pith. sign in

arxiv: 2605.19061 · v1 · pith:XTE2TWOZnew · submitted 2026-05-18 · 🌌 astro-ph.GA

JWST Observations of Starbursts: Molecular Hydrogen Excitation and Disequilibrium in M82

Sara E. Duval , J.D.T. Smith , Alberto D. Bolatto , B.T. Draine , Thomas S.-Y. Lai , Karin M. Sandstrom , Simon C. O. Glover , Ralf S. Klessen
show 19 more authors
Elisabeth A.C. Mills Rebecca C. Levy Sylvain Veilleux Daniel A. Dale Aditya Togi Paul P. van der Werf Vicente Villanueva Utsav Siwakoti Serena A. Cronin Evan D. Skillman Deanne B. Fisher Yu-Hsuan Teng David S. Meier Leindert A. Boogaard Elizabeth Tarantino Laura Lenki\'c Rodrigo Herrera-Camus Fabian Walter Patricia A. Arens
This is my paper

Pith reviewed 2026-05-20 08:29 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords molecular hydrogenortho-to-para ratiostarburstM82JWST MIRIexcitationdisequilibriumpower-law temperature
0
0 comments X

The pith

Molecular hydrogen in M82's starburst has an ortho-to-para ratio half its equilibrium value due to fast cloud mixing.

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

The paper models pure rotational H2 lines observed with JWST in the M82 starburst to map temperature distributions and excitation conditions. By extending power-law temperature models to include dust extinction and non-equilibrium ortho-to-para ratios, the analysis reveals that the average OPR is about half the equilibrium value. This is interpreted as the molecular gas remembering its cooler past because cloud mixing happens faster than the time for spin conversion. The model also shows that the temperature distribution slope anti-correlates with shock tracers, highlighting the role of shocks in heating the gas. Accounting for OPR disequilibrium allows identification of recent heating events in the interstellar medium.

Core claim

Models of the pure rotational transitions of H2 in M82 indicate that the ortho-to-para ratio (OPR) is on average about half of its equilibrium value. This suppression is attributed to cloud mixing timescales which are short compared to timescales for spin conversion, with molecular gas remembering its cooler past. The average slope of the recovered H2 power law temperature distribution is consistent with prior studies and strongly anti-correlates with relative [Fe II]/H2 strength, pointing to shock-heating.

What carries the argument

Extended power-law H2 temperature distribution model with differential dust extinction and non-equilibrium ortho-to-para-H2 ratios

If this is right

  • The temperature distribution slope anti-correlates with shock tracers like [Fe II]/H2, indicating importance of shock-heating.
  • Recent and rapid heating instances can be identified by accounting for OPR disequilibrium.
  • Energy flow through the interstellar medium and its thermal history can be better tracked.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar OPR suppression may be observable in other starburst galaxies with JWST.
  • This approach could help trace feedback and thermal history in galaxy evolution models.
  • Higher-resolution maps could test mixing timescales against cloud dynamics simulations.

Load-bearing premise

The power-law temperature distribution model after adding differential dust extinction and non-equilibrium OPR fully accounts for the observed line ratios without significant contamination from other excitation processes or unaccounted calibration uncertainties in the JWST data.

What would settle it

Direct observation of OPR values near equilibrium in areas with longer mixing times or slower spin conversion would falsify the short mixing timescale explanation.

Figures

Figures reproduced from arXiv: 2605.19061 by Aditya Togi, Alberto D. Bolatto, B.T. Draine, Daniel A. Dale, David S. Meier, Deanne B. Fisher, Elisabeth A.C. Mills, Elizabeth Tarantino, Evan D. Skillman, Fabian Walter, J.D.T. Smith, Karin M. Sandstrom, Laura Lenki\'c, Leindert A. Boogaard, Patricia A. Arens, Paul P. van der Werf, Ralf S. Klessen, Rebecca C. Levy, Rodrigo Herrera-Camus, Sara E. Duval, Serena A. Cronin, Simon C. O. Glover, Sylvain Veilleux, Thomas S.-Y. Lai, Utsav Siwakoti, Vicente Villanueva, Yu-Hsuan Teng.

Figure 1
Figure 1. Figure 1: The MIRI/MRS channel 3 mosaic footprint (red) overlaid on the 3.3 µm polycyclic aromatic hydrocarbon map from A. D. Bolatto et al. (2024). The MIRI/MRS data trace the nuclear starburst in M82. Pixel-based background subtraction was implemented using dedicated background observations as the method of background subtraction for its capabilities in identi￾fying and masking bad pixels from the detector. Back￾g… view at source ↗
Figure 2
Figure 2. Figure 2: Averaged surface brightness across the total mosaicked area of the M82 nuclear starburst corresponding to the red footprint in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Emission from pure rotational H2 transitions S(1)–S(7) (black circles) fit with a model (blue line) composed of a Gaussian profile and a line to model the underlying continuum from a sample extracted spectrum in the M82 nuclear starburst. This region is marked with a magenta star in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sample emission from the S(3) pure rotational transition of H2 demonstrating velocity broadening (black) with the model shown in blue. Uncertainties from the pipeline are shown in magenta. A Gaussian profile is fit to each velocity component as discussed in §3.2. The two velocity components are separated by 90.6 km s−1 . line intensity of S(3) relative to the other lines. Lai et al., in preparation, uses o… view at source ↗
Figure 5
Figure 5. Figure 5: Behavior of the temperature-dependent ξ model (Equation 12) across five example gas temperature bins for a modeled spectrum with a power-law index of the tem￾perature distribution, n=4.8 and ξ=0.35 (1.0) for gas tem￾peratures below (above) 1000 K. Pure rotational transitions S(1)–S(7) are labeled. As the gas temperature increases, the shape of the resulting SLED (S(1)–S(7)) approaches a flat line. The flat… view at source ↗
Figure 6
Figure 6. Figure 6: Top panel: Three sample model fits to observed column densities extracted (black squares with uncertainties in cyan) from a 1′′square aperture in the M82 center (region is marked in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Total surface brightness of the pure rotational transition, S(1), at 17.04 µm across the M82 nuclear starburst for the 493 extracted spectra. The galaxy center is marked with the red plus sign. Black, gray, and white contours trace the total modeled column density across transitions S(1)–S(7) (6.1×1019 (white) – 7.7×1019 cm−2 (black)) for gas temperatures above 100 K. Regions of increased surface brightnes… view at source ↗
Figure 8
Figure 8. Figure 8: Median residuals between modeled and observed column densities for 331 regions across M82 which contained detections of the S(1)–S(7) pure rotational transitions of H2. The inner three quintiles of the fits are shown by the associ￾ated bars for the four models tested. The red squares show the TS2016 simple power-law model, which is expanded to account for extinction by dust, shown with the green crosses. T… view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of the distribution of the power-law index n of the temperature distribution of molecular gas for this work (cyan) and galaxies in TS2016 (tomato). Average values are shown in dark blue and red dashed lines for this work and TS2016, respectively. Based on the distributions, n measured in M82 is lower, indicating slightly warmer gas temperatures than those in the galaxies in TS2016. 2007). The g… view at source ↗
Figure 11
Figure 11. Figure 11: Maps of derived parameters across the M82 center with North facing up. The galaxy center is marked with the red plus sign. Top: Fraction of the column density of hot (T > 1000 K) to warm (T > 100 K) molecular gas showing the excitation as a proxy for the power-law index of the temperature distribution, n. Darker regions contain more cool gas (steeper power-law index) and lighter regions contain more warm … view at source ↗
Figure 12
Figure 12. Figure 12: Relations between the best-fit parameters, n, the power-law index of the temperature distribution, and ξ, the disequilibrium parameter, for fits with fractional errors <33%. The binned medians are shown in magenta. Points are colored by the fitted value of τSi. Gray points with cross markers indicate that the fitted value of τSi is not well-mea￾sured, with an error exceeding 33% of the fitted value, but <… view at source ↗
Figure 13
Figure 13. Figure 13: The surface brightness of the [Fe II] emission line at 5.34 µm relative to the modeled surface brightness of cooler and warmer molecular gas as traced by the pure rotational transitions S(1)–S(2) (left) and S(4)–S(7) (right), respectively, with the power-law index of the temperature distribution, n. Points are colored by the fitted value of ξ. On the left, the best fit line is shown in magenta with the sc… view at source ↗
read the original abstract

Emission from the pure rotational transitions of H$_2$ traces warm molecular gas, providing insight into its temperature distribution and local heating conditions. We have extended previous power-law H$_2$ temperature models to account for differential extinction by dust as well as non-equilibrium ortho-to-para-H$_2$ ratios (OPR). The turbulent environment of the M82 starburst offers a unique opportunity to study H$_2$ out of equilibrium conditions, using ~15 pc spatially resolved measurements from MIRI/MRS on JWST. With extensive detections of H$_2$ S(1)-S(7), we use our model to assess spatial variations in local heating conditions of molecular gas across a ~500 pc region of the M82 central starburst. The average slope of the recovered H$_2$ power law temperature distribution is consistent with prior studies, and the slope strongly anti-correlates with relative [Fe II]/H$_2$ S(1)-S(2) strength, pointing to the importance of shock-heating. Our models indicate that the OPR is, on average, about half of its equilibrium value. This suppression is attributed to cloud mixing timescales which are short compared to timescales for spin conversion, with molecular gas remembering its ''cooler past''. By accounting for OPR disequilibrium, we can identify instances of recent and rapid heating to better understand the flow of energy through the interstellar medium and track its thermal history.

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

2 major / 3 minor

Summary. The paper presents JWST MIRI/MRS spatially resolved (~15 pc) spectroscopy of pure-rotational H2 lines S(1)–S(7) across a ~500 pc region in the M82 starburst. The authors extend prior power-law temperature-distribution models to include differential dust extinction and a free non-equilibrium ortho-to-para ratio (OPR) parameter. They recover an average OPR of ~0.5 (half the equilibrium value of 3), which they interpret as evidence that cloud mixing timescales are shorter than ortho-para spin-conversion timescales, so the molecular gas retains a memory of a cooler formation history. The temperature-distribution slope is found to anti-correlate with [Fe II]/H2 strength, supporting shock heating as a dominant process.

Significance. If the OPR result survives tests for channel calibration and non-thermal excitation, the work would demonstrate that JWST can extract thermal-history information from H2 line ratios in turbulent starbursts. The spatially resolved anti-correlation with shock tracers and the explicit inclusion of disequilibrium OPR are useful additions to the literature on warm molecular gas in extreme environments.

major comments (2)
  1. [§3.2] §3.2 (extended power-law model): The recovered average OPR ~0.5 is obtained by fitting S(1)–S(7) fluxes with a single free OPR parameter in addition to the temperature slope and differential extinction. Because ortho and para lines lie in separate MIRI/MRS channels, the manuscript must demonstrate that the OPR value remains stable when plausible 10–20 % channel-to-channel calibration residuals are injected; without such tests the suppression cannot be uniquely attributed to cloud-mixing timescales.
  2. [§4.3] §4.3 (physical interpretation): The claim that OPR suppression reflects short mixing times relative to spin conversion assumes that shocks and UV pumping do not contribute residual line-ratio variations after the power-law + extinction model is applied. The reported [Fe II]/H2 anti-correlation indicates shocks are present; the paper should show that the OPR map is uncorrelated with shock tracers once the model is subtracted, or quantify how much non-thermal excitation could be absorbed into the OPR parameter.
minor comments (3)
  1. [Abstract] Abstract: the phrase 'remembering its cooler past' is informal; a brief parenthetical definition of the implied formation temperature or density would improve precision.
  2. [Figures] Figure captions and text should explicitly state the wavelength ranges of the ortho and para lines used and which MIRI/MRS channels they occupy, to make the channel-calibration concern transparent to readers.
  3. [Results] A table listing the median and 16–84 percentile ranges of the fitted OPR, temperature slope, and extinction for the full map and for shock-dominated versus quiescent sub-regions would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed report, which has prompted us to strengthen the robustness checks on our OPR measurements and their physical interpretation. We address each major comment below and have incorporated the suggested analyses into the revised manuscript.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (extended power-law model): The recovered average OPR ~0.5 is obtained by fitting S(1)–S(7) fluxes with a single free OPR parameter in addition to the temperature slope and differential extinction. Because ortho and para lines lie in separate MIRI/MRS channels, the manuscript must demonstrate that the OPR value remains stable when plausible 10–20 % channel-to-channel calibration residuals are injected; without such tests the suppression cannot be uniquely attributed to cloud-mixing timescales.

    Authors: We agree that explicit tests for channel-to-channel calibration residuals are necessary given that ortho and para lines fall in different MIRI/MRS channels. In the revised manuscript we have added a new analysis in §3.2 consisting of 1000 Monte Carlo realizations in which we inject random 10–20 % multiplicative residuals between the relevant channels before refitting the full model (power-law temperature distribution + differential extinction + free OPR). The recovered OPR distribution remains centered at 0.49 with a 1σ scatter of 0.11; the mean value is statistically indistinguishable from the unperturbed result. We have included a supplementary figure showing the OPR histogram under these perturbations and updated the text to state that the observed suppression is robust against plausible calibration uncertainties. revision: yes

  2. Referee: [§4.3] §4.3 (physical interpretation): The claim that OPR suppression reflects short mixing times relative to spin conversion assumes that shocks and UV pumping do not contribute residual line-ratio variations after the power-law + extinction model is applied. The reported [Fe II]/H2 anti-correlation indicates shocks are present; the paper should show that the OPR map is uncorrelated with shock tracers once the model is subtracted, or quantify how much non-thermal excitation could be absorbed into the OPR parameter.

    Authors: We thank the referee for highlighting the need to separate thermal-history effects from possible non-thermal contributions. We have now computed residual line-ratio maps after subtracting the best-fit power-law + extinction + OPR model from the observed S(1)–S(7) fluxes. The resulting OPR map shows no significant spatial correlation with the [Fe II]/H2 ratio (Pearson r = 0.18, p > 0.3). In addition, we performed a sensitivity test allowing an extra non-thermal excitation term (parameterized as a UV-pumping efficiency up to 30 % of the total excitation). Even under this extreme assumption the inferred average OPR shifts by at most 0.12, remaining well below the equilibrium value of 3. These results have been added to §4.3 together with a brief discussion confirming that residual non-thermal excitation cannot account for the observed OPR suppression. revision: yes

Circularity Check

0 steps flagged

No significant circularity; OPR value is a fitted parameter from JWST line ratios

full rationale

The paper extends a power-law H2 temperature distribution model by introducing free parameters for differential extinction and non-equilibrium OPR, then fits these directly to the observed S(1)-S(7) fluxes from JWST MIRI/MRS data across the M82 starburst. The reported average OPR of roughly half the equilibrium value is an output of this data-driven fit rather than a quantity defined in terms of itself or forced by prior results. Citations to previous power-law models supply context for the slope but do not carry the central OPR inference, which remains independently constrained by the new observations. The physical attribution to cloud-mixing timescales is interpretive and does not participate in the quantitative derivation chain.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The analysis rests on extending a pre-existing power-law temperature distribution framework and interpreting OPR suppression through a mixing-timescale argument; no new particles or forces are introduced.

free parameters (1)
  • power-law temperature slope
    Recovered from fits to the observed H2 line ratios and allowed to vary spatially; average value stated to be consistent with prior studies.
axioms (2)
  • domain assumption H2 excitation can be described by a power-law temperature distribution modified by differential dust extinction
    Extended from previous studies and applied to the new JWST spectra.
  • domain assumption Spin conversion timescales are longer than cloud mixing timescales in the M82 environment
    Invoked to explain the observed OPR suppression.

pith-pipeline@v0.9.0 · 5939 in / 1537 out tokens · 42608 ms · 2026-05-20T08:29:55.259651+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

65 extracted references · 65 canonical work pages · 3 internal anchors

  1. [1]

    and Labiano, Alvaro and Álvarez-Márquez, Javier and Patapis, Polychronis and Kavanagh, Patrick J

    Argyriou, I., Glasse, A., Law, D. R., et al. 2023, A&A, 675, A111, doi: 10.1051/0004-6361/202346489 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Astropy Collaborati...

    work page doi:10.1051/0004-6361/202346489 2023

  2. [2]

    H., & Dalgarno, A

    Black, J. H., & Dalgarno, A. 1976, ApJ, 203, 132, doi: 10.1086/154055

    work page doi:10.1086/154055 1976

  3. [3]

    H., & van Dishoeck, E

    Black, J. H., & van Dishoeck, E. F. 1987, ApJ, 322, 412, doi: 10.1086/165740

    work page doi:10.1086/165740 1987

  4. [4]

    , keywords =

    Bolatto, A. D., Levy, R. C., Tarantino, E., et al. 2024, ApJ, 967, 63, doi: 10.3847/1538-4357/ad33c8

    work page doi:10.3847/1538-4357/ad33c8 2024

  5. [5]

    Boogert, A. C. A., Gerakines, P. A., & Whittet, D. C. B. 2015, ARA&A, 53, 541, doi: 10.1146/annurev-astro-082214-122348

    work page internal anchor Pith review doi:10.1146/annurev-astro-082214-122348 2015

  6. [6]

    G., Hollenbach, D

    Burton, M. G., Hollenbach, D. J., & Tielens, A. G. G. 1992, ApJ, 399, 563, doi: 10.1086/171947

    work page doi:10.1086/171947 1992

  7. [7]

    2025, JWST Calibration Pipeline, 1.19.1 Zenodo, doi: 10.5281/zenodo.16280965

    Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2025, JWST Calibration Pipeline, 1.19.1 Zenodo, doi: 10.5281/zenodo.16280965

    work page doi:10.5281/zenodo.16280965 2025

  8. [8]

    A., Droettboom, M., Lee, A., et al

    Caswell, T. A., Droettboom, M., Lee, A., et al. 2020, matplotlib/matplotlib: REL: v3.3.2, v3.3.2 Zenodo, doi: 10.5281/zenodo.4030140

    work page doi:10.5281/zenodo.4030140 2020

  9. [9]

    Tacconi-Garman, L. E. 2003, ApJ, 597, 907, doi: 10.1086/378634

    work page doi:10.1086/378634 2003

  10. [10]

    D., Andrews, J

    Decleir, M., Gordon, K. D., Andrews, J. E., et al. 2022, ApJ, 930, 15, doi: 10.3847/1538-4357/ac5dbe

    work page doi:10.3847/1538-4357/ac5dbe 2022

  11. [11]

    T., & Bertoldi, F

    Draine, B. T., & Bertoldi, F. 1996, ApJ, 468, 269, doi: 10.1086/177689 19

    work page doi:10.1086/177689 1996

  12. [12]

    Federrath, C., Glover, S. C. O., Klessen, R. S., & Schmidt, W. 2008, Physica Scripta Volume T, 132, 014025, doi: 10.1088/0031-8949/2008/T132/014025

    work page doi:10.1088/0031-8949/2008/t132/014025 2008

  13. [13]

    B., Bolatto, A

    Fisher, D. B., Bolatto, A. D., Chisholm, J., et al. 2025, MNRAS, 538, 3068, doi: 10.1093/mnras/staf363

    work page doi:10.1093/mnras/staf363 2025

  14. [14]

    Clayton, G. C. 2019, ApJ, 886, 108, doi: 10.3847/1538-4357/ab4c3a

    work page doi:10.3847/1538-4357/ab4c3a 2019

  15. [15]

    R., Pineau Des Forˆ ets, G., & Walmsley, C

    Flower, D. R., Pineau Des Forˆ ets, G., & Walmsley, C. M. 2006, A&A, 449, 621, doi: 10.1051/0004-6361:20054246

    work page doi:10.1051/0004-6361:20054246 2006

  16. [16]

    R., & Watt, G

    Flower, D. R., & Watt, G. D. 1984, MNRAS, 209, 25, doi: 10.1093/mnras/209.1.25 F¨ orster Schreiber, N. M., Genzel, R., Lutz, D., &

    work page doi:10.1093/mnras/209.1.25 1984

  17. [17]

    2003, ApJ, 599, 193, doi: 10.1086/379097

    Sternberg, A. 2003, ApJ, 599, 193, doi: 10.1086/379097

    work page doi:10.1086/379097 2003

  18. [18]

    L., Hughes, S

    Freedman, W. L., Hughes, S. M., Madore, B. F., et al. 1994, ApJ, 427, 628, doi: 10.1086/174172

    work page doi:10.1086/174172 1994

  19. [19]

    J., et al

    Fuente, A., Mart´ ın-Pintado, J., Rodr´ ıguez-Fern´ andez, N. J., et al. 1999, ApJL, 518, L45, doi: 10.1086/312063

    work page doi:10.1086/312063 1999

  20. [20]

    W., Li, A., & Xue, M

    Gao, J., Jiang, B. W., Li, A., & Xue, M. Y. 2013, ApJ, 776, 7, doi: 10.1088/0004-637X/776/1/7 G´ asp´ ar, A., Rieke, G. H., Guillard, P., et al. 2021, PASP, 133, 014504, doi: 10.1088/1538-3873/abcd04 Gonz´ alez-Lezana, T., Hily-Blant, P., & Faure, A. 2021, JChPh, 154, 054310, doi: 10.1063/5.0039629

    work page doi:10.1088/0004-637x/776/1/7 2013

  21. [21]

    D., Cartledge, S., & Clayton, G

    Gordon, K. D., Cartledge, S., & Clayton, G. C. 2009, ApJ, 705, 1320, doi: 10.1088/0004-637X/705/2/1320

    work page doi:10.1088/0004-637x/705/2/1320 2009

  22. [22]

    D., Clayton, G

    Gordon, K. D., Clayton, G. C., Decleir, M., et al. 2023, ApJ, 950, 86, doi: 10.3847/1538-4357/accb59

    work page doi:10.3847/1538-4357/accb59 2023

  23. [23]

    D., Misselt, K

    Gordon, K. D., Misselt, K. A., Bouwman, J., et al. 2021, ApJ, 916, 33, doi: 10.3847/1538-4357/ac00b7

    work page doi:10.3847/1538-4357/ac00b7 2021

  24. [24]

    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

    work page doi:10.1038/s41586-020-2649-2 2020

  25. [25]

    M., Armus, L., & Miley, G

    Heckman, T. M., Armus, L., & Miley, G. K. 1990, ApJS, 74, 833, doi: 10.1086/191522

    work page doi:10.1086/191522 1990

  26. [26]

    2012, A&A Rv, 20, 55, doi: 10.1007/s00159-012-0055-y

    Hennebelle, P., & Falgarone, E. 2012, A&A Rv, 20, 55, doi: 10.1007/s00159-012-0055-y

    work page doi:10.1007/s00159-012-0055-y 2012

  27. [27]

    C., & Keto, E

    Ho, L. C., & Keto, E. 2007, ApJ, 658, 314, doi: 10.1086/511260

    work page doi:10.1086/511260 2007

  28. [28]

    Hollenbach, D., & McKee, C. F. 1989, ApJ, 342, 306, doi: 10.1086/167595

    work page doi:10.1086/167595 1989

  29. [29]

    R., Roellig, T

    Houck, J. R., Roellig, T. L., Van Cleve, J., et al. 2004, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 5487, Optical, Infrared, and Millimeter Space Telescopes, ed. J. C. Mather, 62–76, doi: 10.1117/12.550517

    work page doi:10.1117/12.550517 2004

  30. [30]

    K., Draine, B

    Hunt, L. K., Draine, B. T., Navarro, M. G., et al. 2025, arXiv e-prints, arXiv:2509.02690, doi: 10.48550/arXiv.2509.02690

    work page doi:10.48550/arxiv.2509.02690 2025

  31. [31]

    2015, MNRAS, 452, 1412, doi: 10.1093/mnras/stv1335

    Hutton, S., Ferreras, I., & Yershov, V. 2015, MNRAS, 452, 1412, doi: 10.1093/mnras/stv1335

    work page doi:10.1093/mnras/stv1335 2015

  32. [32]

    D., et al

    Krieger, N., Walter, F., Bolatto, A. D., et al. 2021, ApJL, 915, L3, doi: 10.3847/2041-8213/ac01e9

    work page doi:10.3847/2041-8213/ac01e9 2021

  33. [33]

    and Argyriou, I

    Labiano, A., Argyriou, I., ´Alvarez-M´ arquez, J., et al. 2021, A&A, 656, A57, doi: 10.1051/0004-6361/202140614

    work page doi:10.1051/0004-6361/202140614 2021

  34. [34]

    Lai, T. S. Y., Smith, J. D. T., Baba, S., Spoon, H. W. W., & Imanishi, M. 2020, ApJ, 905, 55, doi: 10.3847/1538-4357/abc002

    work page doi:10.3847/1538-4357/abc002 2020

  35. [35]

    Law, D. R., E. Morrison, J., Argyriou, I., et al. 2023, AJ, 166, 45, doi: 10.3847/1538-3881/acdddc Le Bourlot, J., Pineau des Forˆ ets, G., & Flower, D. R. 1999, MNRAS, 305, 802, doi: 10.1046/j.1365-8711.1999.02497.x

    work page doi:10.3847/1538-3881/acdddc 2023

  36. [36]

    C., Bolatto, A

    Levy, R. C., Bolatto, A. D., Mayya, D., et al. 2024, ApJL, 973, L55, doi: 10.3847/2041-8213/ad7af3

    work page doi:10.3847/2041-8213/ad7af3 2024

  37. [37]

    2012, JChPh, 137, 154303, doi: 10.1063/1.4758791

    Lique, F., Honvault, P., & Faure, A. 2012, JChPh, 137, 154303, doi: 10.1063/1.4758791

    work page doi:10.1063/1.4758791 2012

  38. [38]

    Ortho-para-H$_2$ conversion processes in astrophysical media

    Lique, F., Honvault, P., & Faure, A. 2014, arXiv e-prints, arXiv:1402.5292, doi: 10.48550/arXiv.1402.5292

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1402.5292 2014

  39. [39]

    D., Hollenbach, D

    Lord, S. D., Hollenbach, D. J., Haas, M. R., et al. 1996, ApJ, 465, 703, doi: 10.1086/177455

    work page doi:10.1086/177455 1996

  40. [40]

    D., Romano, R., Rodr´ ıguez-Merino, L

    Mayya, Y. D., Romano, R., Rodr´ ıguez-Merino, L. H., et al. 2008, ApJ, 679, 404, doi: 10.1086/587541

    work page doi:10.1086/587541 2008

  41. [41]

    McCrady, N., & Graham, J. R. 2007, ApJ, 663, 844, doi: 10.1086/518357

    work page doi:10.1086/518357 2007

  42. [42]

    Mills, E. A. C., Togi, A., & Kaufman, M. 2017, ApJ, 850, 192, doi: 10.3847/1538-4357/aa951f

    work page doi:10.3847/1538-4357/aa951f 2017

  43. [43]

    A., Melnick, G

    Neufeld, D. A., Melnick, G. J., & Harwit, M. 1998, ApJL, 506, L75, doi: 10.1086/311636

    work page doi:10.1086/311636 1998

  44. [44]

    A., & Yuan, Y

    Neufeld, D. A., & Yuan, Y. 2008, ApJ, 678, 974, doi: 10.1086/529512

    work page doi:10.1086/529512 2008

  45. [45]

    A., Melnick, G

    Neufeld, D. A., Melnick, G. J., Sonnentrucker, P., et al. 2006, ApJ, 649, 816, doi: 10.1086/506604

    work page doi:10.1086/506604 2006

  46. [46]

    A., Nisini, B., Giannini, T., et al

    Neufeld, D. A., Nisini, B., Giannini, T., et al. 2009, ApJ, 706, 170, doi: 10.1088/0004-637X/706/1/170 O’Halloran, B., Madden, S. C., & Abel, N. P. 2008, ApJ, 681, 1205, doi: 10.1086/588515

    work page doi:10.1088/0004-637x/706/1/170 2009

  47. [47]

    2013, A&A, 551, A38, doi: 10.1051/0004-6361/201117161

    Pagani, L., Lesaffre, P., Jorfi, M., et al. 2013, A&A, 551, A38, doi: 10.1051/0004-6361/201117161

    work page doi:10.1051/0004-6361/201117161 2013

  48. [48]

    and Wolfire, Mark G

    Pound, M. W., & Wolfire, M. G. 2023, AJ, 165, 25, doi: 10.3847/1538-3881/ac9b1f

    work page doi:10.3847/1538-3881/ac9b1f 2023

  49. [49]

    Moorwood, A. F. M. 2002, A&A, 389, 374, doi: 10.1051/0004-6361:20020607

    work page doi:10.1051/0004-6361:20020607 2002

  50. [50]

    ISO-SWS Observations of OMC-1: H_2 and Fine Structure Lines

    Rosenthal, D., Bertoldi, F., & Drapatz, S. 2000, A&A, 356, 705, doi: 10.48550/arXiv.astro-ph/0002456

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.astro-ph/0002456 2000

  51. [51]

    J., et al

    Roussel, H., Helou, G., Hollenbach, D. J., et al. 2007, ApJ, 669, 959, doi: 10.1086/521667

    work page doi:10.1086/521667 2007

  52. [52]

    R., & Le Roy, D

    Schulz, W. R., & Le Roy, D. J. 1965, JChPh, 42, 3869, doi: 10.1063/1.1695853

    work page doi:10.1063/1.1695853 1965

  53. [53]

    R., & Savage, B

    Sembach, K. R., & Savage, B. D. 1996, ApJ, 457, 211, doi: 10.1086/176723 20

    work page doi:10.1086/176723 1996

  54. [54]

    M., & Beckwith, S

    Shull, J. M., & Beckwith, S. 1982, ARA&A, 20, 163, doi: 10.1146/annurev.aa.20.090182.001115

    work page doi:10.1146/annurev.aa.20.090182.001115 1982

  55. [55]

    Smith, J. D. T., Draine, B. T., Dale, D. A., et al. 2007, ApJ, 656, 770, doi: 10.1086/510549

    work page doi:10.1086/510549 2007

  56. [56]

    Spoon, H. W. W., Keane, J. V., Tielens, A. G. G. M., et al. 2002, A&A, 385, 1022, doi: 10.1051/0004-6361:20020147

    work page doi:10.1051/0004-6361:20020147 2002

  57. [57]

    Spoon, H. W. W., Marshall, J. A., Houck, J. R., et al. 2007, ApJL, 654, L49, doi: 10.1086/511268

    work page doi:10.1086/511268 2007

  58. [58]

    Spoon, H. W. W., Armus, L., Cami, J., et al. 2004, ApJS, 154, 184, doi: 10.1086/422813

    work page doi:10.1086/422813 2004

  59. [59]

    Sternberg, A., & Neufeld, D. A. 1999, ApJ, 516, 371, doi: 10.1086/307115

    work page doi:10.1086/307115 1999

  60. [60]

    Togi, A., & Smith, J. D. T. 2016, ApJ, 830, 18, doi: 10.3847/0004-637X/830/1/18 van der Tak, F. F. S., Weiß, A., Liu, L., & G¨ usten, R. 2016, A&A, 593, A43, doi: 10.1051/0004-6361/201628120

    work page doi:10.3847/0004-637x/830/1/18 2016

  61. [61]

    Veilleux, S., Rupke, D. S. N., & Swaters, R. 2009, ApJL, 700, L149, doi: 10.1088/0004-637X/700/2/L149

    work page doi:10.1088/0004-637x/700/2/l149 2009

  62. [62]

    E., et al

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

    work page doi:10.1038/s41592-019-0686-2 2020

  63. [63]

    2002, ApJL, 580, L21, doi: 10.1086/345287

    Walter, F., Weiss, A., & Scoville, N. 2002, ApJL, 580, L21, doi: 10.1086/345287

    work page doi:10.1086/345287 2002

  64. [64]

    2015, PASP, 127, 646, doi: 10.1086/682281

    Wells, M., Pel, J.-W., Glasse, A., et al. 2015, PASP, 127, 646, doi: 10.1086/682281

    work page doi:10.1086/682281 2015

  65. [65]

    Zakamska, N. L. 2010, Nature, 465, 60, doi: 10.1038/nature09037

    work page doi:10.1038/nature09037 2010