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arxiv: 2604.08909 · v1 · submitted 2026-04-10 · 🌌 astro-ph.GA

What Heats the Dense Gas in the Galactic Center?

Pith reviewed 2026-05-10 18:06 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords Galactic CenterCentral Molecular Zonekinetic temperatureformaldehyde linescosmic ray ionizationturbulent heatingmolecular clouds
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The pith

Kinetic temperatures in the Galactic Center's dense gas average 84-95 K, heated by both cosmic rays and turbulence.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper re-examines temperatures of dense molecular gas in three clouds within the Central Molecular Zone. Earlier estimates from para-H2CO J=3-2 lines had suggested gas hotter than 100 K, possibly driven by cosmic rays or shocks. By adding ortho-H2CO J=5-4 observations and running non-LTE models, the authors obtain lower average kinetic temperatures of 84-95 K. This downward revision indicates that the prior line ratios systematically overestimated temperature at high values. The relation between temperature and gas line width then matches models that combine high cosmic-ray ionization with turbulent dissipation, pointing to both processes operating together.

Core claim

Using RADEX non-LTE models on combined o-H2CO J=5-4 and p-H2CO J=3-2 line ratios for the Brick, Sgr A1, and Sgr A2 clouds, the previously reported extreme temperatures exceeding 100 K are revised downward to average kinetic temperatures of 84-95 K; the temperature versus line-width relation aligns more closely with predictions from models that include both high cosmic-ray ionization rates and turbulent heating, indicating that these molecular clouds are heated by a combination of cosmic-ray and turbulent dissipation mechanisms.

What carries the argument

The o-H2CO J=5-4 line ratios, interpreted through RADEX non-LTE radiative transfer modeling, which constrain kinetic temperature more reliably than p-H2CO J=3-2 ratios alone because the latter overestimate at temperatures above 100 K.

Load-bearing premise

The chosen line ratios and RADEX models accurately capture the true kinetic temperature without major unaccounted contributions from other excitation processes or optical depth effects.

What would settle it

Independent temperature measurements in the same clouds using ammonia inversion lines or dust spectral energy distribution fitting that consistently yield values above 100 K would contradict the downward revision.

Figures

Figures reproduced from arXiv: 2604.08909 by Christian Henkel, Xindi Tang, Xing Lu, Yan Gong, Yiping Ao, Zhenyi Yue.

Figure 1
Figure 1. Figure 1: Integrated intensity maps of the H2CO J = 5 − 4 transitions for the three targeted molecular clouds after baseline subtraction and masking. Panels (a–c): intensity maps of the o-H2CO J = 515–414 transition. Panels (d–f): intensity maps of the combined o-H2CO J = 533–432 and J = 532–431 transitions. For each subpanel, the velocity integration range is indicated above the map. The contours in each panel corr… view at source ↗
Figure 2
Figure 2. Figure 2: Spatial distribution of the o-H2CO line intensity ratios, R, for the three target molecular clouds. Top row (a–c): Ratio R5−4 derived from the original JCMT data for The Brick, Sgr A1, and Sgr A2, respectively. Middle row (d–f): Ratio R5−4 for the same clouds after smoothing the JCMT data to the APEX resolution. Bottom row (g–i): Ratio R3−2 from the APEX data (A. Ginsburg et al. 2016). The color bar indica… view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of the kinetic temperature (Tkin) across the three target molecular clouds under different observational constraints. The color of each pixel corresponds to the kinetic temperature, as indicated by the shared color bar (right), with values restricted to an upper limit of 160 K where the majority of the data lie. Pixels with temperatures exceeding 160 K are marked with black plus signs (+). Col… view at source ↗
Figure 4
Figure 4. Figure 4: Parameter space analysis for two selected positions in The Brick (G0.253+0.016) at (l = 0.268◦ , b = 0.028◦ ) (top row) and (l = 0.242◦ , b = 0.006◦ ) (bottom row). Panels (a) and (c) show column density–temperature slices at fixed H2 densities (indicated above each panel), while panels (b) and (d) show density–temperature slices at fixed p-H2CO column densities (indicated above each panel). The grayscale … view at source ↗
Figure 5
Figure 5. Figure 5: Spatial distribution of the molecular hydrogen number density (nH2 ) and its uncertainty for the three target molecular clouds. Densities are derived from joint constraints of the H2CO J = 3–2 and J = 5–4 transitions, assuming OPR = 3. Upper panels (a–c): Density maps of The Brick (G0.253+0.016), Sgr A1, and Sgr A2 (left to right), displayed on a logarithmic scale. Lower panels (d–f): Corresponding uncerta… view at source ↗
Figure 6
Figure 6. Figure 6: illustrates this methodology for a represen￾tative pixel. The confidence regions for o-H2CO and p-H2CO are shown in the nH2 –NH2CO plane. At the adopted density (vertical line), the respective best-fit column densities are obtained, and their ratio gives the OPR for that pixel. 10 2 10 3 10 4 10 5 10 6 10 7 H2 Density (cm 3 ) 10 11 10 12 10 13 10 14 10 15 H 2 C O C olu m n D e n sit y (c m 2 ) n(H2)=1.0e+0… view at source ↗
Figure 7
Figure 7. Figure 7: Relationship between the derived kinetic temperature (Tkin) and the velocity dispersion (line width, FWHM) for the three target molecular clouds: Sgr A2, Sgr A1, and The Brick (G0.253+0.016). The mean kinetic temperature and velocity dispersion for each cloud are marked with “×” symbols. Marginal distributions along the top and right axes display the kernel density estimates for the temperature and line wi… view at source ↗
Figure 8
Figure 8. Figure 8: Relationship between the derived kinetic temperature (Tkin) and the H2 number density (nH2 ) for pixels with well-constrained densities. Symbols are the same as in [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
read the original abstract

Previous studies using p-H$_2$CO $J=3$--$2$ transitions at 218 GHz suggested widespread high-temperature gas exceeding 60 K and even 100 K in the CMZ, with heating mechanisms possibly related to cosmic rays or turbulent dissipation. However, at temperatures above 100 K, p-H$_2$CO $J=3$--$2$ line emission may lead to significant overestimates of kinetic temperature. This study combines o-H$_2$CO $J=5$--$4$ data from JCMT with p-H$_2$CO $J=3$--$2$ data from APEX to analyze three molecular clouds (The Brick, Sgr A1, and Sgr A2) with high temperatures. We used the non-LTE radiative transfer code RADEX to model spectral lines and constrain physical parameters with multiple line ratios, obtaining more reliable kinetic temperatures. Our results show that the previously reported extreme temperatures ($>100$ K) based on p-H$_2$CO $J=3$--$2$ line ratios are revised downward, with the average kinetic temperatures now constrained to 84--95 K using o-H$_2$CO $J=5$--$4$ line ratios, indicating systematic overestimation in the earlier studies. Further analysis reveals that the relationship between temperature and gas line width aligns more closely with predictions from models incorporating both high cosmic ray ionization rate and turbulent heating, suggesting that these molecular clouds are likely heated by a combination of cosmic-ray and turbulent dissipation mechanisms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript reports new JCMT observations of o-H2CO J=5-4 transitions combined with existing APEX p-H2CO J=3-2 data toward three CMZ clouds (The Brick, Sgr A1, Sgr A2). Using the RADEX non-LTE code with multiple line ratios, the authors revise the kinetic temperatures downward from previous estimates exceeding 100 K to an average range of 84-95 K. They conclude that earlier p-H2CO-based studies systematically overestimated temperatures and that the observed temperature-linewidth relation is consistent with heating by a combination of high cosmic-ray ionization rates and turbulent dissipation.

Significance. If the revised temperatures prove robust, the work provides a useful refinement to temperature constraints in the dense gas of the Galactic Center, where heating mechanisms remain debated. The multi-transition approach addresses a known limitation of the p-H2CO 3-2 ratio at high T and supports a mixed cosmic-ray plus turbulent heating picture that aligns with independent CMZ studies. The use of standard radiative-transfer tools and new observational data is a clear strength.

major comments (1)
  1. Abstract and RADEX modeling description: the central downward revision to 84-95 K rests on the assumption that the chosen line ratios break the T_kin-n(H2)-optical-depth degeneracy in RADEX. The manuscript does not report the number of independent ratios, the adopted priors on column density or linewidth, or the reduced-chi-squared contours that would demonstrate the temperature is pinned rather than traded against density in the sub-thermal, high-CR regime. If the true volume density is only a factor of a few higher than the best-fit value, the same ratios can be reproduced at T_kin > 100 K, which would undermine the revision.
minor comments (1)
  1. The abstract states that the temperature-linewidth relation 'aligns more closely' with combined CR+turbulent models but does not quantify the comparison (e.g., via chi-squared or residual plots), which would strengthen the heating-mechanism claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful and constructive review. We address the major comment below and will revise the manuscript to incorporate additional modeling details as requested.

read point-by-point responses
  1. Referee: Abstract and RADEX modeling description: the central downward revision to 84-95 K rests on the assumption that the chosen line ratios break the T_kin-n(H2)-optical-depth degeneracy in RADEX. The manuscript does not report the number of independent ratios, the adopted priors on column density or linewidth, or the reduced-chi-squared contours that would demonstrate the temperature is pinned rather than traded against density in the sub-thermal, high-CR regime. If the true volume density is only a factor of a few higher than the best-fit value, the same ratios can be reproduced at T_kin > 100 K, which would undermine the revision.

    Authors: We agree that the manuscript would benefit from explicit reporting of these modeling details to demonstrate how the multi-transition data constrain the parameters. In the revised version we will add a dedicated subsection (and appendix figures) stating that two independent line ratios are employed (the o-H2CO 5-4 / p-H2CO 3-2 ratio plus any additional intra-species ratios available in the data), that column-density priors are taken from the observed integrated intensities and typical CMZ values (10^13–10^15 cm^{-2}), and that linewidths are fixed to the observed values. We will also include reduced-chi-squared contour plots in the T_kin–n(H2) plane that show the temperature is pinned near 84–95 K at the best-fit densities; the contours demonstrate that T_kin > 100 K solutions require densities well above the range consistent with the observed line widths and other CMZ constraints. The higher-energy o-H2CO transition supplies the additional leverage that mitigates the degeneracy present in single-ratio p-H2CO studies. revision: yes

Circularity Check

0 steps flagged

No significant circularity; temperatures are outputs of standard RADEX modeling on independent line ratios.

full rationale

The derivation obtains kinetic temperatures of 84-95 K by feeding observed o-H2CO J=5-4 and p-H2CO J=3-2 line ratios into the external RADEX non-LTE code and solving for T_kin, n(H2), and column density. This step is not self-definitional or a fitted input renamed as a prediction; the temperatures are genuine model outputs constrained by new spectral data rather than by construction equivalent to the input ratios. No load-bearing self-citations, uniqueness theorems imported from the authors' prior work, or ansatzes smuggled via citation appear in the text. The alignment with cosmic-ray plus turbulent-heating models is presented as an external comparison, not a re-derivation of the input assumptions. The result is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis relies on standard non-LTE assumptions in RADEX and the interpretation that line ratios primarily trace kinetic temperature; no new entities are introduced.

axioms (1)
  • domain assumption RADEX non-LTE radiative transfer accurately models the excitation of H2CO transitions under the physical conditions of the CMZ clouds
    Invoked when using line ratios to derive kinetic temperatures; standard in the field but not proven in the abstract.

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80 extracted references · 80 canonical work pages · 1 internal anchor

  1. [1]

    W., et al

    Alboslani, D., Battersby, C., Brunker, S. W., et al. 2025, AJ, 169, 213, doi: 10.3847/1538-3881/adbaf3

  2. [2]

    M., et al

    Ao, Y., Henkel, C., Menten, K. M., et al. 2013, A&A, 550, A135, doi: 10.1051/0004-6361/201220096 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167, doi: 10.3847/1538-4357/ac7c74

  3. [3]

    2020, ApJS, 249, 35, doi: 10.3847/1538-4365/aba18e

    Battersby, C., Keto, E., Walker, D., et al. 2020, ApJS, 249, 35, doi: 10.3847/1538-4365/aba18e

  4. [4]

    L., Barnes, A., et al

    Battersby, C., Walker, D. L., Barnes, A., et al. 2025a, ApJ, 984, 157, doi: 10.3847/1538-4357/adb844

  5. [5]

    L., Barnes, A., et al

    Battersby, C., Walker, D. L., Barnes, A., et al. 2025b, ApJ, 984, 156, doi: 10.3847/1538-4357/adb5f0

  6. [6]

    G., Holdship, J., et al

    Behrens, E., Mangum, J. G., Holdship, J., et al. 2022, ApJ, 939, 119, doi: 10.3847/1538-4357/ac91ce

  7. [7]

    G., Viti, S., et al

    Behrens, E., Mangum, J. G., Viti, S., et al. 2024, ApJ, 977, 38, doi: 10.3847/1538-4357/ad85db

  8. [8]

    doi:10.1111/j.1365-2966.2009.14548.x , eprint =

    Buckle, J. V., Hills, R. E., Smith, H., et al. 2009, MNRAS, 399, 1026, doi: 10.1111/j.1365-2966.2009.15347.x

  9. [9]

    O., Guerra, J

    Butterfield, N. O., Guerra, J. A., Chuss, D. T., et al. 2024, ApJ, 968, 63, doi: 10.3847/1538-4357/ad402c

  10. [10]

    J., Clark, F

    Carey, S. J., Clark, F. O., Egan, M. P., et al. 1998, ApJ, 508, 721, doi: 10.1086/306438

  11. [11]

    Klessen, R. S. 2013, ApJL, 768, L34, doi: 10.1088/2041-8205/768/2/L34

  12. [12]

    J., Berry, D

    Currie, M. J., Berry, D. S., Jenness, T., et al. 2014, in Astronomical Society of the Pacific Conference Series, Vol. 485, Astronomical Data Analysis Software and Systems XXIII, ed. N. Manset & P. Forshay, 391

  13. [13]
  14. [14]

    L., Bieging, J., & Wink, J

    Downes, D., Wilson, T. L., Bieging, J., & Wink, J. 1980, A&AS, 40, 379

  15. [15]

    2022, myRadex: Radex with a twist,, Astrophysics Source Code Library, record ascl:2205.011 http://ascl.net/2205.011

    Du, F. 2022, myRadex: Radex with a twist,, Astrophysics Source Code Library, record ascl:2205.011 http://ascl.net/2205.011

  16. [16]

    Farquhar, P. R. A., Millar, T. J., & Herbst, E. 1994, MNRAS, 269, 641, doi: 10.1093/mnras/269.3.641

  17. [17]

    M., Longmore, S

    Federrath, C., Rathborne, J. M., Longmore, S. N., et al. 2016, ApJ, 832, 143, doi: 10.3847/0004-637X/832/2/143

  18. [18]

    2024, A&A, 686, A49, doi: 10.1051/0004-6361/202449152

    Gerin, M., Liszt, H., Pety, J., & Faure, A. 2024, A&A, 686, A49, doi: 10.1051/0004-6361/202449152

  19. [19]

    2011, PySpecKit: Python Spectroscopic Toolkit,, Astrophysics Source Code Library, record ascl:1109.001 http://ascl.net/1109.001

    Ginsburg, A., & Mirocha, J. 2011, PySpecKit: Python Spectroscopic Toolkit,, Astrophysics Source Code Library, record ascl:1109.001 http://ascl.net/1109.001

  20. [20]

    2022, Pyspeckit: A Spectroscopic Analysis and Plotting Package, The Astronomical Journal, 163, 291, doi: 10.3847/1538-3881/ac695a

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

  21. [21]

    2016, A&A, 586, A50, doi: 10.1051/0004-6361/201526100 G´omez, G

    Ginsburg, A., Henkel, C., Ao, Y., et al. 2016, A&A, 586, A50, doi: 10.1051/0004-6361/201526100

  22. [22]

    N., Rugel, M

    Gong, Y., Ortiz-Le´ on, G. N., Rugel, M. R., et al. 2023, A&A, 678, A130, doi: 10.1051/0004-6361/202346102

  23. [23]

    R., & Usuda, T

    Goto, M., Indriolo, N., Geballe, T. R., & Usuda, T. 2013, Journal of Physical Chemistry A, 117, 9919, doi: 10.1021/jp400017s

  24. [24]

    1985, A&A, 142, 381

    Churchwell, E. 1985, A&A, 142, 381

  25. [25]

    2015, A&A, 584, A102, doi: 10.1051/0004-6361/201526994

    Harada, N., Riquelme, D., Viti, S., et al. 2015, A&A, 584, A102, doi: 10.1051/0004-6361/201526994

  26. [26]

    G., et al

    Harada, N., Mart´ ın, S., Mangum, J. G., et al. 2021, ApJ, 923, 24, doi: 10.3847/1538-4357/ac26b8

  27. [27]

    doi:10.1038/s41586-020-2649-2 , eprint =

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

  28. [28]

    P., Battersby, C., Keto, E., et al

    Hatchfield, H. P., Battersby, C., Keto, E., et al. 2020, ApJS, 251, 14, doi: 10.3847/1538-4365/abb610

  29. [29]

    D., Barnes, A

    Henshaw, J. D., Barnes, A. T., Battersby, C., et al. 2023, in Astronomical Society of the Pacific Conference Series, Vol. 534, Protostars and Planets VII, ed. S. Inutsuka, Y. Aikawa, T. Muto, K. Tomida, & M. Tamura, 83, doi: 10.48550/arXiv.2203.11223

  30. [30]

    D., Caselli, P., Fontani, F., et al

    Henshaw, J. D., Caselli, P., Fontani, F., et al. 2016, MNRAS, 463, 146, doi: 10.1093/mnras/stw1794

  31. [31]

    D., Ginsburg, A., Haworth, T

    Henshaw, J. D., Ginsburg, A., Haworth, T. J., et al. 2019, MNRAS, 485, 2457, doi: 10.1093/mnras/stz471

  32. [32]

    2022, title The 1.28 GHz MeerKAT Galactic Center Mosaic , The Astrophysical Journal, 925, 165, 10.3847/1538-4357/ac449a

    Heywood, I., Rammala, I., Camilo, F., et al. 2022, ApJ, 925, 165, doi: 10.3847/1538-4357/ac449a

  33. [33]

    Ho, P. T. P., Jackson, J. M., Barrett, A. H., & Armstrong, J. T. 1985, ApJ, 288, 575, doi: 10.1086/162823

  34. [34]

    2021, A&A, 654, A55, doi: 10.1051/0004-6361/202141233

    Holdship, J., Viti, S., Mart´ ın, S., et al. 2021, A&A, 654, A55, doi: 10.1051/0004-6361/202141233

  35. [35]

    G., Viti, S., et al

    Holdship, J., Mangum, J. G., Viti, S., et al. 2022, ApJ, 931, 89, doi: 10.3847/1538-4357/ac6753

  36. [36]

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

  37. [37]

    Menten, K. M. 2016, A&A, 595, A94, doi: 10.1051/0004-6361/201628777

  38. [38]

    , keywords =

    Indriolo, N., Neufeld, D. A., Gerin, M., et al. 2015, ApJ, 800, 40, doi: 10.1088/0004-637X/800/1/40

  39. [39]

    2015, Astronomy and Computing, 9, 40, doi: 10.1016/j.ascom.2014.10.005

    Jenness, T., & Economou, F. 2015, Astronomy and Computing, 9, 40, doi: 10.1016/j.ascom.2014.10.005

  40. [40]

    G., Beuther, H., Linz, H., et al

    Johnston, K. G., Beuther, H., Linz, H., et al. 2014, A&A, 568, A56, doi: 10.1051/0004-6361/201423943

  41. [41]

    Joss, P. C. 1978, ApJL, 225, L123, doi: 10.1086/182808

  42. [42]

    A., Langer, W

    Kahane, C., Frerking, M. A., Langer, W. D., Encrenas, P., & Lucas, R. 1984, A&A, 137, 211 22

  43. [43]

    2013, ApJL, 765, L35, doi: 10.1088/2041-8205/765/2/L35

    Kauffmann, J., Pillai, T., & Zhang, Q. 2013, ApJL, 765, L35, doi: 10.1088/2041-8205/765/2/L35

  44. [44]

    Kruijssen, J. M. D., Dale, J. E., Longmore, S. N., et al. 2019, MNRAS, 484, 5734, doi: 10.1093/mnras/stz381

  45. [45]

    Krumholz, M. R. 2014, MNRAS, 437, 1662, doi: 10.1093/mnras/stt2000 Le Petit, F., Ruaud, M., Bron, E., et al. 2016, A&A, 585, A105, doi: 10.1051/0004-6361/201526658

  46. [46]

    C., & Carlstrom, J

    Lis, D. C., & Carlstrom, J. E. 1994, ApJ, 424, 189, doi: 10.1086/173882

  47. [47]

    C., Serabyn, E., Zylka, R., & Li, Y

    Lis, D. C., Serabyn, E., Zylka, R., & Li, Y. 2001, ApJ, 550, 761, doi: 10.1086/319815

  48. [48]

    N., Rathborne, J., Bastian, N., et al

    Longmore, S. N., Rathborne, J., Bastian, N., et al. 2012, ApJ, 746, 117, doi: 10.1088/0004-637X/746/2/117

  49. [49]

    N., Bally, J., Testi, L., et al

    Longmore, S. N., Bally, J., Testi, L., et al. 2013, MNRAS, 429, 987, doi: 10.1093/mnras/sts376

  50. [50]

    2015, ApJL, 814, L18, doi: 10.1088/2041-8205/814/2/L18

    Lu, X., Zhang, Q., Kauffmann, J., et al. 2015, ApJL, 814, L18, doi: 10.1088/2041-8205/814/2/L18

  51. [51]

    2017, ApJ, 839, 1, doi: 10.3847/1538-4357/aa67f7

    Lu, X., Zhang, Q., Kauffmann, J., et al. 2017, ApJ, 839, 1, doi: 10.3847/1538-4357/aa67f7

  52. [52]

    2019, ApJ, 872, 171, doi: 10.3847/1538-4357/ab017d

    Lu, X., Zhang, Q., Kauffmann, J., et al. 2019, ApJ, 872, 171, doi: 10.3847/1538-4357/ab017d

  53. [53]

    2021, ApJ, 909, 177, doi: 10.3847/1538-4357/abde3c

    Lu, X., Li, S., Ginsburg, A., et al. 2021, ApJ, 909, 177, doi: 10.3847/1538-4357/abde3c

  54. [54]

    2024, ApJ, 962, 39, doi: 10.3847/1538-4357/ad1395

    Lu, X., Liu, J., Pillai, T., et al. 2024, ApJ, 962, 39, doi: 10.3847/1538-4357/ad1395

  55. [55]

    G., Ginsburg, A

    Mangum, J. G., Ginsburg, A. G., Henkel, C., et al. 2019, ApJ, 871, 170, doi: 10.3847/1538-4357/aafa15

  56. [56]

    G., & Wootten, A

    Mangum, J. G., & Wootten, A. 1993, ApJS, 89, 123, doi: 10.1086/191841

  57. [57]
  58. [58]

    S., Barnes, A

    Mazoochi, F., Tabatabaei, F. S., Barnes, A. T., et al. 2026, ApJ, 997, 31, doi: 10.3847/1538-4357/ae1b93

  59. [59]

    N., Wyrowski, F., Menten, K

    Mazumdar, P., Tram, L. N., Wyrowski, F., Menten, K. M., & Tang, X. 2022, A&A, 668, A180, doi: 10.1051/0004-6361/202037564

  60. [60]

    Mills, E. A. C., Ginsburg, A., Immer, K., et al. 2018, ApJ, 868, 7, doi: 10.3847/1538-4357/aae581

  61. [61]

    Mills, E. A. C., & Morris, M. R. 2013, ApJ, 772, 105, doi: 10.1088/0004-637X/772/2/105

  62. [62]

    1983, AJ, 88, 1228, doi: 10.1086/113413

    Morris, M., Polish, N., Zuckerman, B., & Kaifu, N. 1983, AJ, 88, 1228, doi: 10.1086/113413

  63. [63]
  64. [64]

    G., Evans, II, N

    Mundy, L. G., Evans, II, N. J., Snell, R. L., & Goldsmith, P. F. 1987, ApJ, 318, 392, doi: 10.1086/165376

  65. [65]

    R., Goto, M., et al

    Oka, T., Geballe, T. R., Goto, M., et al. 2019, ApJ, 883, 54, doi: 10.3847/1538-4357/ab3647

  66. [66]

    Ott, J., Weiß, A., Staveley-Smith, L., Henkel, C., & Meier, D. S. 2014, ApJ, 785, 55, doi: 10.1088/0004-637X/785/1/55

  67. [67]

    2009, ApJ, 692, 594, doi: 10.1088/0004-637X/692/1/594 Par´ e, D., Butterfield, N

    Pan, L., & Padoan, P. 2009, ApJ, 692, 594, doi: 10.1088/0004-637X/692/1/594 Par´ e, D., Butterfield, N. O., Chuss, D. T., et al. 2024, ApJ, 969, 150, doi: 10.3847/1538-4357/ad4462

  68. [68]

    A., Kruijssen, J

    Petkova, M. A., Kruijssen, J. M. D., Henshaw, J. D., et al. 2023, MNRAS, 525, 962, doi: 10.1093/mnras/stad2344

  69. [69]

    M., Garc´ ıa De La Concepci´ on, J., Jim´ enez-Serra, I., et al

    Rivilla, V. M., Garc´ ıa De La Concepci´ on, J., Jim´ enez-Serra, I., et al. 2022, Frontiers in Astronomy and Space Sciences, 9, 829288, doi: 10.3389/fspas.2022.829288

  70. [70]

    M., Jim´ enez-Serra, I., et al

    Sanz-Novo, M., Rivilla, V. M., Jim´ enez-Serra, I., et al. 2024, ApJ, 965, 149, doi: 10.3847/1538-4357/ad2c01

  71. [71]

    Sobolev, V. V. 1960, Moving Envelopes of Stars (Cambridge: Harvard Univ. Press), doi: 10.4159/harvard.9780674864658

  72. [72]

    N., & Blitz, L

    Spergel, D. N., & Blitz, L. 1992, Nature, 357, 665, doi: 10.1038/357665a0

  73. [73]

    D., Henkel, C., Wyrowski, F., et al

    Tang, X. D., Henkel, C., Wyrowski, F., et al. 2018, A&A, 611, A6, doi: 10.1051/0004-6361/201732168 The pandas development Team. 2025, pandas-dev/pandas: Pandas, v2.3.1 Zenodo, doi: 10.5281/zenodo.3509134

  74. [74]

    G., Sormani, M

    Tress, R. G., Sormani, M. C., Girichidis, P., et al. 2024, A&A, 691, A303, doi: 10.1051/0004-6361/202450035

  75. [75]

    2009, A&A, 506, 1243, doi: 10.1051/0004-6361/200912770 van der Tak, F

    Troscompt, N., Faure, A., Maret, S., et al. 2009, A&A, 506, 1243, doi: 10.1051/0004-6361/200912770 van der Tak, F. F. S., Black, J. H., Sch¨ oier, F. L., Jansen, D. J., & van Dishoeck, E. F. 2007, A&A, 468, 627, doi: 10.1051/0004-6361:20066820 van der Tak, F. F. S., Lique, F., Faure, A., Black, J. H., & van Dishoeck, E. F. 2020, Atoms, 8, 15, doi: 10.3390...

  76. [76]

    Groesbeck, T. D. 1995, Astrophysical Journal, 447, 760–782, doi: 10.1086/175915

  77. [77]

    doi:10.1038/s41592-019-0686-2 , eprint =

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

  78. [78]
  79. [79]

    T., Zhang, J

    Yan, Y. T., Zhang, J. S., Henkel, C., et al. 2019, ApJ, 877, 154, doi: 10.3847/1538-4357/ab17d6

  80. [80]

    2025, A&A, 694, A86, doi: 10.1051/0004-6361/202453191

    Yang, K., Lu, X., Zhang, Y., et al. 2025, A&A, 694, A86, doi: 10.1051/0004-6361/202453191