
Figure 3
Figure 3. Spectra of HC5N for all 12 of our sources (blue) with best fit Gaussians (yellow). The only non-detection of HC5N in on 12 sources occurs in G23605, which is the upper right hand corner spectrum in this figure. The black dashed line shows the systematic velocity from Svoboda et al. (2016). G30660 is the only spectrum shown acquired through the velocity registration method. G29601 and G30120 are scaled for clarity. 6… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Spectra of CCS for all 12 of our sources (blue) with best fit Gaussians (yellow). The only non-detection of CCS in on 12 sources occurs in G23605, which is the upper right hand corner spectrum in this figure. The black dashed lines show the systematic velocity. Velocit…
Figure 5
Figure 5. Figure 5: Stacked spectrum of HC7N. The stack includes all ob￾served transitions of HC7N for all sources with HC5N detections. °10 0 10 20 Velocity (km/s) 0.00 0.05 0.10 0.15 Brightness Temperature (K) [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Stacked spectrum of HC5N. The stack includes all sources of HC5N with detections. model predicted carbon chain abundance is negligible (less than 5%) and the age estimation of the clumps is unchanged. 5.2.3. Chemical Network Our choice of chemical model has the potenti…
Figure 7
Figure 7. Figure 7: Plot on left shows HC5N column density as a function of the clump kinetic temperature. Figure on the right shows CCS column density as a function of HC5N column density. 2 3 4 5 6 7 log(time) [years] −15 −14 −13 −12 −11 −10 −9 −8 −7 log(Abundance) n(H2)= 1.2e4 cm−3 n(H…
Figure 8
Figure 8. Figure 8: Plot of abundance of HC5N as a function of age of the clump. The blue line shows the HC5N abundance predicted by the UMIST 13 chemistry model using a clump density of n(H2)=2.0×104 cm−3 , the black line shows a model with a density of n(H2)=3.1×104 cm−3 , and the green…
Figure 9
Figure 9. Figure 9: Ratio of HC5N to HC7N as a function of time. The blue, green, and black lines show the abundances predicted by the UMIST 13 dark cloud chemistry model at the densities indicated and the red colored area shows our measured ratio with uncertainties. spectra could result …
Figure 10
Figure 10. Figure 10: HC5N moment 0 maps of the remaining 10 sources with carbon chain detections (excluding G24051, which was shown in the main text) with NH3 contour overlaid. This is the same NH3 mask that was used for the spectral extraction [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

85 extracted references · 23 canonical work pages

  1. [1]

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

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

  2. [2]

    write newline

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

  3. [3]

    Q5 ː 5 aNw 9A yك# η4r]Н)LD)d-99U X /oz TSfL /` n

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

    2021

  4. [4]

    E., Ginsburg , A

    Aguirre , J. E., Ginsburg , A. G., Dunham , M. K., et al. 2011, , 192, 4, 10.1088/0067-0049/192/1/4

    doi:10.1088/0067-0049/192/1/4 2011

  5. [5]

    2013, Chemical Reviews, 113, 8710, 10.1021/cr4001176

    Ag \'u ndez , M., & Wakelam , V. 2013, Chemical Reviews, 113, 8710, 10.1021/cr4001176

    doi:10.1021/cr4001176 2013

  6. [6]

    J., Kroto , H

    Alexander , A. J., Kroto , H. W., & Walton , D. R. M. 1976, Journal of Molecular Spectroscopy, 62, 175, 10.1016/0022-2852(76)90347-7

    doi:10.1016/0022-2852(76)90347-7 1976

  7. [7]

    M., et al

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

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

  8. [8]

    2017, , 835, 263, 10.3847/1538-4357/835/2/263

    Battersby , C., Bally , J., & Svoboda , B. 2017, , 835, 263, 10.3847/1538-4357/835/2/263

    doi:10.3847/1538-4357/835/2/263 2017

Show all 85 references
  1. [9]

    J., Maret , S., et al

    Belloche , A., Maury , A. J., Maret , S., et al. 2020, , 635, A198, 10.1051/0004-6361/201937352

    2020 doi

  2. [10]

    A., & Tafalla , M

    Bergin , E. A., & Tafalla , M. 2007, , 45, 339, 10.1146/annurev.astro.45.071206.100404

    2007

  3. [11]

    B., McKee , C

    Beuther , H., Churchwell , E. B., McKee , C. F., & Tan , J. C. 2007, in Protostars and Planets V, ed. B. Reipurth , D. Jewitt , & K. Keil , 165. astro-ph/0602012

    2007 arXiv

  4. [12]

    2023, , 944, 208, 10.3847/1538-4357/acb5e8

    Bianchi , E., Remijan , A., Codella , C., et al. 2023, , 944, 208, 10.3847/1538-4357/acb5e8

    2023 doi

  5. [13]

    2004, , 614, 518, 10.1086/423370

    Bizzocchi , L., & Degli Esposti , C. 2004, , 614, 518, 10.1086/423370

    2004 doi

  6. [14]

    2004, Journal of Molecular Spectroscopy, 225, 145, 10.1016/j.jms.2004.02.019

    Bizzocchi , L., Degli Esposti , C., & Botschwina , P. 2004, Journal of Molecular Spectroscopy, 225, 145, 10.1016/j.jms.2004.02.019

    2004 doi

  7. [15]

    M., Herbst , E., Kalenskii , S

    Burkhardt , A. M., Herbst , E., Kalenskii , S. V., et al. 2018, , 474, 5068, 10.1093/mnras/stx2972

    2018 doi

  8. [16]

    Draine , B. T. 1978, , 36, 595, 10.1086/190513

    1978 doi

  9. [17]

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

    Dunham , M. M., Crapsi , A., Evans , Neal J., I., et al. 2008, , 179, 249, 10.1086/591085

    2008 doi

  10. [18]

    P., Schlemmer , S., Schilke , P., Stutzki , J., & M \"u ller , H

    Endres , C. P., Schlemmer , S., Schilke , P., Stutzki , J., & M \"u ller , H. S. P. 2016, Journal of Molecular Spectroscopy, 327, 95, 10.1016/j.jms.2016.03.005

    2016 doi

  11. [19]

    E., Blake , G

    Evans , Neal J., I., Allen , L. E., Blake , G. A., et al. 2003, , 115, 965, 10.1086/376697

    2003 doi

  12. [20]

    2016, , 828, 100, 10.3847/0004-637X/828/2/100

    Feng , S., Beuther , H., Zhang , Q., et al. 2016, , 828, 100, 10.3847/0004-637X/828/2/100

    2016 doi

  13. [21]

    2020, , 901, 145, 10.3847/1538-4357/abada3

    Feng , S., Li , D., Caselli , P., et al. 2020, , 901, 145, 10.3847/1538-4357/abada3

    2020 doi

  14. [22]

    1998, , 505, 278, 10.1086/306168

    Fukuzawa , K., Osamura , Y., & Schaefer , Henry F., I. 1998, , 505, 278, 10.1086/306168

    1998 doi

  15. [23]

    2014, , 563, A97, 10.1051/0004-6361/201322541

    Gerner , T., Beuther , H., Semenov , D., et al. 2014, , 563, A97, 10.1051/0004-6361/201322541

    2014 doi

  16. [24]

    2022, , 163, 291, 10.3847/1538-3881/ac695a

    Ginsburg , A., Sokolov , V., de Val-Borro , M., et al. 2022, , 163, 291, 10.3847/1538-3881/ac695a

    2022 doi

  17. [25]

    2013, , 208, 14, 10.1088/0067-0049/208/2/14

    Ginsburg , A., Glenn , J., Rosolowsky , E., et al. 2013, , 208, 14, 10.1088/0067-0049/208/2/14

    2013 doi

  18. [26]

    2009, , 699, 585, 10.1088/0004-637X/699/1/585

    Hirota , T., Ohishi , M., & Yamamoto , S. 2009, , 699, 585, 10.1088/0004-637X/699/1/585

    2009 doi

  19. [27]

    Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90, 10.1109/MCSE.2007.55

    2007 doi

  20. [28]

    Jeffreson , S. M. R., & Kruijssen , J. M. D. 2018, , 476, 3688, 10.1093/mnras/sty594

    2018 doi

  21. [29]

    V., Slysh , V

    Kalenskii , S. V., Slysh , V. I., Goldsmith , P. F., & Johansson , L. E. B. 2004, , 610, 329, 10.1086/421456

    2004 doi

  22. [30]

    2010, , 723, L7, 10.1088/2041-8205/723/1/L7

    Kauffmann , J., & Pillai , T. 2010, , 723, L7, 10.1088/2041-8205/723/1/L7

    2010 doi

  23. [31]

    Kauffmann , J., Pillai , T., & Goldsmith , P. F. 2013, , 779, 185, 10.1088/0004-637X/779/2/185

    2013 doi

  24. [32]

    E., & Dunham , M

    Kristensen , L. E., & Dunham , M. M. 2018, , 618, A158, 10.1051/0004-6361/201731584

    2018 doi

  25. [33]

    Kukolich , S. G. 1967, Physical Review, 156, 83, 10.1103/PhysRev.156.83

    1967 doi

  26. [34]

    J., & Lada , E

    Lada , C. J., & Lada , E. A. 2003, , 41, 57, 10.1146/annurev.astro.41.011802.094844

    2003 arXiv

  27. [35]

    M., & Wakelam , V

    Loison , J.-C., Halvick , P., Bergeat , A., Hickson , K. M., & Wakelam , V. 2012, , 421, 1476, 10.1111/j.1365-2966.2012.20412.x

    2012

  28. [36]

    M., Bergeat , A., & Mereau , R

    Loison , J.-C., Wakelam , V., Hickson , K. M., Bergeat , A., & Mereau , R. 2014, , 437, 930, 10.1093/mnras/stt1956

    2014 doi

  29. [37]

    J., Suenram , R

    Lovas , F. J., Suenram , R. D., Ogata , T., & Yamamoto , S. 1992, , 399, 325, 10.1086/171928

    1992 doi

  30. [38]

    J., et al

    McElroy , D., Walsh , C., Markwick , A. J., et al. 2013, , 550, A36, 10.1051/0004-6361/201220465

    2013 doi

  31. [39]

    F., & Ostriker , E

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

    2007 arXiv

  32. [40]

    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

  33. [41]

    P., Waters , B., Schiebel , D., Young , W., & Golap , K

    McMullin , J. P., Waters , B., Schiebel , D., Young , W., & Golap , K. 2007, in Astronomical Society of the Pacific Conference Series, Vol. 376, Astronomical Data Analysis Software and Systems XVI, ed. R. A. Shaw , F. Hill , & D. J. Bell , 127

    2007

  34. [42]

    2018, , 475, 5501, 10.1093/mnras/sty180

    Mendoza , E., Lefloch , B., Ceccarelli , C., et al. 2018, , 475, 5501, 10.1093/mnras/sty180

    2018 doi

  35. [43]

    2016, , 826, L8, 10.3847/2041-8205/826/1/L8

    Molinari , S., Merello , M., Elia , D., et al. 2016, , 826, L8, 10.3847/2041-8205/826/1/L8

    2016 doi

  36. [44]

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

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

    2018 doi

  37. [45]

    Oliphant, T. E. 2007, Computing in Science & Engineering, 9

    2007

  38. [46]

    1994, , 291, 943

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

    1994

  39. [47]

    Peretto , N., & Fuller , G. A. 2009, VizieR Online Data Catalog, J/A+A/505/405

    2009

  40. [48]

    P \'e rez, F., & Granger, B. E. 2007, Computing in Science & Engineering, 9

    2007

  41. [49]

    J., & Menten , K

    Pillai , T., Wyrowski , F., Carey , S. J., & Menten , K. M. 2006, , 450, 569, 10.1051/0004-6361:20054128

    2006 doi

  42. [50]

    2018, , 474, 2796, 10.1093/mnras/stx2960

    Qu \'e nard , D., Jim \'e nez-Serra , I., Viti , S., Holdship , J., & Coutens , A. 2018, , 474, 2796, 10.1093/mnras/stx2960

    2018 doi

  43. [51]

    M., Jackson , J

    Rathborne , J. M., Jackson , J. M., & Simon , R. 2006, , 641, 389, 10.1086/500423

    2006 doi

  44. [52]

    M., Lada , C

    Rathborne , J. M., Lada , C. J., Muench , A. A., Alves , J. F., & Lombardi , M. 2008, , 174, 396, 10.1086/522889

    2008 doi

  45. [53]

    D., Luna , A., & Carrasco , L

    Retes-Romero , R., Mayya , Y. D., Luna , A., & Carrasco , L. 2020, , 897, 53, 10.3847/1538-4357/ab93ac

    2020 doi

  46. [54]

    K., Ginsburg , A., et al

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

    2010 doi

  47. [55]

    W., Pineda , J

    Rosolowsky , E. W., Pineda , J. E., Foster , J. B., et al. 2008, , 175, 509, 10.1086/524299

    2008 doi

  48. [56]

    2016, , 459, 3756, 10.1093/mnras/stw887

    Ruaud , M., Wakelam , V., & Hersant , F. 2016, , 459, 3756, 10.1093/mnras/stw887

    2016 doi

  49. [57]

    2021, , 652, A71, 10.1051/0004-6361/202140469

    Sabatini , G., Bovino , S., Giannetti , A., et al. 2021, , 652, A71, 10.1051/0004-6361/202140469

    2021 doi

  50. [58]

    1987, , 317, L115, 10.1086/184923

    Saito , S., Kawaguchi , K., Yamamoto , S., et al. 1987, , 317, L115, 10.1086/184923

    1987 doi

  51. [59]

    2008, , 672, 371, 10.1086/523635

    Sakai , N., Sakai , T., Hirota , T., & Yamamoto , S. 2008, , 672, 371, 10.1086/523635

    2008 doi

  52. [60]

    2013, Chemical Reviews, 113, 8981, 10.1021/cr4001308

    Sakai , N., & Yamamoto , S. 2013, Chemical Reviews, 113, 8981, 10.1021/cr4001308

    2013 doi

  53. [62]

    2012 b , , 756, 60, 10.1088/0004-637X/756/1/60

    ---. 2012 b , , 756, 60, 10.1088/0004-637X/756/1/60

    2012 doi

  54. [64]

    2009 b , , 504, 415, 10.1051/0004-6361/200811568

    ---. 2009 b , , 504, 415, 10.1051/0004-6361/200811568

    2009 doi

  55. [65]

    M., Majumdar , L., Goldsmith , P

    Seo , Y. M., Majumdar , L., Goldsmith , P. F., et al. 2019, , 871, 134, 10.3847/1538-4357/aaf887

    2019 doi

  56. [66]

    P., & McCall , B

    Snow , T. P., & McCall , B. J. 2006, , 44, 367, 10.1146/annurev.astro.43.072103.150624

    2006 arXiv

  57. [67]

    2016, , 592, L11, 10.1051/0004-6361/201628652

    Spezzano , S., Bizzocchi , L., Caselli , P., Harju , J., & Br \"u nken , S. 2016, , 592, L11, 10.1051/0004-6361/201628652

    2016 doi

  58. [68]

    1992, , 392, 551, 10.1086/171456

    Suzuki , H., Yamamoto , S., Ohishi , M., et al. 1992, , 392, 551, 10.1086/171456

    1992 doi

  59. [69]

    E., Shirley , Y

    Svoboda , B. E., Shirley , Y. L., Battersby , C., et al. 2016, , 822, 59, 10.3847/0004-637X/822/2/59

    2016 doi

  60. [70]

    E., Shirley , Y

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

    2019 doi

  61. [71]

    X., Qin , S.-L., et al

    Tang , M., Ge , J. X., Qin , S.-L., et al. 2019, , 887, 243, 10.3847/1538-4357/ab5447

    2019 doi

  62. [72]

    Taniguchi , K., Gorai , P., & Tan , J. C. 2024, , 369, 34, 10.1007/s10509-024-04292-9

    2024 doi

  63. [74]

    2019 b , , 881, 57, 10.3847/1538-4357/ab2d9e

    ---. 2019 b , , 881, 57, 10.3847/1538-4357/ab2d9e

    2019 doi

  64. [75]

    2017, , 69, L7, 10.1093/pasj/psx065

    Taniguchi , K., & Saito , M. 2017, , 69, L7, 10.1093/pasj/psx065

    2017 doi

  65. [76]

    K., & Minamidani , T

    Taniguchi , K., Saito , M., Sridharan , T. K., & Minamidani , T. 2018, , 854, 133, 10.3847/1538-4357/aaa66f

    2018 doi

  66. [77]

    2017, , 228, 12, 10.3847/1538-4365/228/2/12

    Tatematsu , K., Liu , T., Ohashi , S., et al. 2017, , 228, 12, 10.3847/1538-4365/228/2/12

    2017 doi

  67. [78]

    M., Sip o cz , B

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

    2018 doi

  68. [79]

    A., Peretto , N., Pineda , J

    Traficante , A., Fuller , G. A., Peretto , N., Pineda , J. E., & Molinari , S. 2015, , 451, 3089, 10.1093/mnras/stv1158

    2015 doi

  69. [80]

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

    Urquhart , J. S., K \"o nig , C., Giannetti , A., et al. 2018, , 473, 1059, 10.1093/mnras/stx2258

    2018 doi

  70. [81]

    2018, , 478, 5514, 10.1093/mnras/sty1336

    Vastel , C., Qu \'e nard , D., Le Gal , R., et al. 2018, , 478, 5514, 10.1093/mnras/sty1336

    2018 doi

  71. [82]

    I., Sobolev , A

    Vasyunin , A. I., Sobolev , A. M., Wiebe , D. S., & Semenov , D. A. 2004, Astronomy Letters, 30, 566, 10.1134/1.1784498

    2004 doi

  72. [83]

    M., Winnewisser , G., & Toelle , F

    Walmsley , C. M., Winnewisser , G., & Toelle , F. 1980, , 81, 245

    1980

  73. [84]

    M., Wiesemeyer , H., & Klein , B

    Wyrowski , F., G \"u sten , R., Menten , K. M., Wiesemeyer , H., & Klein , B. 2012, , 542, L15, 10.1051/0004-6361/201218927

    2012 doi

  74. [85]

    M., et al

    Wyrowski , F., G \"u sten , R., Menten , K. M., et al. 2016, , 585, A149, 10.1051/0004-6361/201526361

    2016 doi

  75. [86]

    2017, Introduction to Astrochemistry: Chemical Evolution from Interstellar Clouds to Star and Planet Formation , 10.1007/978-4-431-54171-4

    Yamamoto , S. 2017, Introduction to Astrochemistry: Chemical Evolution from Interstellar Clouds to Star and Planet Formation , 10.1007/978-4-431-54171-4

    2017 doi

  76. [87]

    E., Sakai , N., et al

    Zhang , Y., Higuchi , A. E., Sakai , N., et al. 2018, , 864, 76, 10.3847/1538-4357/aad7ba

    2018 doi

  77. [88]

    2023, , 523, 2770, 10.1093/mnras/stad1604

    Zhu , F.-Y., Wang , J., Yan , Y., Zhu , Q.-F., & Li , J. 2023, , 523, 2770, 10.1093/mnras/stad1604

    2023 doi

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.