REVIEW 4 major objections 5 minor 75 references
Why is the Star Formation Rate Proportional to Dense Gas Mass?
T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read The paper claims that the Gao-Solomon star formation law is not a statistical sampling effect but the product of three deterministic correlations, anchored by a new scaling between the most massive core and the gravitationally bound gas…
desk verdict The M_max_core-M_bound_gas correlation is a real, new observational result; the Gao-Solomon explanation is a plausible but not closed chain of assumptions. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing object is the $M^{\rm max}_{\rm core}$-$M^{\rm bound}_{\rm gas}$ scaling, measured at matched 0.03 pc resolution: for nearby clouds from Herschel column density maps and for distant clouds from ALMA-IMF 1.3 mm continuum, with the same source-extraction pipeline applied to both samples. The bound gas mass is defined as the mass above the turning point where the column-density probability distribution function (N-PDF) goes from log-normal to power-law, i.e., the self-gravitating tail, identified by maximum-likelihood and MCMC fitting for each cloud. The argument's engine is the exponent chain of Eq. 9: since ${\rm SFR} \propto (m^{\rm max}_{\rm star})^{2.04}$ and $m^{\rm max}_{\rm star} \propto M^{\rm max}_{\rm core}$ while $M^{\rm max}_{\rm core} \propto (M^{\rm bound}_{\rm gas})^{0.506}$, multiplying the exponents gives ${\rm SFR} \propto (M^{\rm bound}_{\rm gas})^{0.506\times2.04} \approx (M^{\rm bound}_{\rm gas})^{1.03}$; the near-unity slope is what turns two unrelated power laws into the observed linear star-formation law.
What would settle it
Measure, in the same star-forming regions, both the gravitationally bound gas mass from column-density PDFs and the dense gas mass as Gao and Solomon defined it (e.g., HCN J=1-0 luminosity under their conversion assumptions): if the two are not proportional across the sample, the comparison in Figure 3 is not actually against the Gao-Solomon relation and the claimed chain collapses. As an independent check, resolve the most massive cores and weigh the star or protostar each one actually forms; an efficiency far from 30% would destroy the normalization of the predicted SFR.
Extended reading notes
Core claim
The central discovery is the correlation $\log(M^{\rm max}_{\rm core}/M_{\odot}) = 0.506\,\log(M^{\rm bound}_{\rm gas}/M_{\odot}) - 0.32$ (Eq. 7), with a Spearman coefficient of 0.83, spanning $M^{\rm bound}_{\rm gas}$ from roughly $10^2$ to $10^5\,M_{\odot}$ in 23 molecular clouds; excluding four fields with known missing short-spacing flux steepens the slope to 0.55. The paper then shows that inserting this relation into two previously proposed links, a constant 30% efficiency converting the most massive core into the most massive star and the theoretical relation $\log({\rm SFR}/(M_{\odot}\,{\rm yr}^{-1})) = 2.04\,\log(m^{\rm max}_{\rm star}/M_{\odot}) - 5.80$ (Eq. 8), produces ${\rm SFR} \propto (M^{\rm bound}_{\rm gas})^{1.03}$, matching the Gao-Solomon relation once it is rescaled upward by the factor 2.7 used in the literature. The paper argues that the Gao-Solomon relation is therefore the combination of three non-trivial correlations, one of them new: (i) SFR versus $m^{\rm max}_{\rm star}$, (ii) $m^{\rm max}_{\rm star}$ versus $M^{\rm max}_{\rm core}$, and (iii) $M^{\rm max}_{\rm core}$ versus $M^{\rm bound}_{\rm gas}$. It also argues that random sampling of a canonical core mass function cannot reproduce the tightness or the normalization of the observed core-gas correlation: for Taurus and Perseus, the chance of drawing 172 cores above $3\,M_{\odot}$ with none exceeding $41.1\,M_{\odot}$ is at most about $8\times10^{-3}$, so the stochastic picture is rejected at better than 99.1% confidence. The paper notes honestly that the galactic-scale SFR-$m^{\rm max}_{\rm star}$ relation turning out to hold on individual cloud scales is unexplained and could be a coincidence.
Load-bearing premise
Two premises carry the whole chain: that the gravitationally bound gas mass equals the dense gas mass of the Gao-Solomon relation, an identification the paper asserts in a footnote and leaves to a companion paper, and that every most massive core converts to its most massive star at an exactly constant 30% efficiency.
Editorial extensions
If this is right
- The Gao-Solomon relation becomes a corollary of the new core-gas scaling: chaining Eq. 7 through the 30% efficiency and Eq. 8 yields a slope of 1.03, so the linear law needs no averaging over many clouds to emerge.
- The same deterministic recipe works for single low-mass clouds like Taurus and massive regions like W43 and W51, implying that star formation from cloud scale to galaxy scale follows one chain.
- Random sampling of the core mass function is ruled out as the origin of the most massive core: simulated clouds with canonical CMF slopes produce systematically heavier and more scattered most-massive cores than observed, so ten Taurus-mass clouds would not, in this picture, collectively form OB stars.
- A single cloud measurement becomes predictive: given the gravitationally bound gas mass, the recipe yields both the expected most massive star and the expected star formation rate of the region.
Reading between the lines
- If the chain holds, the exponent product 0.506 × 2.04 ≈ 1.03 is a near-coincidence: environments that shift either exponent (temperature, metallicity, turbulence, or a different column-density break) should shift the Gao-Solomon slope away from unity, a testable prediction for galaxies with unusual star-formation efficiencies.
- The core-gas correlation rests on only 23 regions; an extension the paper leaves implicit is testing whether the same 0.506 slope holds across substructures within a single giant molecular cloud, which would separate a universal deterministic law from an artifact of averaging a few regions.
- If the star-formation-rate versus most-massive-star relation truly holds at cloud scale, the upper end of the stellar initial mass function encodes the current star formation rate; resolving the stellar content of the most massive cores in this sample would test that link directly.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes an explanation for the linear Gao-Solomon star formation law (SFR proportional to dense gas mass) using a chain of three correlations: SFR vs. the most massive star, the most massive star vs. the most massive core, and the most massive core vs. the gravitationally bound gas mass. Using archival Herschel and ALMA-IMF data for 11 nearby and 12 distant star-forming regions, the authors measure M_max_core and M_bound_gas and report log(M_max_core/M_sun) = 0.506 log(M_bound_gas/M_sun) - 0.32 (Eq. 7). Assuming a 30% efficiency to convert M_max_core to m_max_star and adopting an SFR-m_max_star relation from Yan et al. (2017) (Eq. 8), they derive SFR proportional to M_bound_gas^1.03, matching the Gao-Solomon relation (Fig. 3). They further argue, via Monte Carlo simulations, that the observed M_max_core-M_bound_gas relation is too tight to arise from random sampling of a standard core mass function.
Significance. If the central claim holds, the paper identifies a new empirical scaling relation between the most massive core and the gravitationally bound gas mass, and it offers an alternative to the stochastic-sampling picture for the origin of the Gao-Solomon relation. The analysis has notable strengths: the core extraction is applied consistently at the same physical resolution across both nearby and distant samples, the correlation is robust to the three adopted dust temperature estimators (Appendix C), and the Monte Carlo test in Section 4.2 explicitly quantifies the tension with random CMF sampling. The M_max_core-M_bound_gas correlation itself appears to be a real and useful observational result. However, the conversion of this correlation into a derivation of the Gao-Solomon relation rests on several load-bearing assumptions that are not independently established in this manuscript, so the explanatory claim is not yet secured.
major comments (4)
- [§4.1, Footnote 3 and Figure 3] The comparison in Figure 3 tests the Gao-Solomon relation only if M_bound_gas can be substituted for M_dense_gas. This equivalence is asserted in Footnote 3, with the actual validation deferred entirely to Jiao et al. (submitted), a companion paper that is not available for evaluation. The manuscript does not quantify M_bound_gas/M_dense_gas for the sample or show that this ratio is constant over the three orders of magnitude in mass. If the ratio varies with cloud mass or with star formation activity, then both the slope and the normalization of the derived SFR-M_bound_gas relation change, and Figure 3 is not a comparison against the observed Gao-Solomon relation. The authors should either present a direct demonstration of M_bound_gas ~ M_dense_gas for these clouds or reframe the Gao-Solomon comparison as a conditional prediction rather than an empirical test.
- [§4.1, Eq. (8)] Equation (8) is taken from a theoretical model (Yan et al. 2017) and then, later in the same section, the paper states that it 'can also be regarded as empirically constrained by the presented data.' But the presented data constrain Eq. (8) only through Eq. (7), the assumed 30% efficiency, and the M_bound_gas ≈ M_dense_gas substitution. Using Eq. (8) both as input to the recipe and as part of the consistency check in Figure 3 makes the agreement partially circular. The authors should separate what is assumed from what is tested; an independent calibration of Eq. (8) from directly measured m_max_star values or from YSO-count SFRs would resolve this concern.
- [§4.1, recipe step 2] The constant 30% star-forming efficiency converting M_max_core to m_max_star is assumed without direct measurement, and it sets the zero-point of the derived SFR. The agreement in Figure 3 therefore does not validate the normalization of the hypothesized relation; a different assumed SFE would shift the derived relation vertically while preserving its slope. The authors should present the sensitivity of the SFR-M_bound_gas comparison to the assumed SFE and provide any empirical constraints that favor ~30% specifically.
- [§3.3 and Eq. (7)] The fiducial slope 0.506 in Eq. (7) is quoted without an uncertainty, and it changes to 0.55 when four ALMA-IMF fields with known missing flux are excluded; the three temperature methods in Appendix C yield slopes of 0.506, 0.554, and 0.575, a spread of about 0.07. Since the exponent chain in Eq. (9) uses 0.506 directly, the claim that the final relation is 'approximately linear' ('slope 1.03') should carry the propagated uncertainty from these choices. Reporting a central slope with a systematic uncertainty and showing how the derived SFR-M_bound_gas slope changes under the alternative assumptions would make the result quantitative rather than suggestive.
minor comments (5)
- [Figure 2 caption] The caption states the scaling relation as M_bound_gas ∝ (M_max_core)^0.506, which is inverted relative to Eq. (7); the text and figure should be consistent about which quantity is the independent variable.
- [§4.2, step 1] The simulation step says the core sample pool follows 'the CMF (Equation 8)', but Equation 8 is the SFR-m_max_star relation; the CMF power law is Equation 10.
- [Footnote 3] Footnote 3 contains a typo, 'measured in the say described in Section 3.2'; it should read 'measured in the way described in Section 3.2'.
- [Footnote 1] Footnote 1 refers to 'Footenote 3'; the spelling should be 'Footnote 3'.
- [§5, Conclusion] The opening sentence refers to 'Hershel'; the observatory name should be 'Herschel'.
Circularity Check
Gao-Solomon 'derivation' is partially self-referential: Figure 3's slope is forced by the fitted Eq. 7 combined with same-author Eq. 8, while the Mbound≈Mdense substitution and the 'empirical constraint' of Eq. 8 depend on same-author companion work.
-
fitted input called prediction
[Section 4.1, recipe for SFRtheory and Figure 3 (Eqs. 7-9)]
"With this recipe, the measurements of Mbound_gas in our sample then allow us to infer SFRtheory (see Appendix D for a comparison with the observed SFR) and a relation between SFRtheory and Mbound_gas, which can be compared with the Gao-Solomon relation. ... We plot the SFR theory and Mbound_gas of our sample in Figure 3. The black dashed line shows a linear regression of all the data points, yielding a slope of 1.03 across all clouds."
SFRtheory is computed from Equation 7, which was fitted to the same Mbound_gas values that are then regressed in Figure 3, via SFRtheory = f(Eq7(Mbound), Eq8, SFE=0.3). The resulting slope is algebraically fixed: 0.506 x 2.04 = 1.03, as the paper itself states in Eq. 9. Thus the 'predicted' linear SFR-Mbound relation is not an independent measurement of the Gao-Solomon slope; it is the fitted exponent of Eq. 7 transported through an imported theoretical exponent. Only the normalization carries new information, and that normalization depends on the assumed 30% SFE and the intercept of Eq. 8.
-
self citation load bearing
[Footnote 3]
"Nevertheless, these two quantities are not unrelated. In many but not all star-forming regions, we found M bound gas ∼ M dense gas if M dense gas is measured in the same way as Gao & Solomon (2004) while M bound gas is measured in the say described in Section 3.2 (Jiao et al. submitted; also see the related discussion in Bonnell et al. 2003; Xu et al. 2023; Vázquez-Semadeni et al. 2017)."
The Gao-Solomon relation is defined for Mdense_gas, but Figure 3 plots SFRtheory against Mbound_gas and overlays the Gao-Solomon line. The identification Mbound_gas ≈ Mdense_gas is asserted for 'many but not all' regions and is deferred entirely to Jiao et al. (submitted), a companion paper by overlapping authors that is not available for evaluation. If this equivalence is not quantitative in the same star-forming regions, the overlay in Figure 3 is not a test of the Gao-Solomon relation. This is load-bearing self-citation: the central comparison rests on an unverifiable equivalence imported from the authors' own unpublished work.
1 more flagged steps
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self citation load bearing
[Section 4.1, paragraph after Eq. 8]
"At this moment, we can also regard Equation 8 as a relation being empirically constrained by the presented data, while the theoretical derivation of Yan et al. (2017) is one way to comprehend this empirical relation."
Equation 8 (log SFR = 2.04 log mmax_star - 5.80) is first introduced as a theoretical relation from Yan et al. (2017), a paper with a present co-author (Z. Yan). The claim that the presented data empirically constrain Eq. 8 is circular: the data can reach Eq. 8 only through Equation 7 (fitted to the same clouds), the assumed constant 30% SFE, and the Mbound≈Mdense substitution. No direct measurement of mmax_star or of the SFR-mmax_star relation is presented. The consistency with Gao-Solomon is generated by the same chain that Eq. 8 is used to construct, so this sentence makes part of the validation loop self-referential.
full rationale
The paper contains real independent content: the observed Mmax_core-Mbound_gas correlation (Eq. 7) is a new measurement, robust to the dust-temperature method (Figure 9), and the Section 4.2 test against random sampling of the CMF is an external-style falsification of the stochastic picture. However, the conversion of Eq. 7 into a 'derivation' of the Gao-Solomon relation is only partially independent. The slope in Figure 3 (1.03) is not a measured prediction: it is the product of the fitted exponent 0.506 and the imported theoretical exponent 2.04, as the paper's own Eq. 9 states. The normalization is set by an assumed 30% star-forming efficiency rather than by a direct calibration. Two load-bearing links are supplied by same-author sources: the Mbound_gas ≈ Mdense_gas equivalence is deferred to Jiao et al. (submitted), and Equation 8 is taken from Yan et al. (2017) and then described as 'empirically constrained by the presented data' even though the data constrain it only through the same fitted chain. These are genuine partial circularities in the central derivation, though they do not erase the independent value of the Eq. 7 measurement or the stochastic-sampling argument.
Assumptions & free parameters
free parameters (4)
- Star-forming efficiency (SFE) =
0.3
- SFR-m_max_star relation slope and intercept =
2.04, -5.80
- CMF power-law slopes in Monte Carlo test =
-1.0, -1.35
- Maximum possible core mass in simulation =
m_up/SFE with m_up = 150 or 100 M_sun
assumptions (5)
- domain assumption The N-PDF power-law tail traces self-gravitating, bound gas.
- domain assumption M_bound_gas approximates M_dense_gas in the Gao-Solomon relation.
- ad hoc to paper The most massive star forms in the most massive core with 30% efficiency.
- domain assumption Optimal sampling of the IMF (Kroupa et al. 2013; Yan et al. 2017) underlies Eq. 8.
- domain assumption Adopted dust opacity, emissivity index, and gas-to-dust ratio (kappa=0.01 cm^2/g, beta=1.8, R=100).
Cite this review
Pith. "Pith review of Why is the Star Formation Rate Proportional to Dense Gas Mass?." pith.science (2026). https://pith.science/paper/KCRWA4FL
@misc{pith2026250507764,
author = {Pith},
title = {Pith review of: Why is the Star Formation Rate Proportional to Dense Gas Mass?},
year = {2026},
howpublished = {\url{https://pith.science/paper/KCRWA4FL}},
note = {Machine review of arXiv:2505.07764}
}
abstract
One of the most profound empirical laws of star formation is the Gao-Solomon relation, a linear correlation between the star formation rate (SFR) and the dense molecular gas mass. It is puzzling how the complicated physics in star-formation results in this surprisingly simple proportionality. Using archival Herschel and Atacama Large Millimeter/submillimeter Array Observations, we derived the masses of the most massive cores ($M^{\rm max}_{\rm core}$) and masses of the gravitationally bound gas ($ M_{\rm gas}^{\rm bound}$) in the parent molecular clouds for a sample of low-mass and high-mass star-forming regions. We discovered a significant correlation $\log(M^{\rm max}_{\rm core}/M_{\odot}) = 0.506 \log(M_{\rm gas}^{\rm bound}/M_{\odot})-0.32$. Our discovered $M^{\rm max}_{\rm core}$-$M_{\rm gas}^{\rm bound}$ correlation can be approximately converted to the Gao-Solomon relation if there is (1) a constant 30% efficiency of converting $M^{\rm max}_{\rm core}$ to the mass of the most massive star ($m^{\rm max}_{\rm star}$), and (2) if SFR and $m^{\rm max}_{\rm star}$ are tightly related through $\log({\rm SFR}/(M_{\odot} {\rm yr}^{-1})) = 2.04 \log(m^{\rm max}_{\rm star}/M_{\odot})-5.80$. Intriguingly, both requirements have been suggested by previous theoretical studies (c.f. Yan et al. 2017). Based on this result, we hypothesize that the Gao-Solomon relation is a consequence of combining the following three non-trivial relations (i) SFR vs. $m^{\rm max}_{\rm star}$, (ii) $m^{\rm max}_{\rm star}$ vs. $M^{\rm max}_{\rm core}$, and (iii) $M^{\rm max}_{\rm core}$ vs. $M_{\rm gas}^{\rm bound}$. This finding may open a new possibility to understand the Gao-Solomon relation in an analytic sense.
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Works this paper leans on
-
[1]
Alves, J., Lombardi, M., & Lada, C. J. 2007, A&A, 462, L17
work page 2007
-
[2]
Alves, J., Lombardi, M., & Lada, C. J. 2017, A&A, 606, L2 Andr´ e, P., Men’shchikov, A., Bontemps, S., et al. 2010, A&A, 518, L102
work page 2017
-
[3]
Bergin, E. A. & Tafalla, M. 2007, ARA&A, 45, 339
2007
-
[4]
& Arnouts, S
Bertin, E. & Arnouts, S. 1996, A&AS, 117, 393
1996
-
[5]
Bonfand, M., Csengeri, T., Bontemps, S., et al. 2024, A&A, 687, A163
work page 2024
-
[6]
A., Bate, M
Bonnell, I. A., Bate, M. R., & Vine, S. G. 2003, MNRAS, 343, 413
2003
-
[7]
H.-H., Burkhart, B., Goodman, A., & Collins, D
Chen, H. H.-H., Burkhart, B., Goodman, A., & Collins, D. C. 2018, ApJ, 859, 162
2018
-
[8]
Cheng, Y., Lu, X., Sanhueza, P., et al. 2024, arXiv e-prints, arXiv:2404.03858
work page Pith review arXiv 2024
Show all 75 references
-
[9]
& Povich, M
Chomiuk, L. & Povich, M. S. 2011, AJ, 142, 197
2011
-
[10]
R., & Newman, M
Clauset, A., Shalizi, C. R., & Newman, M. E. J. 2009, SIAM Review, 51, 661
2009
-
[11]
2025, arXiv e-prints, arXiv:2503.05663
Coletta, A., Molinari, S., Schisano, E., et al. 2025, arXiv e-prints, arXiv:2503.05663
2025 arXiv
-
[12]
S., et al
Contreras, Y., Schuller, F., Urquhart, J. S., et al. 2013, A&A, 549, A45
2013
-
[13]
Dame, T. M. & Lada, C. J. 2023, ApJ, 944, 197
2023
-
[14]
2017, MNRAS, 471, 100
Elia, D., Molinari, S., Schisano, E., et al. 2017, MNRAS, 471, 100
2017
-
[15]
2020, ApJ, 894, 103
Evans, Neal J., I., Kim, K.-T., Wu, J., et al. 2020, ApJ, 894, 103
2020
-
[16]
M., Jørgensen, J
Evans, Neal J., I., Dunham, M. M., Jørgensen, J. K., et al. 2009, ApJS, 181, 321
2009
-
[17]
Evans, II, N. J. 1999, ARA&A, 37, 311
1999
-
[18]
J., Heiderman, A., & Vutisalchavakul, N
Evans, II, N. J., Heiderman, A., & Vutisalchavakul, N. 2014, ApJ, 782, 114
2014
-
[19]
W., Lang, D., & Goodman, J
Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306 Galv´ an-Madrid, R., D´ ıaz-Gonz´ alez, D. J., Motte, F., et al. 2024, arXiv e-prints, arXiv:2407.07359
2013 arXiv
-
[20]
& Solomon, P
Gao, Y. & Solomon, P. M. 2004, ApJ, 606, 271
2004
-
[21]
Ginsburg, A., Goddi, C., Kruijssen, J. M. D., et al. 2017, ApJ, 842, 92
2017
-
[22]
2022, A&A, 662, A9
Ginsburg, A., Csengeri, T., Galv´ an-Madrid, R., et al. 2022, A&A, 662, A9
2022
-
[23]
R., Lique, F., & Santa-Maria, M
Goicoechea, J. R., Lique, F., & Santa-Maria, M. G. 2022, A&A, 658, A28
2022
-
[24]
F., Heyer, M., Narayanan, G., et al
Goldsmith, P. F., Heyer, M., Narayanan, G., et al. 2008, ApJ, 680, 428 18
2008
-
[25]
Goldsmith, P. F. & Kauffmann, J. 2017, ApJ, 841, 25
2017
-
[26]
Griffin, M. J. 2010, in Astronomy and Astrophysics, Vol. 518
2010
-
[27]
E., Huard, T., & Heyer, M
Heiderman, A., Evans, Neal J., I., Allen, L. E., Huard, T., & Heyer, M. 2010, ApJ, 723, 1019
2010
-
[28]
2022b, Research in Astronomy and Astrophysics, 22, 075016 Jim´ enez-Serra, I., B´ aez-Rubio, A., Rivilla, V
Jiao, S., Wu, J., Ruan, H., et al. 2022b, Research in Astronomy and Astrophysics, 22, 075016 Jim´ enez-Serra, I., B´ aez-Rubio, A., Rivilla, V. M., et al. 2013, ApJL, 764, L4
2013
-
[29]
H., Clark, P
Jones, G. H., Clark, P. C., Glover, S. C. O., & Hacar, A. 2023, MNRAS, 520, 1005
2023
-
[30]
& Tan, J
Kainulainen, J. & Tan, J. C. 2013, A&A, 549, A53
2013
-
[31]
L., Evans, N
Kauffmann, J., Bertoldi, F., Bourke, T. L., Evans, N. J., I., & Lee, C. W. 2008, A&A, 487, 993
2008
-
[32]
2017, A&A, 603, A89
Kauffmann, J., Pillai, T., Zhang, Q., et al. 2017, A&A, 603, A89
2017
-
[33]
1998, ApJ, 498, 541
Kennicutt, Robert C., J. 1998, ApJ, 498, 541
1998
-
[34]
2006, MNRAS, 365, 590
Koen, C. 2006, MNRAS, 365, 590
2006
-
[35]
2019, ApJ, 873, 31 K¨ onyves, V., Andr´ e, P., Men’shchikov, A., et al
Kong, S. 2019, ApJ, 873, 31 K¨ onyves, V., Andr´ e, P., Men’shchikov, A., et al. 2015, A&A, 584, A91
2019
-
[36]
2001, MNRAS, 322, 231
Kroupa, P. 2001, MNRAS, 322, 231
2001
-
[37]
& Weidner, C
Kroupa, P. & Weidner, C. 2003, ApJ, 598, 1076
2003
-
[38]
2013, in Planets, Stars and Stellar Systems
Kroupa, P., Weidner, C., Pflamm-Altenburg, J., et al. 2013, in Planets, Stars and Stellar Systems. Volume 5: Galactic Structure and Stellar Populations, ed. T. D. Oswalt & G. Gilmore, Vol. 5, 115
2013
-
[39]
J., Forbrich, J., Lombardi, M., & Alves, J
Lada, C. J., Forbrich, J., Lombardi, M., & Alves, J. F. 2012, ApJ, 745, 190
2012
-
[40]
2019, A&A, 631, A72
Lin, Y., Csengeri, T., Wyrowski, F., et al. 2019, A&A, 631, A72
2019
-
[41]
B., Li, D., et al
Lin, Y., Liu, H. B., Li, D., et al. 2016, ApJ, 828, 32
2016
-
[42]
Liu, H. B. 2019, ApJL, 877, L22
2019
-
[43]
C., Cheng, Y., & Kong, S
Liu, M., Tan, J. C., Cheng, Y., & Kong, S. 2018, ApJ, 862, 105
2018
-
[44]
Lombardi, M., Alves, J., & Lada, C. J. 2015, A&A, 576, L1
2015
-
[45]
2024, A&A, 690, A33
Louvet, F., Sanhueza, P., Stutz, A., et al. 2024, A&A, 690, A33
2024
-
[46]
1989, A&A, 215, L13
Martin-Pintado, J., Bachiller, R., Thum, C., & Walmsley, M. 1989, A&A, 215, L13
1989
-
[47]
M., Reid, M
Menten, K. M., Reid, M. J., Forbrich, J., & Brunthaler, A. 2007, A&A, 474, 515
2007
-
[48]
2010, PASP, 122, 314
Molinari, S., Swinyard, B., Bally, J., et al. 2010, PASP, 122, 314
2010
-
[49]
2016, A&A, 591, A149
Molinari, S., Schisano, E., Elia, D., et al. 2016, A&A, 591, A149
2016
-
[50]
2025, arXiv e-prints, arXiv:2503.05555
Molinari, S., Schilke, P., Battersby, C., et al. 2025, arXiv e-prints, arXiv:2503.05555
2025 arXiv
-
[51]
1998, A&A, 336, 150
Motte, F., Andre, P., & Neri, R. 1998, A&A, 336, 150
1998
-
[52]
2022, A&A, 662, A8
Motte, F., Bontemps, S., Csengeri, T., et al. 2022, A&A, 662, A8
2022
-
[53]
Offner, S. S. R., Clark, P. C., Hennebelle, P., et al. 2014, in Protostars and Planets VI, ed. H. Beuther, R. S. Klessen, C. P. Dullemond, & T. Henning, 53
2014
-
[54]
& Henning, T
Ossenkopf, V. & Henning, T. 1994, in Astronomy and
1994
-
[55]
V., Orkisz, J
Pety, J., Guzm´ an, V. V., Orkisz, J. H., et al. 2017, A&A, 599, A98
2017
-
[56]
2010, in Astronomy and Astrophysics, Vol
Poglitsch, A. 2010, in Astronomy and Astrophysics, Vol. 518, 2
2010
-
[57]
K., Ginsburg, A., et al
Rosolowsky, E., Dunham, M. K., Ginsburg, A., et al. 2010, ApJS, 188, 123
2010
-
[58]
Sanders, D. B. & Mirabel, I. F. 1996, ARA&A, 34, 749
1996
-
[59]
M., Zhang, Q., et al
Sanhueza, P., Jackson, J. M., Zhang, Q., et al. 2017, ApJ, 841, 97
2017
-
[60]
G., Goicoechea, J
Santa-Maria, M. G., Goicoechea, J. R., Pety, J., et al. 2023, A&A, 679, A4
2023
- [61]
-
[62]
2015, MNRAS, 453, L41
Schneider, N., Bontemps, S., Girichidis, P., et al. 2015, MNRAS, 453, L41
2015
-
[63]
2015, A&A, 582, A93 Su´ arez, G., Galv´ an-Madrid, R., Aguilar, L., et al
Schulz, C., Pflamm-Altenburg, J., & Kroupa, P. 2015, A&A, 582, A93 Su´ arez, G., Galv´ an-Madrid, R., Aguilar, L., et al. 2021, ApJ, 921, 48
2015
-
[64]
J., & Andre, P
Terebey, S., Chandler, C. J., & Andre, P. 1993, ApJ, 414, 759 V´ azquez-Semadeni, E., Gonz´ alez-Samaniego, A., & Col´ ın, P. 2017, MNRAS, 467, 1313
1993
- [65]
-
[66]
& Kroupa, P
Weidner, C. & Kroupa, P. 2004, MNRAS, 348, 187
2004
-
[67]
2013, MNRAS, 434, 84
Weidner, C., Kroupa, P., & Pflamm-Altenburg, J. 2013, MNRAS, 434, 84
2013
-
[68]
P., Blitz, L., & McKee, C
Williams, J. P., Blitz, L., & McKee, C. F. 2000, in Protostars and Planets IV, ed. V. Mannings, A. P. Boss, & S. S. Russell, 97
2000
-
[69]
Wong, Y. H. V., Hirashita, H., & Li, Z.-Y. 2016, PASJ, 68, 67
2016
-
[70]
2005, ApJL, 635, L173
Wu, J., Evans, Neal J., I., Gao, Y., et al. 2005, ApJL, 635, L173
2005
-
[71]
L., & Knez, C
Wu, J., Evans, Neal J., I., Shirley, Y. L., & Knez, C. 2010, ApJS, 188, 313 Decoding SFR versus Mdense relation 19
2010
-
[72]
2023, MNRAS, 520, 3259
Xu, F.-W., Wang, K., Liu, T., et al. 2023, MNRAS, 520, 3259
2023
-
[73]
2017, A&A, 607, A126
Yan, Z., Jerabkova, T., & Kroupa, P. 2017, A&A, 607, A126
2017
-
[74]
2023, A&A, 670, A151
Yan, Z., Jerabkova, T., & Kroupa, P. 2023, A&A, 670, A151
2023
-
[75]
Y., Wang, J
Zhu, F. Y., Wang, J. Z., Zhu, Q. F., & Zhang, J. S. 2022, A&A, 665, A94
2022
Reviewed August 15, 2026 · model on record in the stance chip above.
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