REVIEW 3 major objections 4 minor 3 cited by
The NewEra model grid
T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash
Pith's one-line read The NewEra LTE grid—37,438 spherical stellar atmosphere models—is now public, with spectra substantially changed by updated molecular line lists.
desk verdict A well-documented, genuinely useful new PHOENIX model grid with updated opacities; the main soft spot is that validation is internal (line-list reduction tested only at 25 Å for one model) rather than against observed spectra. 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 machinery is the PHOENIX/1D stellar atmosphere code in LTE and spherical symmetry, driven by three input blocks: the ACES chemical-equilibrium solver for the equation of state, the atomic line database with 851 million lines, and a molecular database, dominated by Exomol, with 834 billion lines. A line-selection step compares each candidate line's central opacity to the local continuous opacity at reference layers and keeps lines whose line-to-continuum opacity ratio exceeds $10^{-4}$; strong lines get Voigt profiles while weaker lines get Gauss profiles. This reduces the list actually used per model to roughly 800,000 to 2.5 million atomic lines and about 20.5 billion molecular lines. The HDF5 delivery format is the final part of the machinery: one file per model contains the restartable atmosphere structure, the input namelist, and high-sampling spectra from 900 Å to 30 µm at sampling rates above one million except in parts of the 5.8–30 µm range.
What would settle it
Take a well-observed benchmark star with known parameters, such as the Sun or a nearby M dwarf, and compare high-resolution observed spectra in molecular bands (for example water or FeH in the optical or near-infrared) to the corresponding NewEra model after proper convolution; wavelength-correlated residuals beyond the 0.4% flux level quoted for the 25 Å convergence test would show the reduced line list misses opacity that matters at high resolution.
Extended reading notes
Core claim
On its own terms, the paper's central claim is that the new NewEra LTE grid delivers substantially more realistic synthetic spectra than earlier PHOENIX grids while keeping the parameter coverage and structure of the well-used ACES grid. Most of the spectral change comes from new molecular line opacities, not from changes in the atmospheric structure. A second, quantitative claim is that the adopted line-selection reduction, from 823.8 billion molecular lines down to about 20.5 billion actually used lines, changes the emitted flux at 25 Å resolution by less than 0.4%, so the grid is considered converged relative to the full line list. In addition, all models use spherical symmetry, which changes center-to-limb variation in a way that matters for both giants and, to a lesser degree, dwarfs; this affects limb-darkening modeling in transits. The paper does not compare any NewEra spectrum to an observed stellar spectrum; calibration of hydrogen line dissolution is cited as in preparation.
Load-bearing premise
The load-bearing premise is that the line opacities—the reduced 20.5-billion-line molecular list, the atomic database, and the LTE approximation up to 12000 K—preserve the real opacity of stellar atmospheres closely enough that the released spectra and limb darkening match actual stars; the paper checks convergence of the reduction at 25 Å resolution and code consistency, but does not compare any model to an observed spectrum.
Editorial extensions
If this is right
- Analyses currently using ACES, GAIA DR1, or NextGen spectra can be rerun on NewEra, with the largest changes in optical cool-star spectra where molecular opacities dominate.
- Transit light-curve fits relying on limb-darkening coefficients need updated values from spherical models, especially for giants and for dwarfs where the wavelength-dependent apparent radius matters.
- The HDF5 files make it possible to reconstruct the exact model structure and input line lists, so derived abundances or C/O ratios can be traced back to specific line data.
- The GAIA-compatible and JWST-compatible low-sampling spectra allow direct use in population synthesis and JWST archive tools without re-gridding.
- The newly calibrated hydrogen line dissolution will shift Balmer-line predictions and hotter-star analyses relative to earlier grids.
Reading between the lines
- A testable consequence of better molecular lists is that elemental abundances derived from M-dwarf spectra will shift systematically relative to older grids; a benchmark sample with independent abundance constraints would reveal whether the shifts are improvements.
- The 25 Å convergence test does not by itself guarantee convergence at the highest sampling rates delivered; users working near the sampling limit should verify that omitted weak lines do not affect narrow-band features such as exoplanet transmission windows.
- The stated plan for an NLTE version implies that LTE residuals will soon be quantified for hotter stars; until then, the LTE grid is the natural baseline against which those NLTE corrections will be measured.
- Because limb-darkening data are only available upon request, a natural follow-up is precomputed limb-darkening coefficients; wavelength-dependent spherical limb darkening should measurably improve transit depth precision for small planets around cool stars.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents the NewEra LTE grid of 37,438 spherically symmetric PHOENIX/1D model atmospheres and synthetic spectra, covering Teff 2300–12000 K, log g 0.0–6.0, [M/H] −4.0 to +0.5, and alpha-element variations. The input physics uses the ACES equation of state, about 851 million Kurucz atomic lines, and about 823.8 billion molecular lines, mostly from Exomol, reduced to roughly 20.5 billion lines. The paper documents the atmospheric structures, spectral energy distributions, limb darkening, and comparisons to the NextGen, GAIA DR1, and ACES grids. The main scientific claims are that the grid is computed, publicly released in HDF5 format with high- and low-sampling spectra, and that the spectra differ substantially from previous grids, mostly because of updated molecular line lists.
Significance. If the grid is accurate, it will be a valuable community resource for stellar spectroscopy, stellar populations, and transit light-curve analysis. The paper has clear strengths: the grid construction is carefully documented, cross-platform reproducibility is checked to 10^-6 relative accuracy, the line-selection procedure is described in detail, the data release includes checksums and access software, and the 37,438-model grid is a substantial extension of the ACES parameter space. The main weakness is that the central claim of improved spectra is not validated against observed stellar spectra or flux-calibrated observations; the comparisons are exclusively against older synthetic grids. The paper is transparent about the LTE approximation and about the hydrogen line dissolution calibration being in preparation, but these caveats do not by themselves replace external validation. As a data-release paper the construction is largely sound; the missing observational anchor is what prevents me from recommending acceptance without revision.
major comments (3)
- [Section 3.2, Table 3] The molecular-line reduction is validated only at 25 Å resolution for a single model (Teff=2300 K, log g=4.5, [M/H]=0), while the primary delivered product is the HSR spectrum with sampling rate at least 10^6 (Table 3) and the supplied GAIA/JWST convolutions use Gaussian filters with 0.1–2 Å scales. A flux agreement below 0.4% at 25 Å does not establish that the omitted weak lines are negligible in the high-resolution data, where individual weak lines remain visible, nor does it test other grid corners where different molecules dominate, such as TiO/VO bands near 4000–5000 K or metal-poor models with different line-to-continuum ratios. Additionally, the thresholds ('10^-3 to 10^-4 depending on species') and the criterion for 'molecules not present' are not itemized, so the reduction is not independently reproducible. Please provide high-resolution flux comparisons for several grid corners and quantify the per-species thresholds and molecule-omission criterion.
- [Section 4.4, Abstract] The abstract concludes that the spectra show significant differences 'mostly due to the updates of the molecular line lists,' and Section 4.4 presents these differences as improvements, but no NewEra spectrum is compared with observed stellar spectra or observed flux-calibrated SEDs. The only external anchor mentioned, the hydrogen line dissolution calibration against Sirius A, is cited as 'in preparation.' As a result, the paper supports the statement that the grid differs from older grids, but not that it is more accurate. Please add comparisons to observed spectra for a small set of representative stars spanning the grid, or explicitly reword the conclusions to present the grid as an internally consistent update whose observational validation remains to be demonstrated.
- [Sections 1 and 5] The LTE assumption up to Teff=12000 K is flagged as an approximation with an NLTE grid in preparation, but the caveat is not quantified. For effective temperatures above roughly 8000–9000 K, non-LTE effects on Balmer lines and ionization equilibria are known to be important, and users of the released HSR spectra cannot judge where the LTE assumption degrades. I request either quantitative statements (for example, comparisons of selected models with existing NLTE calculations) or a clear statement in the abstract and data documentation restricting the reliable temperature range for spectroscopic applications.
minor comments (4)
- [Section 4.6] The phrase 'intervals based sampling rate' is awkward and should be rephrased, for example as 'The HSR spectra have a sampling rate lambda/delta-lambda of at least one million.'
- [Section 4.4] The statement that NewEra and ACES spectra differ little in the near-IR because of very similar water line data sits in some tension with the broad claim that molecular line updates drive the large differences; please clarify which wavelength regions are dominated by which molecular species and why the near-IR agreement does not contradict the overall conclusion.
- [Section 4.6] The LSR wavelength range is described as extending from the soft X-ray to the radio region, but Table 4 ends at 1000 microns (sub-millimeter); please adjust the wording to match the actual coverage.
- [Section 6] Given that limb darkening is a stated motivation for the grid, making limb darkening data available only 'upon request' is a limitation for users of the release; consider providing at least a representative set of center-to-limb intensity profiles in the HDF5 files.
Circularity Check
No significant circularity: the grid spectra are computed from external line lists and an independent EOS, and self-citations supply input parameters rather than conclusions.
full rationale
The NewEra grid is computed from external line-opacity databases (Kurucz atomic lines; Exomol molecular lines) and the ACES equation of state, which is used as a solver with its own thermodynamic data. No quantity claimed as a prediction is fitted to the grid's own output. The molecular-line reduction is validated by directly comparing spectra computed with the full 823.8-billion-line list and the reduced 20.5-billion-line list for one representative model, which is a ground-truth check rather than a circular re-derivation. The paper's inheritance of the mass-Teff-log(g) setup, mixing length, and abundance patterns from Husser et al. (2013), and its use of ACES from Barman et al. (2011), are references to prior work by overlapping authors, but these references supply input parameters and a solver, not the conclusions; no uniqueness theorem or fitted prediction is carried by self-citation. The calibration of hydrogen line dissolution to Sirius A (Aufdenberg et al., in preparation) is an external calibration, and the paper does not present a Sirius A spectrum as a test of that calibration. Comparisons to the NextGen, GAIA DR1, and ACES grids are independent benchmarks. The lack of direct comparison to observed stellar spectra and the 25-A-only validation of the line-list reduction are scientific limitations or correctness risks, not circularity.
Assumptions & free parameters
free parameters (4)
- Atomic line selection thresholds =
10^-4 and 1
- Molecular line inclusion threshold =
10^-3 to 10^-4 per species
- Mixing length and mass-Teff-log(g) relations =
Inherited from Husser et al. (2013)
- Hydrogen line dissolution calibration =
Calibrated to Sirius A NLTE modeling (in preparation)
assumptions (5)
- domain assumption ACES equation of state correctly computes chemical equilibrium for 839 species under stellar atmosphere conditions.
- domain assumption Kurucz (2017) atomic line data and Exomol molecular line data are sufficiently complete and accurate for the grid's purposes.
- domain assumption LTE is a valid approximation for the parameter space covered, including Teff up to 12000 K.
- domain assumption The mass-Teff-log(g) and mixing length relations from Husser et al. (2013) apply to the NewEra grid.
- domain assumption Spherical symmetry provides adequate center-to-limb variation for dwarf models.
Cite this review
Pith. "Pith review of The NewEra model grid." pith.science (2026). https://pith.science/paper/C45A6QJM
@misc{pith2026250417597,
author = {Pith},
title = {Pith review of: The NewEra model grid},
year = {2026},
howpublished = {\url{https://pith.science/paper/C45A6QJM}},
note = {Machine review of arXiv:2504.17597}
}
abstract
Analyses of stellar spectra, stellar populations, and transit light curves rely on grids of synthetic spectra and center-to-limb variations (limb darkening) from model stellar atmospheres. Extensive model grids from PHOENIX, a generalized non-LTE 1D and 3D stellar atmosphere code, have found widespread use in the astronomical community, however current PHOENIX/1D models have been substantially improved over the last decade. To make these improvements available to the community, we have constructed the NewEra LTE model grid consisting of 37438 models with $2300K \leq T_{eff} \leq 12000K$, $0.0\le log{(g)} \le 6.0$ metallicities [M/H] from $-4.0$ to $+0.5$, and for metallicities $-2.0 \le [M/H] \le 0.0$ additional $\alpha$ element variations from $-0.2 \le [\alpha/{\rm Fe}] \le +1.2$ are included. The models use databases of 851 million atomic lines and 834 billion molecular lines and employ the Astrophysical Chemical Equilibrium Solver for the equation of state. All models in the NewEra grid have been calculated in spherical symmetry because center-to-limb variation differences from plane-parallel models are quite large for giants and not insignificant for dwarfs. All model data are provided in the Hierarchical Data Format 5 (HDF5) format, including low and high sampling rate spectra. These files also include a variety of details about the models, such as the exact abundances and isotopic patterns used and results of the atomic and molecular line selection. Although the model structures have small differences with the previous grid generation, the spectra show significant differences, mostly due to the updates of the molecular line lists.
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Reference graph
Works this paper leans on
-
[1]
Y., Yachmenev, A., Yurchenko, S
Adam, A. Y., Yachmenev, A., Yurchenko, S. N., & Jensen, P. 2019, Journal of Physical Chemistry A, 123, 4755 Al Derzi, A. R., Furtenbacher, T., Tennyson, J., Yurchenko, S. N., & Császár, A. G. 2015,J. Quant. Spec. Radiat. Transf. , 161, 117
2019
-
[2]
Yurchenko, S. N. 2016,MNRAS, 461, 1012
2016
-
[3]
F., Yachmenev, A., Tennyson, J., & Yurchenko, S
Al-Refaie, A. F., Yachmenev, A., Tennyson, J., & Yurchenko, S. N. 2015, MNRAS, 448, 1704
2015
-
[4]
& Hauschildt, P
Allard, F. & Hauschildt, P. H. 1995, ApJ, 445, 433
1995
-
[5]
Amaral, P. H. R., Diniz, L. G., Jones, K. A., et al. 2019,ApJ, 878, 95
2019
-
[6]
Azzam, A. A. A., Tennyson, J., Yurchenko, S. N., & Naumenko, O. V. 2016, MNRAS, 460, 4063
2016
-
[7]
J., Strange, J
Barber, R. J., Strange, J. K., Hill, C., et al. 2014,MNRAS, 437, 1828
2014
-
[8]
S., Hauschildt, P
Barman, T. S., Hauschildt, P. H., Schweitzer, A., et al. 2002,ApJ, 569, L51
2002
Show all 108 references
-
[9]
S., Hauschildt, P
Barman, T. S., Hauschildt, P. H., Short, C. I., & Baron, E. 2000,ApJ, 537, 946
2000
-
[10]
S., Macintosh, B., Konopacky, Q
Barman, T. S., Macintosh, B., Konopacky, Q. M., & Marois, C. 2011, ApJ, 733, 65
2011
-
[11]
R., Jeffers, S
Barnes, J. R., Jeffers, S. V., Haswell, C. A., et al. 2024,MNRAS, 534, 1257
2024
-
[12]
E., Branch, D., et al
Baron, E., Nugent, P. E., Branch, D., et al. 2003,ApJ, 586, 1199
2003
-
[13]
J., Chiu, C., Golpayegani, S., et al
Barton, E. J., Chiu, C., Golpayegani, S., et al. 2014,MNRAS, 442, 1821
2014
-
[14]
J., Yurchenko, S
Barton, E. J., Yurchenko, S. N., & Tennyson, J. 2013,MNRAS, 434, 1469
2013
-
[15]
Bernath, P. F. 2020,J. Quant. Spec. Radiat. Transf. , 240, 106687
2020
-
[16]
F., Dodangodage, R., & Liévin, J
Bernath, P. F., Dodangodage, R., & Liévin, J. 2022,ApJ, 933, 99
2022
-
[17]
Bittner, D. M. & Bernath, P. F. 2018,ApJS, 235, 8
2018
-
[18]
Brooke, J. S. A., Bernath, P. F., & Western, C. M. 2015, J. Chem. Phys. , 143, 026101
2015
-
[19]
W., J., et al
Burrows, A., Dulick, M., Bauschlicher, C. W., J., et al. 2005,ApJ, 624, 988
2005
-
[20]
2008, ApJ, 676, 857
Casebeer, D., Baron, E., Leighly, K., Jevremovic, D., & Branch, D. 2008, ApJ, 676, 857
2008
-
[21]
Chapman, R. D. 1966,ApJ, 143, 61 Chubb,K.L.,Naumenko,O.,Keely,S.,etal.2018, J. Quant. Spec. Ra- diat. Transf., 218, 178
1966
-
[22]
L., Tennyson, J., & Yurchenko, S
Chubb, K. L., Tennyson, J., & Yurchenko, S. N. 2020,MNRAS, 493, 1531
2020
-
[23]
Clark, V. H. J., Owens, A., Tennyson, J., & Yurchenko, S. N. 2020, J. Quant. Spec. Radiat. Transf. , 246, 106929
2020
-
[24]
M., Lodi, L., & Tennyson, J
Coppola, C. M., Lodi, L., & Tennyson, J. 2011,MNRAS, 415, 487
2011
-
[25]
Coxon, J. A. & Hajigeorgiou, P. G. 2015, J. Quant. Spec. Ra- diat. Transf., 151, 133
2015
-
[26]
D., et al
Darby-Lewis, D., Tennyson, J., Lawson, K. D., et al. 2018, Journal of Physics B Atomic Molecular Physics, 51, 185701
2018
-
[27]
J., Sampedro, L., Alfaro, E
Delgado, A. J., Sampedro, L., Alfaro, E. J., et al. 2016,MNRAS, 460, 3305
2016
-
[28]
M., Baron, E., Branch, D., et al
DerKacy, J. M., Baron, E., Branch, D., et al. 2020,ApJ, 901, 86
2020
-
[29]
W., J., Burrows, A., et al
Dulick, M., Bauschlicher, C. W., J., Burrows, A., et al. 2003,ApJ, 594, 651
2003
-
[30]
M., Bernath, P
Fernando, A. M., Bernath, P. F., Hodges, J. N., & Masseron, T. 2018, J. Quant. Spec. Radiat. Transf. , 217, 29
2018
-
[31]
T., et al
Friesen, B., Baron, E., Parrent, J. T., et al. 2017,MNRAS, 467, 2392
2017
-
[32]
J., Bernath, P
Frohman, D. J., Bernath, P. F., & Brooke, J. S. A. 2016, J. Quant. Spec. Radiat. Transf. , 169, 104
2016
-
[33]
Fuhrmeister, B., Schmitt, J. H. M. M., & Hauschildt, P. H. 2005,A&A, 436, 677
2005
-
[34]
Fuhrmeister, B., Schmitt, J. H. M. M., & Hauschildt, P. H. 2010,A&A, 511, A83
2010
-
[35]
E., Rothman, L
Gordon, I. E., Rothman, L. S., Hill, C., et al. 2017,J. Quant. Spec. Ra- diat. Transf., 203, 3
2017
-
[36]
N., Yurchenko, S
Gorman, M. N., Yurchenko, S. N., & Tennyson, J. 2019,MNRAS, 490, 1652 Article number, page 10 Hauschildt et al.: NewEra model grid
2019
-
[37]
J., Tennyson, J., Kaminsky, B
Harris, G. J., Tennyson, J., Kaminsky, B. M., Pavlenko, Y. V., & Jones, H. R. A. 2006,MNRAS, 367, 400
2006
-
[38]
H., Barman, T., & Baron, E
Hauschildt, P. H., Barman, T., & Baron, E. 2008, Physica Scripta Vol- ume T, 130, 014033
2008
-
[39]
2025, The PHOENIX/1D NewEra model atmosphere grid: Access software
Schweitzer, A. 2025, The PHOENIX/1D NewEra model atmosphere grid: Access software
2025
-
[40]
Hauschildt, P. H. & Baron, E. 1999, Journal of Computational and Applied Mathematics, 109, 41
1999
-
[41]
H., Baron, E., & Allard, F
Hauschildt, P. H., Baron, E., & Allard, F. 1997, ApJ, 483, 390
1997
-
[42]
Hodges, J. N. & Bernath, P. F. 2017,ApJ, 840, 81
2017
-
[43]
& Bernath, P
Hou, S. & Bernath, P. F. 2017,J. Quant. Spec. Radiat. Transf. , 203, 511
2017
-
[44]
& Bernath, P
Hou, S. & Bernath, P. F. 2018,J. Quant. Spec. Radiat. Transf. , 210, 44
2018
-
[45]
O., Wende-von Berg, S., Dreizler, S., et al
Husser, T. O., Wende-von Berg, S., Dreizler, S., et al. 2013,A&A, 553, A6
2013
-
[46]
L., Désert, J.-M., et al
Kreidberg, L., Bean, J. L., Désert, J.-M., et al. 2014,Nature, 505, 69
2014
-
[47]
Kurucz, R. L. 1992, Rev. Mexicana Astron. Astrofis., 23, 45
1992
-
[48]
Kurucz, R. L. 2017, Canadian Journal of Physics, 95, 825 Kučinskas, A., Hauschildt, P. H., Ludwig, H. G., et al. 2005,A&A, 442, 281
2017
-
[49]
N., & Bernath, P
Langleben, J., Tennyson, J., Yurchenko, S. N., & Bernath, P. 2019, MNRAS, 488, 2332
2019
-
[50]
B., Khatu, V
Lester, J. B., Khatu, V. C., & Neilson, H. R. 2017,PASP, 129, 024201
2017
-
[51]
E., Le Roy, R
Li, G., Gordon, I. E., Le Roy, R. J., et al. 2013,J. Quant. Spec. Ra- diat. Transf., 121, 78
2013
-
[52]
E., Rothman, L
Li, G., Gordon, I. E., Rothman, L. S., et al. 2015,ApJS, 216, 15
2015
-
[53]
Y., Tennyson, J., & Yurchenko, S
Li, H. Y., Tennyson, J., & Yurchenko, S. N. 2019,MNRAS, 486, 2351
2019
-
[54]
Littleton, J. E. & Davis, S. P. 1985, ApJ, 296, 152
1985
-
[55]
N., & Tennyson, J
Lodi, L., Yurchenko, S. N., & Tennyson, J. 2015, Molecular Physics, 113, 1998
2015
-
[56]
F., Freedman, W
Madore, B. F., Freedman, W. L., & Owens, K. 2025,ApJ, 981, 32
2025
-
[57]
P., Chubb, K
Mant, B. P., Chubb, K. L., Yachmenev, A., Tennyson, J., & Yurchenko, S. N. 2020, Molecular Physics, 118, e1581951
2020
-
[58]
P., Yachmenev, A., Tennyson, J., & Yurchenko, S
Mant, B. P., Yachmenev, A., Tennyson, J., & Yurchenko, S. N. 2018, MNRAS, 478, 3220
2018
-
[59]
2014,A&A, 571, A47
Masseron, T., Plez, B., Van Eck, S., et al. 2014,A&A, 571, A47
2014
-
[60]
K., Masseron, T., Hoeijmakers, H
McKemmish, L. K., Masseron, T., Hoeijmakers, H. J., et al. 2019,MN- RAS, 488, 2836
2019
-
[61]
K., Syme, A.-M., Borsovszky, J., et al
McKemmish, L. K., Syme, A.-M., Borsovszky, J., et al. 2020,MNRAS, 497, 1081
2020
-
[62]
K., Yurchenko, S
McKemmish, L. K., Yurchenko, S. N., & Tennyson, J. 2016,MNRAS, 463, 771
2016
-
[63]
C., Baron, E., Branch, D., et al
Mitchell, R. C., Baron, E., Branch, D., et al. 2002,ApJ, 574, 293
2002
-
[64]
B., Taylor, S., Tennyson, J., et al
Mitev, G. B., Taylor, S., Tennyson, J., et al. 2022,MNRAS, 511, 2349
2022
-
[65]
I., Alijah, A., Zobov, N
Mizus, I. I., Alijah, A., Zobov, N. F., et al. 2017,MNRAS, 468, 1717
2017
-
[66]
R., Lester, J
Neilson, H. R., Lester, J. B., & Baron, F. 2022,A&A, 662, A38
2022
-
[67]
K., Tennyson, J., & Yurchenko, S
Owens, A., Conway, E. K., Tennyson, J., & Yurchenko, S. N. 2020, MNRAS, 495, 1927
2020
-
[68]
N., & Tennyson, J
Owens, A., Mitrushchenkov, A., Yurchenko, S. N., & Tennyson, J. 2022b, MNRAS, 516, 3995 Owens,A.,Tennyson,J.,&Yurchenko,S.N.2021, MNRAS,502,1128
2021
-
[69]
2018,MNRAS, 479, 3002 Owens,A.,Yachmenev,A.,Thiel,W.,Tennyson,J.,&Yurchenko,S.N
Owens, A., Yachmenev, A., Thiel, W., et al. 2018,MNRAS, 479, 3002 Owens,A.,Yachmenev,A.,Thiel,W.,Tennyson,J.,&Yurchenko,S.N. 2017, MNRAS, 471, 5025
2018
-
[70]
M., Reiners, A., Jeffers, S
Passegger, V. M., Reiners, A., Jeffers, S. V., et al. 2018,A&A, 615, A6
2018
-
[71]
M., Schweitzer, A., Shulyak, D., et al
Passegger, V. M., Schweitzer, A., Shulyak, D., et al. 2019,A&A, 627, A161
2019
-
[72]
J., Yurchenko, S
Paulose, G., Barton, E. J., Yurchenko, S. N., & Tennyson, J. 2015, MNRAS, 454, 1931
2015
-
[73]
I., Yurchenko, S
Pavlyuchko, A. I., Yurchenko, S. N., & Tennyson, J. 2015,MNRAS, 452, 1702
2015
-
[74]
L., Hauschildt, P
Peacock, S., Barman, T., Shkolnik, E. L., Hauschildt, P. H., & Baron, E. 2019, ApJ, 871, 235
2019
-
[75]
L., Kyuberis, A
Polyansky, O. L., Kyuberis, A. A., Zobov, N. F., et al. 2018,MNRAS, 480, 2597
2018
-
[76]
2017,MNRAS, 472, 3648
Prajapat, L., Jagoda, P., Lodi, L., et al. 2017,MNRAS, 472, 3648
2017
-
[77]
Qin, Z., Bai, T., & Liu, L. 2021,J. Quant. Spec. Radiat. Transf. , 258, 107352
2021
-
[78]
N., & Tennyson, J
Qu, Q., Yurchenko, S. N., & Tennyson, J. 2021,MNRAS, 504, 5768
2021
-
[79]
S., Brooke, J
Ram, R. S., Brooke, J. S. A., Western, C. M., & Bernath, P. F. 2014, J. Quant. Spec. Radiat. Transf. , 138, 107
2014
-
[80]
N., Tennyson, J., & Le Roy, R
Rivlin, T., Lodi, L., Yurchenko, S. N., Tennyson, J., & Le Roy, R. J. 2015, MNRAS, 451, 634
2015
-
[81]
S., Gordon, I
Rothman, L. S., Gordon, I. E., Barbe, A., et al. 2009, J. Quant. Spec. Radiat. Transf. , 110, 533
2009
-
[82]
S., Gordon, I
Rothman, L. S., Gordon, I. E., Barber, R. J., et al. 2010, J. Quant. Spec. Radiat. Transf. , 111, 2139
2010
-
[83]
2019,A&A, 630, A58
Roueff, E., Abgrall, H., Czachorowski, P., et al. 2019,A&A, 630, A58
2019
-
[84]
B., Bedell, M., Kempton, E
Savel, A. B., Bedell, M., Kempton, E. M. R., et al. 2025,AJ, 169, 135
2025
-
[85]
N., Kim, G.-S., & Tennyson, J
Semenov, M., Clark, N., Yurchenko, S. N., Kim, G.-S., & Tennyson, J. 2022, MNRAS, 516, 1158
2022
-
[86]
Shemansky, D. E. 1969,J. Chem. Phys. , 51, 689
1969
-
[87]
I., Hauschildt, P
Short, C. I., Hauschildt, P. H., Starrfield, S., & Baron, E. 2001,ApJ, 547, 1057
2001
-
[88]
G., Knutson, H
Shporer, A., O’Rourke, J. G., Knutson, H. A., et al. 2014,ApJ, 788, 92
2014
-
[89]
N., Solomonik, V
Smirnov, A. N., Solomonik, V. G., Yurchenko, S. N., & Tennyson, J. 2019, Physical Chemistry Chemical Physics (Incorporating Faraday Transactions), 21, 22794
2019
-
[90]
Smith, W. R. & Missen, R. W. 1982, Chemical reaction equilibrium analysis: Theory and Algorithms (New York: Wiley & Sons Inc)
1982
-
[91]
N., Shapiro, A
Smitha, H. N., Shapiro, A. I., Witzke, V., et al. 2025,ApJ, 978, L13
2025
-
[92]
& Tennyson, J
Sochi, T. & Tennyson, J. 2010,MNRAS, 405, 2345
2010
-
[93]
N., & Yachmenev, A
Somogyi, W., Yurchenko, S. N., & Yachmenev, A. 2021, J. Chem. Phys. , 155, 214303
2021
-
[94]
F., Tennyson, J., & Yurchenko, S
Sousa-Silva, C., Al-Refaie, A. F., Tennyson, J., & Yurchenko, S. N. 2015, MNRAS, 446, 2337
2015
-
[95]
& McKemmish, L
Syme, A.-M. & McKemmish, L. K. 2021,MNRAS, 505, 4383
2021
-
[96]
2022, GNU Parallel is a general parallelizer to run multiple serial command line programs in parallel without changing them
Tange, O. 2022, GNU Parallel is a general parallelizer to run multiple serial command line programs in parallel without changing them
2022
-
[97]
N., Al-Refaie, A
Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., et al. 2016, Journal of Molecular Spectroscopy, 327, 73 The HDF Group. 1997-2025, Hierarchical Data Format, version 5, https://www.hdfgroup.org/HDF5/
2016
-
[98]
K., Tennyson, J., & Yurchenko, S
Upadhyay, A., Conway, E. K., Tennyson, J., & Yurchenko, S. N. 2018, MNRAS, 477, 1520
2018
-
[99]
Yurchenko, S. N. 2010,MNRAS, 402, 492
2010
-
[100]
Western, C. M. 2017,J. Quant. Spec. Radiat. Transf. , 186, 221
2017
-
[101]
M., Carter-Blatchford, L., Crozet, P., et al
Western, C. M., Carter-Blatchford, L., Crozet, P., et al. 2018, J. Quant. Spec. Radiat. Transf. , 219, 127
2018
-
[102]
N., Bernath, P., et al
Wong, A., Yurchenko, S. N., Bernath, P., et al. 2017,MNRAS, 470, 882
2017
-
[103]
N., Lodi, L., & Tennyson, J
Yorke, L., Yurchenko, S. N., Lodi, L., & Tennyson, J. 2014,MNRAS, 445, 1383
2014
-
[104]
Yurchenko, S. N. 2015,J. Quant. Spec. Radiat. Transf. , 152, 28
2015
-
[105]
N., Amundsen, D
Yurchenko, S. N., Amundsen, D. S., Tennyson, J., & Waldmann, I. P. 2017, A&A, 605, A95
2017
-
[106]
N., Blissett, A., Asari, U., et al
Yurchenko, S. N., Blissett, A., Asari, U., et al. 2016,MNRAS, 456, 4524
2016
-
[107]
Yurchenko, S. N. & Tennyson, J. 2014,MNRAS, 440, 1649 Yurchenko,S.N.,Tennyson,J.,Barber,R.J.,&Thiel,W.2013,Journal of Molecular Spectroscopy, 291, 69
2014
-
[108]
N., Tennyson, J., Syme, A.-M., et al
Yurchenko, S. N., Tennyson, J., Syme, A.-M., et al. 2022,MNRAS, 510, 903
2022
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