pith. sign in

arxiv: 2605.27177 · v1 · pith:WD6H3SYInew · submitted 2026-05-26 · 🌌 astro-ph.GA · physics.chem-ph

Digging into the Massive Protostar S255IR NIRS3: A Study of Nitrogen-Bearing Molecules and Their Prebiotic Chemistry

Pith reviewed 2026-06-29 17:27 UTC · model grok-4.3

classification 🌌 astro-ph.GA physics.chem-ph
keywords high-mass protostarnitrogen-bearing moleculesALMA observationschemical modelingS255IR NIRS3gas-grain chemistryprebiotic molecules
0
0 comments X

The pith

Abundances of nitrogen-bearing molecules toward S255IR NIRS3 align closely with three-phase warm-up chemical model predictions.

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

This paper identifies rotational emission lines from several complex nitrogen-bearing molecules including methyl cyanide, ethyl cyanide, vinyl cyanide, cyanamide, and formamide toward the high-mass protostar S255IR NIRS3 using ALMA band 4 data. It also detects vibrationally excited cyanoacetylene. Column densities and excitation temperatures between 175 and 220 K are derived via LTE spectral modelling, indicating the molecules originate in warm inner regions. Fractional abundances relative to H2 are compared to three-phase warm-up chemical models, showing consistency within factors of roughly 0.67 to 1.28 for the detected species. A Pearson correlation analysis reveals strong links among the cyanide family members, and the work outlines their gas-grain formation pathways.

Core claim

The observed abundances of CH₃CN, C₂H₅CN, C₂H₃CN, NH₂CN, NH₂CHO, and HC₃N (ν₇ = 2) relative to H₂ are consistent with model predictions within factors of 1.04, 0.67, 1.28, 0.76, 0.72, and 0.96, respectively. These molecules are detected via rotational emission lines, with excitation temperatures of 175-220 K pointing to origin in regions where T ≥ 100 K, and the cyanide species show chemical linkage through abundance correlations.

What carries the argument

LTE spectral modelling to extract column densities and excitation temperatures, followed by direct comparison of fractional abundances to three-phase warm-up chemical models.

If this is right

  • The r > 0.7 correlation among CH₃CN, C₂H₃CN, and C₂H₅CN indicates these cyanide species share linked formation routes in the gas-grain network.
  • Excitation temperatures of 175-220 K confirm the molecules trace the warm inner envelope rather than colder outer material.
  • Model agreement supports gas-grain chemistry as the dominant production mechanism for these N-bearing species in high-mass protostellar environments.
  • The same modelling and comparison approach can be applied to other massive star-forming regions to test the generality of the chemical networks.

Where Pith is reading between the lines

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

  • If the abundance matches hold across sources, undetected N-bearing species could be predicted from the same models for targeted searches.
  • Extending the correlation analysis to other molecular families might identify additional chemical linkages not examined here.
  • The results suggest similar prebiotic molecule inventories could appear in lower-mass protostars once comparable warm-up conditions are reached.

Load-bearing premise

That local thermodynamic equilibrium (LTE) spectral modelling yields accurate column densities and excitation temperatures without significant optical-depth or non-LTE corrections for the detected lines.

What would settle it

Non-LTE radiative transfer calculations or higher-resolution spectra that revise the derived column densities by more than a factor of two, breaking the reported agreement with model predictions.

Figures

Figures reproduced from arXiv: 2605.27177 by Ariful Hoque, Arijit Manna, Sabyasachi Pal, Sandip Dutta, Sekhar Sinha, Sushanta Kumar Mondal, Tapas Baug.

Figure 1
Figure 1. Figure 1: Dust continuum emission image of NIRS3 at 142.49 GHz (λ = 2.10 mm). The red circle represents the synthesized beam of the continuum emission image, whose size is 2.13′′×1.18′′. The RMS of the continuum emission image is 2.35 mJy. The goal of this study is to conduct a comprehensive inves￾tigation of N-bearing molecules, focusing on their spatial dis￾tributions and prebiotic formation chemistry toward the h… view at source ↗
Figure 2
Figure 2. Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: SED of NIRS3 covering the wavelength range from 3.6 µm to 3443 µm. The black curve represents the best-fit SED obtained from the radiative transfer model of Robitaille (2017). The right panel shows a corner diagram demonstrating the results of our MCMC parameter estimation for the SED model. The 1-D histograms on the diagonal show the marginalized posterior densities of mass, dust temperature, and β. The o… view at source ↗
Figure 4
Figure 4. Figure 4: Modelled images of the column density of H2, dust optical depth, and dust temperature, which were generated with the radiative transfer code RADMC-3D. T b ( K ) 0 1 2 3 4 5 6 Frequency (GHz) 128 128.5 129 129.5 127.61–129.49 GHz CH3CN SO2 SO2 SO2 SO CH3OH T b ( K ) 0 1 2 3 4 Frequency (GHz) 130 130.5 131 129.56–131.44 GHz SO2 CH3OH t-HCOOH SiO T b ( K ) 0 2 4 6 8 10 12 14 16 Frequency (GHz) 140 140.5 141 1… view at source ↗
Figure 5
Figure 5. Figure 5: Millimeter wavelength molecular emission spectra of NIRS3 from ALMA band 4. The resolution of the spectra is 976.56 kHz. For the fitting, the distance to the source was fixed at 1.78 kpc. The details of the radiative transfer fitting procedure ap￾plied to the flux measurements are provided in Sanna et al. (2014) and Mininni et al. (2021). We employed a Markov Chain Monte Carlo (MCMC) algorithm to fit the r… view at source ↗
Figure 6
Figure 6. Figure 6: Emission lines of N-bearing molecules CH3CN, C2H5CN, C2H3CN, NH2CN, NH2CHO, and HC3N (ν7 = 2). The green lines indicate the observed molecular spectra of NIRS3, and the blue lines represent the LTE model spectra of detected N-bearing species. Red spectra are the LTE model spectra of all species, including detected N-bearing molecules. The systemic velocity of the spectra is 5.0 km s−1 [PITH_FULL_IMAGE:fi… view at source ↗
Figure 7
Figure 7. Figure 7: Corner plots showing the covariances of the posterior probability distributions of the column density (log10(N) cm−2 ), excitation temperature (K), and FWHM (km s−1 ) of the detected N-bearing molecules [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Integrated emission maps (moment zero) of non-blended transitions of CH3CN, C2H5CN, C2H3CN, NH2CN, NH2CHO, and HC3N (ν7 = 2) towards NIRS3. The emission maps of detected N-bearing species are overlaid with the 2.29 mm continuum emission map (black contour). The contour levels are at 20%, 40%, 60%, and 80% of the peak flux. The red circles represent the synthesised beams of the emission maps [PITH_FULL_IMA… view at source ↗
Figure 9
Figure 9. Figure 9: Cartoon illustration (not up to scale) of molecular dis￾tribution of detected N-bearing molecules towards NIRS3 based on estimated source sizes and temperatures. The direction of CO (J = 3–2) outflow is obtained from Zinchenko et al. (2020). 3.2.5. Cyanoacetylene (HC3N, ν7 = 2) Cyanoacetylene (HC3N) is the shortest cyanopolyyne (HC2n+1N, where n = 1, 2, 3, 5, 7, ..) molecule, which has so far mainly been f… view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of the abundances of detected N-bearing molecules relative to CH3OH (upper panel) and CH3CN (lower panel) towards NIRS3, with different hot cores and hot corinos. states across a wide spectral range is required to accurately determine its excitation temperature and enhance our under￾standing of its physical conditions. 3.2.6. Study of N-bearing possible glycine precursor molecules After the det… view at source ↗
Figure 11
Figure 11. Figure 11: Pearson correlation coefficient heat maps of the abundances relative to CH3OH (left panel) and excitation temperatures (right panel) of the identified N-bearing molecules. The colours show the level of correlation, which corresponds to the Pearson coefficient (r) shown in each box. ing the making of the integrated emission maps of the de￾tected N-bearing molecules, we used the channel ranges of the spectr… view at source ↗
Figure 12
Figure 12. Figure 12: Proposed chemical network to understand the chemical link between detected N-bearing molecules towards NIRS3. The red and green lines show the grain surface and gas-phase pathways. CRP represents the photon produced as a consequence of cosmic ray ionization. of C2H5CN and C2H3CN, respectively, on the grain surfaces of hot cores (Garrod 2013; Garrod et al. 2017; Suzuki et al. 2018; Bonfand et al. 2019; Gar… view at source ↗
read the original abstract

The study of complex nitrogen (N)-bearing molecules is essential for probing the physical and chemical evolution of star-forming regions. In this paper, we present the identification of rotational emission lines from several complex N-bearing species such as methyl cyanide (CH$_{3}$CN), ethyl cyanide (C$_{2}$H$_{5}$CN), vinyl cyanide (C$_{2}$H$_{3}$CN), cyanamide (NH$_{2}$CN), and formamide (NH$_{2}$CHO) toward the high-mass protostar S255IR NIRS3 using ALMA band 4 observations. In addition, the vibrationally excited transitions of cyanoacetylene (HC$_{3}$N, $\nu_{7}$ = 2) were detected. The column densities and excitation temperatures of these molecules were derived through LTE spectral modelling, yielding excitation temperatures in the range of 175$-$220 K. The high excitation temperatures (175$-$220 K) indicate that the identified N-bearing molecules arise from the warm inner regions ($T \geq 100$ K) of the source. The fractional abundances were further estimated relative to H$_{2}$, CH$_{3}$OH, and CH$_{3}$CN. A Pearson correlation heat map of the abundances reveals a strong positive correlation ($r > 0.7$) among three molecules in the cyanide family, such as CH$_{3}$CN, C$_{2}$H$_{3}$CN, and C$_{2}$H$_{5}$CN, suggesting that these N-bearing molecules may be chemically linked. Comparison with three-phase warm-up chemical models shows that the observed abundances of CH$_{3}$CN, C$_{2}$H$_{5}$CN, C$_{2}$H$_{3}$CN, NH$_{2}$CN, NH$_{2}$CHO, and HC$_{3}$N ($\nu_{7}$ = 2) relative to H$_{2}$ are consistent with model predictions within factors of 1.04, 0.67, 1.28, 0.76, 0.72, and 0.96, respectively. Finally, we discuss the potential formation pathways of the identified N-bearing molecules in the context of gas-grain chemistry within S255IR NIRS3.

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 / 2 minor

Summary. The manuscript reports ALMA Band 4 observations toward the high-mass protostar S255IR NIRS3, identifying rotational lines of CH₃CN, C₂H₅CN, C₂H₃CN, NH₂CN, NH₂CHO, and vibrationally excited HC₃N (ν₇=2). LTE spectral modeling yields excitation temperatures of 175–220 K and column densities that are converted to fractional abundances relative to H₂. These abundances are stated to match three-phase warm-up chemical model predictions within factors of 1.04, 0.67, 1.28, 0.76, 0.72, and 0.96, respectively. A Pearson correlation analysis shows strong positive correlations (r > 0.7) among the cyanide-family species, and the paper discusses possible gas-grain formation routes.

Significance. If the column densities are robust, the work supplies new observational constraints on nitrogen chemistry in a high-mass hot core and demonstrates quantitative consistency with published warm-up models for six species. The reported correlation among cyanides and the prebiotic context add value for chemical network validation.

major comments (2)
  1. [LTE spectral modelling section (implied by abstract and modelling description)] The central claim of model agreement (factors 0.67–1.28) rests entirely on column densities obtained from LTE modeling. The manuscript provides no quantitative optical-depth assessment (e.g., τ derived from line ratios, hyperfine intensity ratios, or isotopologue comparisons) for the detected transitions, particularly the stronger CH₃CN K-ladder lines that are known to reach τ ≳ 1 in similar sources at T ≈ 200 K and n(H₂) > 10⁷ cm⁻³. Without such checks, the derived N_col values—and therefore the abundance ratios fed to the model comparison—may be systematically underestimated.
  2. [LTE spectral modelling section] No non-LTE test (RADEX, LVG, or similar) is reported despite the high densities and temperatures involved. If even a subset of the lines used for the six species are moderately optically thick or depart from LTE, the claimed consistency with the three-phase models would no longer be diagnostic at the stated precision.
minor comments (2)
  1. [Abstract and results section] The abstract states that abundances are also given relative to CH₃OH and CH₃CN, but the model comparison is performed only relative to H₂; clarify whether the additional reference abundances are used quantitatively or only for qualitative discussion.
  2. [Results section] The Pearson correlation heat map is mentioned but the exact number of species and the statistical significance of r > 0.7 are not quantified in the provided text; add the correlation matrix or p-values for transparency.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. The concerns regarding the robustness of the LTE-derived column densities are well taken, and we address them point by point below. We will revise the manuscript accordingly.

read point-by-point responses
  1. Referee: The central claim of model agreement (factors 0.67–1.28) rests entirely on column densities obtained from LTE modeling. The manuscript provides no quantitative optical-depth assessment (e.g., τ derived from line ratios, hyperfine intensity ratios, or isotopologue comparisons) for the detected transitions, particularly the stronger CH₃CN K-ladder lines that are known to reach τ ≳ 1 in similar sources at T ≈ 200 K and n(H₂) > 10⁷ cm⁻³. Without such checks, the derived N_col values—and therefore the abundance ratios fed to the model comparison—may be systematically underestimated.

    Authors: We acknowledge that the submitted manuscript did not contain an explicit optical-depth analysis. In the revised version we will add quantitative estimates of τ for the principal transitions of each species, using the derived excitation temperatures and column densities together with available isotopologue lines (where detected) and line-ratio checks for CH₃CN. If any lines prove moderately optically thick we will apply the appropriate corrections before re-comparing with the chemical models. revision: yes

  2. Referee: No non-LTE test (RADEX, LVG, or similar) is reported despite the high densities and temperatures involved. If even a subset of the lines used for the six species are moderately optically thick or depart from LTE, the claimed consistency with the three-phase models would no longer be diagnostic at the stated precision.

    Authors: We agree that an explicit non-LTE verification strengthens the analysis. Although the high densities (>10⁷ cm⁻³) in the hot core support the LTE assumption, we will include RADEX calculations for representative transitions of CH₃CN, C₂H₅CN and NH₂CHO in the revised manuscript to confirm that departures from LTE are negligible at the derived temperatures and densities. revision: yes

Circularity Check

0 steps flagged

No significant circularity; abundances compared to independent external chemical models

full rationale

The derivation proceeds as: (1) LTE spectral modelling of ALMA Band 4 lines yields column densities and Tex (175-220 K) for CH3CN, C2H5CN, etc.; (2) fractional abundances relative to H2 are computed from those columns; (3) the resulting numbers are compared post-hoc to published three-phase warm-up chemical model outputs, producing agreement factors 1.04, 0.67, 1.28, 0.76, 0.72, 0.96. No equation or step reduces the model predictions to the observed columns by construction, no parameters are fitted to force agreement, and the models are external literature results rather than self-citations or ansatzes imported from the authors' prior work. The LTE assumption is an input to step (1) but does not create a definitional loop with the model comparison. The Pearson correlation among cyanide-family abundances is likewise computed directly from the derived columns and is not presented as a prediction. This is a standard observational comparison against external benchmarks; the central claim therefore retains independent content.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

Column densities and excitation temperatures are derived by fitting; LTE is assumed; chemical model comparison uses published networks whose parameters are not re-derived here.

free parameters (2)
  • Excitation temperature
    Fitted per species via LTE modelling to match observed line intensities; range 175-220 K reported.
  • Column density
    Fitted parameter for each molecule in the LTE model.
axioms (1)
  • domain assumption Local thermodynamic equilibrium applies to the emitting gas
    Invoked for spectral modelling of rotational lines.

pith-pipeline@v0.9.1-grok · 6000 in / 1227 out tokens · 37142 ms · 2026-06-29T17:27:09.124795+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

154 extracted references · 112 canonical work pages · 2 internal anchors

  1. [1]

    602C `\.=

    thebibliography [1] 20pt to References -5pt =0pt \@twocolumntrue 12pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =10pt plus 1pt =20pt =10pt 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 ...

  2. [2]

    Adams, F. C. 2010, ARA&A https://doi.org/10.1146/annurev-astro-081309-130830, 48, 47

  3. [3]

    Astropy Collaboration, 2022, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/ac7c74, 935, 167

  4. [4]

    P., Cernicharo, J., Pardo, J

    Ag \'u ndez, M., Fonfr \' a, J. P., Cernicharo, J., Pardo, J. R., & Gu \'e lin, M. 2008, A&A https://doi.org/10.1051/0004-6361:20078956, 479, 493

  5. [5]

    2010, A&A https://doi.org/10.1051/0004-6361/201015186, 517, L2

    Ag \'u ndez, M., Cernicharo, J., Gu \'e lin, M., et al. 2010, A&A https://doi.org/10.1051/0004-6361/201015186, 517, L2

  6. [6]

    2015, A&A https://doi.org/10.1051/0004-6361/201526650, 579, L10

    Ag \'u ndez, M., Cernicharo, J., de Vicente, P., et al. 2015, A&A https://doi.org/10.1051/0004-6361/201526650, 579, L10

  7. [7]

    A., 2019, ARA&A https://doi.org/10.1146/annurev-astro-091918-104409, 57, 113

    Altwegg, K., Balsiger, H., Fuselier, S. A., 2019, ARA&A https://doi.org/10.1146/annurev-astro-091918-104409, 57, 113

  8. [8]

    A new look at the statistical model identification

    Akaike H., 1974, ITAC, 19, 716. doi:10.1109/TAC.1974.1100705

  9. [9]

    T., et al

    Bonfand, M., Belloche, A., Garrod, R. T., et al. 2019, A&A https://doi.org/10.1051/0004-6361/201935523, 628, A27

  10. [10]

    M., 2022, A&A https://doi.org/10.1051/0004-6361/202140519, 662, A32

    Bouscasse L., Csengeri T., Belloche A., Wyrowski F., Bontemps S., G \"u sten R., Menten K. M., 2022, A&A https://doi.org/10.1051/0004-6361/202140519, 662, A32

  11. [11]

    2009, Int

    Balucani, N. 2009, Int. J. Mol. Sci. https://doi.org/10.3390/ijms10052304, 10, 2304

  12. [12]

    Belloche, A., et al., 2020, A&A https://doi.org/10.1051/0004-6361/201937352, 635, A198

  13. [13]

    Belloche, A., M \"u ller , H. S. P., Garrod , R. T. Menten , K. M., 2016, A&A https://doi.org/10.1051/0004-6361/201527268, 587, A91

  14. [14]

    Belloche, A., M \"u ller, H. S. P., Menten, K. M., Schilke, P., Comito, C., 2013, A&A https://doi.org/10.1051/0004-6361/201321096, 559, A47

  15. [15]

    Bianchi, E., et al., 2022, A&A https://doi.org/10.1051/0004-6361/202141893, 662, A103

  16. [16]

    et al., 2023, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/acb5e8/meta, 944, 208

    Bianchi, E. et al., 2023, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/acb5e8/meta, 944, 208

  17. [17]

    2014, A&A https://doi.org/10.1051/0004-6361/201423890, 566, L5

    Biver, N., Bockel \'e e-Morvan, D., Debout, V., et al. 2014, A&A https://doi.org/10.1051/0004-6361/201423890, 566, L5

  18. [18]

    2022, A&A https://doi.org/10.1051/0004-6361/202244970, 668, A171

    Biver, N., Boissier, J., Bockel \'e e-Morvan D., et al. 2022, A&A https://doi.org/10.1051/0004-6361/202244970, 668, A171

  19. [19]

    Bottinelli, S., et al., 2004, ApJ https://iopscience.iop.org/article/10.1086/423952/meta, 615, 354

  20. [20]

    A., Handa, T., Nagayama, T., Sunada, K., & Omodaka, T., 2016, MNRAS https://doi.org/10.1093/mnras/stw958, 460, 283

    Burns, R. A., Handa, T., Nagayama, T., Sunada, K., & Omodaka, T., 2016, MNRAS https://doi.org/10.1093/mnras/stw958, 460, 283

  21. [21]

    -E., Evans, N

    Baek, G., Lee, J. -E., Evans, N. J., et al., 2023, ApJL https://iopscience.iop.org/article/10.3847/2041-8213/acef1d, 954, L25

  22. [22]

    A., Cernicharo, J., Viti, S., Marcelino, N., Palau, A., Esplugues, G

    Bell, T. A., Cernicharo, J., Viti, S., Marcelino, N., Palau, A., Esplugues, G. B., Tercero, B., 2014, A&A https://doi.org/10.1051/0004-6361/201321872, 564, A114

  23. [23]

    S., Pearson, J

    Brauer, C. S., Pearson, J. C., Drouin, B. J., Yu, S., 2009, ApJS https://ui.adsabs.harvard.edu/abs/2009ApJS..184..133B/abstract, 184, 133

  24. [24]

    P., Anderson D

    Burnham K. P., Anderson D. R., 2004, Sociol. Methods Res. https://journals.sagepub.com/doi/10.1177/0049124104268644, 33, 261

  25. [25]

    K., M \"u ller, H

    Calcutt, H., J rgensen, J. K., M \"u ller, H. S. P. et al., 2018, A&A https://doi.org/10.1051/0004-6361/201732289, 616, A90

  26. [26]

    J., Chandler C

    Cacciapuoti L., Macias E., Maury A. J., Chandler C. J., Sakai N., Tychoniec ., Viti S., et al., 2023, https://ui.adsabs.harvard.edu/abs/2023A\

  27. [27]

    Carpenter, J. M. 2000, AJ https://iopscience.iop.org/article/10.1086/316845/meta, 120, 3139

  28. [28]

    Ceccarelli, C., et al., 2017, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/aa961d/meta, 850, 176

  29. [29]

    Cesaroni, R., Moscadelli, L., Neri, R., Sanna, A., Caratti o Garatti, A., Eisloffel, J., Stecklum, B., et al., 2018, https://ui.adsabs.harvard.edu/abs/2018A\

  30. [30]

    2008, ApJ https://iopscience.iop.org/article/10.1086/595583/meta, 688, L83

    Cernicharo, J., Gu \'e lin, M., Ag \'u ndez, M., et al. 2008, ApJ https://iopscience.iop.org/article/10.1086/595583/meta, 688, L83

  31. [31]

    2013, ApJL https://iopscience.iop.org/article/10.1088/2041-8205/771/1/L10/meta, 771, L10

    Cernicharo, J., Tercero, B., Fuente, A., et al. 2013, ApJL https://iopscience.iop.org/article/10.1088/2041-8205/771/1/L10/meta, 771, L10

  32. [32]

    M., et al

    Coletta, A., Fontani, F., Rivilla, V. M., et al. 2020, A&A https://doi.org/10.1051/0004-6361/202038212, 641, A54

  33. [33]

    M., Beltr \'a n, M

    Colzi, L., Rivilla, V. M., Beltr \'a n, M. T., et al. 2021, A&A https://doi.org/10.1051/0004-6361/202141573, 653, A129

  34. [34]

    A., Palmer, M

    Cordiner, M. A., Palmer, M. Y., Nixon, C. A., et al. 2015, ApJL https://iopscience.iop.org/article/10.1088/2041-8205/800/1/L14/meta, 800, L14

  35. [35]

    R., Garrod, R

    Coutens, A., Willis, E. R., Garrod, R. T., et al. 2018, A&A https://doi.org/10.1051/0004-6361/201732346, 612, A107

  36. [36]

    N., & Pilachowski, C

    Cox, A. N., & Pilachowski, C. A. 2000, Physics Today https://doi.org/10.1063/1.1325201, 53, 77

  37. [37]

    P., Juhasz, A., Pohl, A., et al

    Dullemond, C. P., Juhasz, A., Pohl, A., et al. 2012, RADMC-3D: A multi-purpose radiative transfer tool. Astrophysics Source Code Library, record ascl:1202.015 https://ui.adsabs.harvard.edu/abs/2012ascl.soft02015D/abstract

  38. [38]

    Y., Lai, S

    Duan, H. Y., Lai, S. P., Hirano, N., & Thieme, T. J. 2023, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/acb531, 947(2), 48

  39. [39]

    2019, ACS Earth Space Chem

    Enrique-Romero, J., Rimola, A., Ceccarelli, C., et al. 2019, ACS Earth Space Chem. https://doi.org/10.1021/acsearthspacechem.9b00156, 3, 2158

  40. [40]

    Enrique-Romero, J., Ceccarelli, C., Rimola, A., et al., 2021, A&A https://doi.org/10.1051/0004-6361/202141531, 655, A9

  41. [41]

    2022, ApJs https://iopscience.iop.org/article/10.3847/1538-4365/ac480e/meta, 259, 39

    Enrique-Romero, J., Rimola, A., Ceccarelli, C., et al. 2022, ApJs https://iopscience.iop.org/article/10.3847/1538-4365/ac480e/meta, 259, 39

  42. [42]

    2007, A&A https://doi.org/10.1051/0004-6361:20077485, 470, 639

    Fontani, F., Pascucci, I., Caselli, P., et al. 2007, A&A https://doi.org/10.1051/0004-6361:20077485, 470, 639

  43. [43]

    N., & Widicus Weaver, S

    Friedel, D. N., & Widicus Weaver, S. L. 2012, ApJS https://iopscience.iop.org/article/10.1088/0067-0049/201/2/17/meta, 201, 17

  44. [44]

    N., Snyder, L

    Friedel, D. N., Snyder, L. E., 2008, ApJ https://ui.adsabs.harvard.edu/abs/2008ApJ...672..962F/exportcitation, 672, 962

  45. [45]

    1997, ApJ https://iopscience.iop.org/article/10.1086/304782/meta, 489, 113

    Fukuzawa, K., & Osamura, Y. 1997, ApJ https://iopscience.iop.org/article/10.1086/304782/meta, 489, 113

  46. [46]

    2016, The Journal of Open Source Software https://joss.theoj.org/papers/10.21105/joss.00024, 1, 24

    Foreman-Mackey, D. 2016, The Journal of Open Source Software https://joss.theoj.org/papers/10.21105/joss.00024, 1, 24

  47. [47]

    Gardner, F. F. & Winnewisser, G. 1975, ApJ https://adsabs.harvard.edu/full/1975ApJ...195L.127G, 195, L127

  48. [48]

    Garrod, R. T. 2013, ApJ https://iopscience.iop.org/article/10.1088/0004-637X/765/1/60/meta, 765, 60

  49. [49]

    T., Jin, M., Matis, K

    Garrod, R. T., Jin, M., Matis, K. A., et al. 2022, ApJs https://iopscience.iop.org/article/10.3847/1538-4365/ac3131/meta, 259, 1

  50. [50]

    T., Belloche, A., Müller, H

    Garrod, R. T., Belloche, A., Müller, H. S. P., Menten, K. M. 2017, A&A https://doi.org/10.1051/0004-6361/201630254, 601, A48

  51. [51]

    T., Herbst E., 2006, A&A, 457, 927

    Garrod R. T., Herbst E., 2006, A&A, 457, 927

  52. [52]

    H., et al

    Goesmann, F., Rosenbauer, H., Bredeh\"oft, J. H., et al. 2015, Science https://doi.org/10.1126/science.aab0689, 349, 020689

  53. [53]

    J., Fried, L

    Goldman, N., Reed, E. J., Fried, L. E., William Kuo, I. F., & Maiti, A. 2010, Nature Chem. https://doi.org/10.1038/nchem.827, 2, 949

  54. [54]

    2023, MNRAS https://doi.org/10.1093/mnras/stad2892, 526, 4535

    Giani, L., Ceccarelli, C., Mancini, L., et al. 2023, MNRAS https://doi.org/10.1093/mnras/stad2892, 526, 4535

  55. [55]

    Hern \'a ndez-Hern \'a ndez, V., Zapata, L., Kurtz, S., Garay, G., 2014, ApJ https://ui.adsabs.harvard.edu/abs/2014ApJ...786...38H/abstract, 786, 38

  56. [56]

    K., Juvela, M., Tej, A., Goldsmith, P

    Hoque, A., Baug, T., Dewangan, L. K., Juvela, M., Tej, A., Goldsmith, P. F., Garc \' a, P., et al., 2025, ApJ https://ui.adsabs.harvard.edu/abs/2025ApJ...987..197H/abstract, 987, 197

  57. [57]

    E., Herbst, E., Garrod, R

    Hassel, G. E., Herbst, E., Garrod, R. T. 2008, ApJ https://iopscience.iop.org/article/10.1086/588185/meta, 681, 1385

  58. [58]

    T., Ilyushin, V., Ziurys, L

    Halfen, D. T., Ilyushin, V., Ziurys, L. M., 2011, ApJ https://iopscience.iop.org/article/10.1088/0004-637X/743/1/60/meta, 743, 60

  59. [59]

    Naturforsch

    Heise, H.M., Lutz, H., Dreizler, H., 1974, Z. Naturforsch. A https://doi.org/10.1515/zna-1974-0916, 29a, 1345

  60. [60]

    Herbst, E., & van Dishoeck, E. F. 2009, ARA&A https://doi.org/10.1146/annurev-astro-082708-101654, 47, 427

  61. [61]

    Hsieh T.-H., et al., 2023, A&A https://doi.org/10.1051/0004-6361/202244183, 669, A137

  62. [62]

    2021, A&A https://doi.org/10.1051/0004-6361/202039798, 647, A23

    Hirota, T., Cesaroni, R., Moscadelli, L., et al. 2021, A&A https://doi.org/10.1051/0004-6361/202039798, 647, A23

  63. [63]

    V., Alekseev, E

    Ilyushin, V. V., Alekseev, E. A., Dyubko, S. F., Motiyenko, R. A., Hougen, J. T., 2005, JMoSp https://ui.adsabs.harvard.edu/abs/2005JMoSp.229..170I/abstract, 229, 170

  64. [64]

    Iino, T., Sagawa, H., Tsukagoshi, T., 2020, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/ab66b0, 890, 95

  65. [65]

    D., et al., 2021, ApJs https://iopscience.iop.org/article/10.3847/1538-4365/ac1441/meta, 257, 9

    Ilee J. D., et al., 2021, ApJs https://iopscience.iop.org/article/10.3847/1538-4365/ac1441/meta, 257, 9

  66. [66]

    Irvine, W. M. & Schloerb, F. 1984, ApJ https://adsabs.harvard.edu/full/record/seri/ApJ../0282/1984ApJ...282..516I.html, 282, 516

  67. [67]

    K., J rgensen, J

    Jacobsen, S. K., J rgensen, J. K., van der Wiel, M. H. D., et al. 2018, A&A https://doi.org/10.1051/0004-6361/201731668, 612, A72

  68. [68]

    2017, A&A https://doi.org/10.1051/0004-6361/201629506, 597, A40

    Jaber Al-Edhari, A., Ceccarelli, C., Kahane, C., et al. 2017, A&A https://doi.org/10.1051/0004-6361/201629506, 597, A40

  69. [69]

    R., Lovas, F

    Johnson, D. R., Lovas, F. J., Gottlieb, C. A., et al. 1977, ApJ https://adsabs.harvard.edu/full/1977ApJ...218..370J, 218, 370

  70. [70]

    K., Belloche, A., Garrod, R

    J rgensen, J. K., Belloche, A., Garrod, R. T., 2020, ARA&A https://doi.org/10.1146/annurev-astro-032620-021927, 58, 727

  71. [71]

    1992, ApJ https://adsabs.harvard.edu/full/record/seri/ApJ../0386/1992ApJ...386L..51K.html, 386, L51

    Kawaguchi, K., Ohishi, M., Ishikawa, S.-I., & Kaifu, N. 1992, ApJ https://adsabs.harvard.edu/full/record/seri/ApJ../0386/1992ApJ...386L..51K.html, 386, L51

  72. [72]

    1994, ApJ https://adsabs.harvard.edu/full/1994ApJ...420L..95K, 420, L95

    Kawaguchi, K., Kasai, Y., Ishikawa, S.-I., et al. 1994, ApJ https://adsabs.harvard.edu/full/1994ApJ...420L..95K, 420, L95

  73. [73]

    Kurtz, S., Cesaroni, R., Churchwell, E., Hofner, P., & Walmsley, C. M. 2000, in Protostars and Planets IV https://ui.adsabs.harvard.edu/abs/2000prpl.conf..299K/abstract, eds. V. Mannings, A. P. Boss, & S. S. Russell (Tucson: University of Arizona Press), 299

  74. [74]

    G., Nelson, A

    Kukolich, S. G., Nelson, A. C., 1971, CPL https://ui.adsabs.harvard.edu/abs/1971CPL....11..383K/abstract, 11, 383

  75. [75]

    H., Johnson, D

    Kirchhoff, W. H., Johnson, D. R., Lovas, F. J., 1973, JPCRD https://ui.adsabs.harvard.edu/abs/1973JPCRD...2....1K/abstract, 2, 1

  76. [76]

    Disentangling the independently controllable factors of variation by interacting with the world

    Lada, C. J., & Lada, E. A. 2003, ARA&A https://doi.org/10.1146/annurev.astro.41.011802.094844, 41, 57

  77. [77]

    Ligterink, N. F. W., Calcutt, H., Coutens, A., et al. 2018, A&A https://doi.org/10.1051/0004-6361/201731980, 619, A28

  78. [78]

    Ligterink, N. F. W., El-Abd, S. J., Brogan, C. L., et al. 2020, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/abad38/meta, 901, 37

  79. [79]

    2020, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/abc0ec/meta, 904, 181

    Liu, S.-Y., Su, Y.-N., Zinchenko, I., et al. 2020, ApJ https://iopscience.iop.org/article/10.3847/1538-4357/abc0ec/meta, 904, 181

  80. [80]

    et al., 2014, A&A https://doi.org/10.1051/0004-6361/201423622, 572, A44

    L \'o pez, A., Tercero, B., Kisiel, Z. et al., 2014, A&A https://doi.org/10.1051/0004-6361/201423622, 572, A44

Showing first 80 references.