Pith. sign in

REVIEW 4 major objections 4 minor 161 references

The $\texttt{MSG}$ model for cloudy substellar atmospheres: A grid of self-consistent substellar atmosphere models with microphysical cloud formation

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A new grid of cloudy brown dwarf models cannot reproduce the 10-micron silicate feature.

desk verdict Honest, useful cloudy-substellar grid at log g=4.0 with a real negative result, but the headline comparison to field L dwarfs is weakened by a gravity mismatch and needs revision. read the letter →

arxiv 2501.05521 v1 pith:MF3AW3ZT submitted 2025-01-09 astro-ph.EP astro-ph.SR

Beatriz Campos Estrada , David A. Lewis , Christiane Helling , Richard A. Booth , Francisco Ardévol Martínez , Uffe G. J{o}rgensen This is my paper · ORCID
classification astro-ph.EPastro-ph.SR
keywords browndwarfssubstellaratmospheresmicrophysicalcloudformationsilicateabsorptionfeatureatmosphericmixingtimescaleself-consistentatmospheremodelsDRIFTmodelMARCS
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper presents a new grid of self-consistent cloudy substellar atmosphere models, the MSG grid, produced by coupling the MARCS radiative-convective equilibrium code with the kinetic, non-equilibrium cloud formation model DRIFT. The purpose is to test whether microphysical cloud formation, with cloud opacity feeding back on the atmospheric structure, can reproduce the silicate absorption feature at about $10\,\mu$m that JWST and Spitzer observe in many brown dwarfs and in the planetary-mass companion VHS 1256 b. The result is negative: across effective temperatures $T_\mathrm{eff}=1200$–$2500$ K at $\log(g)=4.0$, none of the models produces the feature, and the models with TiO$_2$ nucleation are redder in the near-infrared than the observed population. Switching the nucleation species to SiO, or slowing the mixing timescale by up to a factor of 1000, reduces the near-infrared redness but does not bring back the silicate feature. The authors trace the missing feature to a shortage of small ($0.1$–$1\,\mu$m) cloud particles at low pressures and argue that the treatment of convection in cloud-forming regions is the likely missing ingredient.

What carries the argument

The load-bearing machinery is the iterative MSG coupling: the MARCS atmosphere code supplies pressure-temperature structure, gas density, scale height, and convective velocity to DRIFT, whose dust moment equations describe nucleation, growth, evaporation, settling, and gas-phase element depletion, with a mixing timescale $\tau_\mathrm{mix}$ parameterising convective replenishment and capped by $\beta_\mathrm{cr}=2.2$. DRIFT returns cloud particle sizes, condensate volume fractions, and depleted abundances, from which cloud opacity is computed using effective medium theory and Mie theory, and a control factor $f$ limits the change in cloud opacity and element abundances between iterations so that the radiative-convective equilibrium solution converges. This loop allows the authors to attribute the missing silicate feature to the microphysical state of the cloud at observable altitudes rather than to numerical non-convergence.

What would settle it

Re-running the MSG coupling with the convective-drag terms of Witte et al. (2011) included in the dust moment equations, and checking whether a $T_\mathrm{eff}=1500$ K, $\log(g)=4.0$ model then produces the $10\,\mu$m silicate feature in its synthetic spectrum, would settle whether the missing feature is intrinsic to the microphysics or an artifact of the mixing prescription.

Watch

Extended reading notes

Core claim

The central claim is that a microphysically self-consistent cloud model, in which the cloud opacity and gas-phase element depletion are iterated to convergence with the atmospheric structure, still cannot reproduce the observed silicate absorption band near $10\,\mu$m in substellar atmospheres. The MSG grid couples MARCS to DRIFT via a control-factor algorithm that damps oscillations in cloud opacity and depleted abundances, and it includes twelve condensate species; nonetheless the resulting spectra show a strong cloud continuum that is too red in the near-infrared when TiO$_2$ seeds the clouds. Using SiO as condensation nuclei, or scaling the mixing timescale upward by up to $1000\times$, produces less red near-infrared spectra, but the $10\,\mu$m feature remains absent. The paper argues that the model does not form enough small particles of order $0.1$–$1\,\mu$m at pressures near $0.1$–$1$ mbar, where the silicate opacity would be visible against the gas, and that the mixing prescription, which ignores convective drag on cloud particles inside detached convective zones, is the most plausible source of the discrepancy.

Load-bearing premise

The central negative result depends on the parameterised mixing timescale, in particular the assumption that inside detached convective zones $\tau_\mathrm{mix}$ stays fixed at its value at the top of the radiative zone below, which ignores the possibility that convection drags cloud particles and disrupts cloud layers.

Editorial extensions

If this is right

  • The 10 $\mu$m silicate feature becomes a sharp observational test of cloud microphysics: a model must get particle sizes, number densities, and altitudes simultaneously right to show it.
  • TiO$_2$ nucleation, a standard choice in DRIFT-based grids, yields near-infrared colors redder than the observed L dwarf population, so grids that assume it may bias inferred effective temperatures and gravities.
  • SiO nucleation and reduced mixing efficiency both make the near-infrared less red, so the two knobs are degenerate and require independent constraints from other observables.
  • Below $T_\mathrm{eff}=1600$ K the cloudy models develop detached convective zones, and the paper identifies the constant-$\tau_\mathrm{mix}$ treatment there as the prime suspect for the over-red colors and missing silicate feature.
  • The control-factor convergence scheme makes it practical to run self-consistent cloudy models down to $1200$ K, enabling JWST-era comparisons across the L/T transition.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the missing feature is an artifact of the constant-$\tau_\mathrm{mix}$ assumption in detached convective zones, including convective drag on cloud particles in the dust moment equations should restore the 10 $\mu$m band, turning it into a probe of convective cloud recycling.
  • The paper's reasoning predicts that any change that raises the number of condensation nuclei at the top of the atmosphere (higher nucleation rate, stronger mixing, or a bimodal particle size distribution) will strengthen the silicate feature; this can be tested by varying the cluster size $N_\ell$ or the size distribution in the same MARCS-DRIFT loop.
  • Because reduced-mixing models improve the near-infrared but worsen the mid-infrared match, a patchy-cloud retrieval that averages two converged MSG spectra with different cloud fractions is a natural next step, and the grid supplies the end-member spectra for it.
  • Applying the same MARCS-DRIFT coupling to hot Jupiter atmospheres, where $10\,\mu$m silicate features have already been detected, would test the same microphysics outside the brown dwarf regime.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper presents the MSG grid, a new set of self-consistent 1D substellar atmosphere models coupling MARCS with the DRIFT/StaticWeather microphysical cloud model, with effective temperatures 1200–2500 K at log(g)=4.0. The central scientific claim is that this grid, despite including cloud radiative feedback, microphysical nucleation/growth/evaporation, and an updated opacity treatment, cannot reproduce the ~10 μm silicate absorption feature seen in recent JWST/Spitzer observations, and that the TiO2-nucleation models are too red in the near-infrared compared to the observed L-dwarf population. The authors explore two modifications, switching the condensation nuclei from TiO2 to SiO and reducing the mixing efficiency, and show that both reduce the NIR redness but do not produce the silicate feature. They attribute the missing feature to a lack of small cloud particles at low pressures and to gas opacity hiding the cloud's silicate signature, and they identify convective transport as the main avenue for future work.

Significance. If the central negative result holds, the paper is valuable: it provides a new self-consistent cloudy grid with kinetic cloud formation, a documented control-theory convergence algorithm, and a clear falsifiable prediction (the absence of a 10 μm silicate feature in this modeling framework). The paper is also unusually honest: it explicitly reports where the model fails, labels the mixing-timescale scaling as breaking self-consistency, and discusses the detached-convective-zone caveat in Section 6.3. These features make the manuscript a useful benchmark for the substellar-atmosphere community. The strength of the published claim, however, depends on whether the model is compared to observations at the correct gravity and whether the mixing prescription controls the result; both need to be addressed before the negative result can be considered established for field L dwarfs.

major comments (4)
  1. [§5.3, Fig. 6] The central observational comparison is made to 2MASS 1507-1627, an L5 dwarf with Teff≈1600 K and log(g)≈5.2, while every MSG model in the grid is computed at log(g)=4.0. The cloud scale height (Eq. 10), the mixing timescale (Eq. 9), the settling-mixing balance in the dust moment equations (Eqs. 1 and 6), and the pressure where τ=1 is reached all depend on gravity. The missing 10 μm silicate feature and the too-red near-infrared continuum could therefore be artifacts of comparing a log(g)=4.0 model to a log(g)=5.2 object. The paper should either compute at least one MSG model at the gravity of the comparison target, or compare to an object with log(g)≈4.0, before concluding that the grid as a whole cannot reproduce the observed silicate feature.
  2. [§6.3, Eq. (12)] The treatment of detached convective zones is a load-bearing assumption for the central claim: when a detached convective zone appears (which it does at Teff≤1600 K, exactly the L-dwarf regime of interest), τmix is set to a constant value taken from the top of the radiative zone below. This neglects the convective velocity field and the possibility that convective motions drag cloud particles, as implemented in Witte et al. (2011). Since the paper's own discussion recognizes that this can change cloud structure and potentially inhibit reddening, the conclusion that the MSG grid cannot produce the silicate feature is conditional on this mixing prescription. A quantitative sensitivity test, or at least a clear statement that the result is a property of this mixing model rather than of the microphysical cloud treatment, is needed.
  3. [§5.5, Figs. 10–11] The 1000× slower mixing models that improve the near-infrared agreement are explicitly not self-consistent, as the authors state. These non-self-consistent models are nevertheless used in Fig. 11 to support the statement that reduced mixing makes the models less red. This is acceptable as an exploratory test, but the conclusion should be framed as a property of the perturbed, non-self-consistent model, and the manuscript should state whether the perturbed models satisfy any structural convergence check beyond the quoted criteria. As written, the reader cannot tell how much of the NIR improvement is due to the intended physical effect and how much is due to the loss of self-consistency.
  4. [§5.6, Figs. 12–13] The optical-depth analysis correctly shows that the silicate feature is present in the cloud optical properties at 0.1–1 mbar but is hidden in the emergent spectra because the gas is optically thicker at those wavelengths. However, this analysis is performed only for log(g)=4.0 models at one effective temperature. Given that the comparison targets are predominantly field dwarfs with log(g)≈5.0–5.5, the pressure and altitude of the τ=1 surface will shift, and the conclusion that the gas always hides the silicate feature needs to be demonstrated at the gravity of the observed objects.
minor comments (4)
  1. [§2, Eq. (5)] The notation V^s_ℓ is used in Eq. (5) but is not defined consistently with V_ℓ in Eq. (1); please define the per-species lower volume boundary explicitly.
  2. [§6.4] The sentence beginning 'The major reasoning for testing one species against the other Comparisons of classical and non-classical nucleation theories...' is grammatically incomplete and should be rewritten.
  3. [§5.4, Fig. 8] In the text, 'Fig. 8 left panel shows the cloud composition' is misleading: the left panel of Fig. 8 shows optical depth, while the composition is shown in the middle panel. Please correct the cross-reference.
  4. [§4.3] The control-factor percentages (10% increase, 50% decrease) are stated to have been chosen after testing on a toy model; a brief description of that toy model, or a reference to where it is described, would help the reader judge the robustness of the convergence algorithm.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the MSG grid's negative silicate result is an emergent forward-model outcome benchmarked against external data; the mixing-efficiency exploration in Sec. 5.5 is transparent and not presented as a fitted prediction.

full rationale

MSG is a forward grid rather than a derivation, so there is no claim that a first-principles result is being predicted from its own inputs. The central negative result — that the 10 µm silicate feature is absent in the synthetic spectra — emerges from the MARCS-DRIFT coupling (cloud microphysics, Mie/EMT opacities, radiative-convective equilibrium) and is tested against external observations (Cushing et al. 2005; Suárez & Metchev 2022; Miles et al. 2023; Petrus et al. 2024). The 1000× slower-mixing run in Sec. 5.5 is explicitly labelled as a self-consistency-breaking experiment ('This parameterisation breaks the self-consistency, however we find this to be the most reasonable manner to test our assumptions'), and it improves the NIR comparison while worsening the MIR/silicate comparison, so the main conclusion is not forced by the tuned parameter. Self-citations to Jørgensen et al. (2024), Helling et al. (2008c), Woitke & Helling (2004) and Witte et al. (2011) document the host codes and prior DRIFT-PHOENIX work; no uniqueness theorem is imported and no alternative is forbidden by a self-citation. The βcr=2.2 mixing parameter is traced to the external Ludwig et al. (2002) simulations and is presented as a stated choice, not as a derived prediction. The log(g)=4.0 models versus log(g)≈5.2 comparison object is a physical validity concern (gravity mismatch), not a circularity, so it does not raise the circularity score.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The model rests on standard tools (MARCS, DRIFT, Mie theory, EMT) and on several hand-set parameters, most notably the mixing timescale and its scaling, the control factor, and the choice of nucleation species. No new physical entities are introduced.

free parameters (5)
  • Number of monomers in CCN, N_l = 1000
    Set to 1000 following the DRIFT convention; a test with N_l=10 produced no visible change in the silicate feature (Section 6.2).
  • Sticking coefficient alpha_r = 1
    Assumed for all surface reactions (Section 2); affects nucleation and growth rates.
  • Critical mixing slope beta_cr = 2.2
    Taken from Ludwig et al. (2002) for the mixing timescale parameterization in radiative zones (Equation 12).
  • Control factor f and update percentages = f in (0,1], +10% / -50%
    Hand-tuned on a toy model to avoid oscillations in the MARCS-DRIFT coupling (Section 4.3).
  • Mixing timescale scaling factor = 1x, 10x, 100x, 1000x
    Applied to test reduced mixing efficiency; the 1000x case improves NIR agreement with observations, which is a posteriori tuning (Section 5.5).
assumptions (5)
  • domain assumption 1D plane-parallel, quasi-static, LTE radiative-convective equilibrium atmosphere (MARCS)
    Basis of the atmospheric structure calculation; ignores 3D circulation, patchy clouds and non-LTE effects (Section 3).
  • domain assumption Stationary microphysical cloud formation described by dust moment equations with a mixing timescale (DRIFT)
    Assumes steady state and a single average particle size from moments; no particle size distribution or time dependence (Section 2).
  • ad hoc to paper Mixing timescale parameterization with beta_cr=2.2 and constant tau_mix in detached convective zones
    Used to close the element replenishment problem; the detached-convective-zone treatment ignores convective drag on cloud particles (Sections 2 and 6.3).
  • domain assumption Effective medium theory (Bruggeman or LLL) for mixed-material optical constants
    Approximates the optical properties of inhomogeneous cloud particles as a homogeneous medium (Section 4.2).
  • domain assumption Classical nucleation theory (Gail et al. 1984) with TiO2 or SiO as the sole nucleation species
    Nucleation rates vary by orders of magnitude between theories; only one nucleating species is considered at a time (Section 6.4).

how reviews work

0 comments
Cite this review

Pith. "Pith review of The $\texttt{MSG}$ model for cloudy substellar atmospheres: A grid of self-consistent substellar atmosphere models with microphysical cloud formation." pith.science (2026). https://pith.science/paper/MF3AW3ZT

@misc{pith2026250105521,
  author       = {Pith},
  title        = {Pith review of: The $\textttMSG$ model for cloudy substellar atmospheres: A grid of self-consistent substellar atmosphere models with microphysical cloud formation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MF3AW3ZT}},
  note         = {Machine review of arXiv:2501.05521}
}
abstract

State-of-the-art JWST observations are unveiling unprecedented views into the atmospheres of substellar objects in the infrared, further highlighting the importance of clouds. Current forward models struggle to fit the silicate clouds absorption feature at ~$10\,\mu$m observed in substellar atmospheres. In the MSG model, we aim to couple the MARCS 1D radiative-convective equilibrium atmosphere model with the 1D kinetic, stationary, non-equilibrium, cloud formation model DRIFT, to create a new grid of self-consistent cloudy substellar atmosphere models with microphysical cloud formation. We aim to test if this new grid is able to reproduce the silicate cloud absorption feature at ~$10\,\mu$m. We model substellar atmospheres with effective temperatures in the range 1200-2500 K and with $\log(g)=4.0$. We compute atmospheric structures that self-consistently account for condensate cloud opacities based on microphysical properties. We present an algorithm based on control theory to help converge such self-consistent models. Synthetic atmosphere spectra are computed for each model to explore the observable impact of the cloud microphysics. We additionally explore the impact of choosing different nucleation species (TiO$_2$ or SiO) and the effect of less efficient atmospheric mixing on these spectra. The new MSG cloudy grid using TiO$_2$ nucleation shows spectra which are redder in the near-infrared compared to the currently known population of substellar atmospheres. We find the models with SiO nucleation, and models with reduced mixing efficiency are less red in the near-infrared. The grid is unable to reproduce the silicate features similar to those found in recent JWST observations and Spitzer archival data. We thoroughly discuss further work that may better approximate the impact of convection in cloud-forming regions and steps that may help resolve the silicate cloud feature.

Figures

Figures reproduced from arXiv: 2501.05521 by the authors.

Figure 1
Figure 1. Diagram of the MSG model algorithm for cloudy substellar atmospheres. The boxes with a dashed outline indicate parameters that are inputs to the model. The boxes with a dotted outline indicate control processes within the workflow. For a detailed explanation of the cloud formation process, see section 2. For a detailed explanation of the control process, see section 4.3. For the convergence criteria considered under… view at source ↗
Figure 2
Figure 2. shows the Pgas − Tgas profiles of MSG cloudy models (solid curves) and MSG cloud-free models (dashed curves), at the effec￾tive temperatures of 2400 K, 2100 K, 1800 K and 1500 K, and log(g) = 4.0. The cloudy models are consistently warmer than the cloud-free models at the same effective temperature, indi￾cating the clouds have a blanketing effect over the atmosphere. This effect is generally seen in L dwarf models (… view at source ↗
Figure 3
Figure 3. The average cloud particle size ⟨a⟩ (left), the cloud particle number density nd (middle) and the nucleation rate J⋆ (right) along the atmosphere for models with TiO2 nucleation at Teff = 2400 K, 2100 K, 1800 K and 1500 K and log(g) = 4.0. The corresponding Pgas − Tgas profiles are shown in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Left: The optical depth of the cloud (orange), gas (blue), and the total (green) throughout the atmosphere at λ ≈ 1.1 µm (solid curves) and λ ≈ 10.0 µm (dashed curves), for a MSG model with TiO2 nucleation, at 1500 K and log(g) = 4.0. The τ = 1.0 level is shown for ref…
Figure 5
Figure 5. Figure 5: Synthetic spectra of MSG models with TiO2 nucleation at Teff = 2400 K (blue), 2100 K (orange), 1800 K (green) and 1500 K (pink), with log(g) = 4.0, and the respective cloud-free spectra at the same Teff and log(g) in grey. The emergent fluxes are normalised with respec…
Figure 6
Figure 6. Figure 6: Comparison between observed spectra of 2MASS 1507-1627 (L5) and MSG cloudy models. 2MASS 1507-1627 has an effective temperature ∼1600 K (Cushing et al. 2005) and a surface gravity of about log(g)≈5.2 (Filippazzo et al. 2015). The observed data is shown in black. The NI…
Figure 7
Figure 7. Figure 7: Left: Pressure-temperature profiles for MSG models at Teff= 1500 K and log(g) = 4.0 with TiO2 nucleation (blue dashed curve) and SiO nucleation (orange solid curve). Convective zones are plotted with thicker lines while radiative zones are plotted with thinner lines. M…
Figure 8
Figure 8. Figure 8: Left: The optical depth of the cloud (orange), gas (blue), and the total (green) throughout the atmosphere at λ ≈ 1.1 µm (solid curves) and λ ≈ 10.0 µm (dashed curves), for a MSG model with SiO nucleation, at 1500 K and log(g) = 4.0. The τ = 1.0 level is shown for refe…
Figure 9
Figure 9. Figure 9: Synthetic spectra of MSG models with SiO nucleation at Teff = 1800 K (green) and 1500 K (pink) and 1200 K (brown), with log(g) = 4.0, and the respective cloud-free spectra at the same effective temperatures and log(g) in grey. The emergent fluxes are normalised with re…
Figure 10
Figure 10. Figure 10: Pressure-temperature profiles (top left) for MSG models with SiO nucleation, at Teff = 1500 K and log(g) = 4.0, and different mixing timescale scalings (1x, 10x slower, 100x slower, 1000x slower). Convective zones are plotted with thicker lines while radiative zones a…
Figure 11
Figure 11. Figure 11: Comparison between observed spectra of 2MASS 1507-1627 (black curves) (Cushing et al. 2005; Suárez & Metchev 2022) and two MSG cloudy models with SiO nucleation: one with fully self-consistent mixing (blue curves), and the other where the mixing timescale was scaled u…
Figure 12
Figure 12. Figure 12: The cloud optical depth as a function of wavelength, at four different heights (pressures) in the atmosphere, for two MSG models at Teff= 1500 K, log(g) = 4.0, with TiO2 nucleation (top) and SiO nucle￾ation (bottom). The τ = 1.0 level is plotted as the black dashed cu…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

161 extracted references · 51 canonical work pages

  1. [1]

    Ackerman, A. S. & Marley, M. S. 2001, ApJ, 556, 872

    2001

  2. [2]

    F., Changeat, Q., Waldmann, I

    Al-Refaie, A. F., Changeat, Q., Waldmann, I. P., & Tinetti, G. 2021, ApJ, 917, 37

    2021

  3. [3]

    F., Yachmenev, A., Tennyson, J., & Yurchenko, S

    Al-Refaie, A. F., Yachmenev, A., Tennyson, J., & Yurchenko, S. N. 2015, MN- RAS, 448, 1704

    2015

  4. [4]

    H., Alexander, D

    Allard, F., Hauschildt, P. H., Alexander, D. R., Tamanai, A., & Schweitzer, A. 2001, ApJ, 556, 357

    2001

  5. [5]

    2012, Philosophical Transactions of the Royal Society of London Series A, 370, 2765

    Allard, F., Homeier, D., & Freytag, B. 2012, Philosophical Transactions of the Royal Society of London Series A, 370, 2765

    2012

  6. [6]

    F., Spiegelman, F., Leininger, T., & Molliere, P

    Allard, N. F., Spiegelman, F., Leininger, T., & Molliere, P. 2019, A&A, 628, A120

    2019

  7. [7]

    2013, ApJ, 768, 121

    Apai, D., Radigan, J., Buenzli, E., et al. 2013, ApJ, 768, 121

    2013

  8. [8]

    Aringer, B., Kerschbaum, F., & Jörgensen, U. G. 2002, A&A, 395, 915

    2002

Show all 161 references
  1. [9]

    Auer, L. H. & Mihalas, D. 1970, MNRAS, 149, 65

    1970

  2. [10]

    2024, Detecting ongoing gas-to-solid nucleation on the ultra-hot planet W ASP-76 b, JWST Proposal

    Baeyens, R., Barat, S., Decin, L., et al. 2024, Detecting ongoing gas-to-solid nucleation on the ultra-hot planet W ASP-76 b, JWST Proposal. Cycle 3, ID. #6045

    2024

  3. [11]

    J., Strange, J

    Barber, R. J., Strange, J. K., Hill, C., et al. 2014, MNRAS, 437, 1828

    2014

  4. [12]

    J., Chiu, C., Golpayegani, S., et al

    Barton, E. J., Chiu, C., Golpayegani, S., et al. 2014, MNRAS, 442, 1821

    2014

  5. [13]

    1997, ApJ, 476, 199

    Begemann, B., Dorschner, J., Henning, T., et al. 1997, ApJ, 476, 199

    1997

  6. [14]

    2023, Clouds or Chemistry?: Pinpoint- ing the drivers of variability across the L/T transition via the benchmark L/T binary WISE 1049AB, JWST Proposal

    Biller, B., Chen, X., Crossfield, I., et al. 2023, Clouds or Chemistry?: Pinpoint- ing the drivers of variability across the L/T transition via the benchmark L/T binary WISE 1049AB, JWST Proposal. Cycle 2, ID. #2965

    2023

  7. [15]

    A., V os, J

    Biller, B. A., V os, J. M., Zhou, Y ., et al. 2024, arXiv e-prints, arXiv:2407.09194

    2024 arXiv

  8. [16]

    Bittner, D. M. & Bernath, P. F. 2018, ApJS, 235, 8

    2018

  9. [17]

    E., Lynas-Gray, A

    Blackwell, D. E., Lynas-Gray, A. E., & Smith, G. 1995, A&A, 296, 217

    1995

  10. [18]

    Bohren, C. F. & Huffman, D. R. 1983, Absorption and scattering of light by small particles

    1983

  11. [19]

    T., Gómez Martín, J

    Bromley, S. T., Gómez Martín, J. C., & Plane, J. M. C. 2016, Physical Chemistry Chemical Physics (Incorporating Faraday Transactions), 18, 26913

    2016

  12. [20]

    Brooke, J. S. A., Bernath, P. F., & Western, C. M. 2015, J. Chem. Phys., 143, 026101

    2015

  13. [21]

    Brooke, J. S. A., Bernath, P. F., Western, C. M., et al. 2016, J. Quant. Spectr. Rad. Transf., 168, 142

    2016

  14. [22]

    Bruggeman, D. A. G. 1935, Annalen der Physik, 416, 636

    1935

  15. [23]

    J., Marley, M

    Burgasser, A. J., Marley, M. S., Ackerman, A. S., et al. 2002, ApJ, 571, L151

    2002

  16. [24]

    K., Gonzales, E

    Burningham, B., Faherty, J. K., Gonzales, E. C., et al. 2021, MNRAS, 506, 1944

    2021

  17. [25]

    W., J., et al

    Burrows, A., Dulick, M., Bauschlicher, C. W., J., et al. 2005, ApJ, 624, 988

    2005

  18. [26]

    B., et al

    Burrows, A., Marley, M., Hubbard, W. B., et al. 1997, ApJ, 491, 856

    1997

  19. [27]

    2006, ApJ, 640, 1063

    Burrows, A., Sudarsky, D., & Hubeny, I. 2006, ApJ, 640, 1063

    2006

  20. [28]

    Cannon, C. J. 1973, ApJ, 185, 621

    1973

  21. [29]

    2000, ApJ, 542, 464

    Chabrier, G., Baraffe, I., Allard, F., & Hauschildt, P. 2000, ApJ, 542, 464

    2000

  22. [30]

    L., et al

    Charnay, B., Bézard, B., Baudino, J. L., et al. 2018, ApJ, 854, 172

    2018

  23. [31]

    L., Rocchetto, M., Yurchenko, S

    Chubb, K. L., Rocchetto, M., Yurchenko, S. N., et al. 2021, A&A, 646, A21

    2021

  24. [32]

    A., Yurchenko, S

    Coles, P. A., Yurchenko, S. N., & Tennyson, J. 2019, MNRAS, 490, 4638

    2019

  25. [33]

    S., Sudarsky, D., Milsom, J

    Cooper, C. S., Sudarsky, D., Milsom, J. A., Lunine, J. I., & Burrows, A. 2003, ApJ, 586, 1320

    2003

  26. [34]

    M., Lodi, L., & Tennyson, J

    Coppola, C. M., Lodi, L., & Tennyson, J. 2011, MNRAS, 415, 487

    2011

  27. [35]

    C., Marley, M

    Cushing, M. C., Marley, M. S., Saumon, D., et al. 2008, ApJ, 678, 1372

    2008

  28. [36]

    C., Rayner, J

    Cushing, M. C., Rayner, J. T., & Vacca, W. D. 2005, ApJ, 623, 1115

    2005

  29. [37]

    C., Roellig, T

    Cushing, M. C., Roellig, T. L., Marley, M. S., et al. 2006, ApJ, 648, 614

    2006

  30. [38]

    & Williams, D

    Dalgarno, A. & Williams, D. A. 1962, ApJ, 136, 690 de Regt, S., Gandhi, S., Snellen, I. A. G., et al. 2024, A&A, 688, A116

    1962

  31. [39]

    & Eriksson, K

    Decin, L. & Eriksson, K. 2007, A&A, 472, 1041

    2007

  32. [40]

    2003, A&A, 400, 709

    Decin, L., Vandenbussche, B., Waelkens, C., et al. 2003, A&A, 400, 709

    2003

  33. [41]

    Dominik, C., Sedlmayr, E., & Gail, H. P. 1993, A&A, 277, 578

    1993

  34. [42]

    Doughty, N. A. & Fraser, P. A. 1966, MNRAS, 132, 267

    1966

  35. [43]

    A., Fraser, P

    Doughty, N. A., Fraser, P. A., & McEachran, R. P. 1966, MNRAS, 132, 255

    1966

  36. [44]

    Doyle, R. O. 1968, ApJ, 153, 987

    1968

  37. [45]

    2024, Nature, 625, 51

    Dyrek, A., Min, M., Decin, L., et al. 2024, Nature, 625, 51

    2024

  38. [46]

    1964, Comptes Rendus Academie des Sciences (serie non specifiee), 258, 3189

    Feautrier, P. 1964, Comptes Rendus Academie des Sciences (serie non specifiee), 258, 3189

    1964

  39. [47]

    M., Bernath, P

    Fernando, A. M., Bernath, P. F., Hodges, J. N., & Masseron, T. 2018, J. Quant. Spectr. Rad. Transf., 217, 29

    2018

  40. [48]

    C., Rice, E

    Filippazzo, J. C., Rice, E. L., Faherty, J., et al. 2015, ApJ, 810, 158

    2015

  41. [49]

    S., Lustig-Yaeger, J., Fortney, J

    Freedman, R. S., Lustig-Yaeger, J., Fortney, J. J., et al. 2014, ApJS, 214, 25

    2014

  42. [50]

    S., Marley, M

    Freedman, R. S., Marley, M. S., & Lodders, K. 2008, ApJS, 174, 504

    2008

  43. [51]

    P., Keller, R., & Sedlmayr, E

    Gail, H. P., Keller, R., & Sedlmayr, E. 1984, A&A, 133, 320

    1984

  44. [52]

    Gail, H. P. & Sedlmayr, E. 1988, A&A, 206, 153

    1988

  45. [53]

    P., Wetzel, S., Pucci, A., & Tamanai, A

    Gail, H. P., Wetzel, S., Pucci, A., & Tamanai, A. 2013, A&A, 555, A119

    2013

  46. [54]

    & Benneke, B

    Gao, P. & Benneke, B. 2018, ApJ, 863, 165

    2018

  47. [55]

    S., & Ackerman, A

    Gao, P., Marley, M. S., & Ackerman, A. S. 2018, ApJ, 855, 86

    2018

  48. [56]

    R., Moran, S

    Gao, P., Wakeford, H. R., Moran, S. E., & Parmentier, V . 2021, Journal of Geo- physical Research (Planets), 126, e06655

    2021

  49. [57]

    R., Bergemann, M., Serenelli, A., et al

    Gent, M. R., Bergemann, M., Serenelli, A., et al. 2022, A&A, 658, A147

    2022

  50. [58]

    GharibNezhad, E., Shayesteh, A., & Bernath, P. F. 2013, MNRAS, 432, 2043

    2013

  51. [59]

    Gobrecht, D., Plane, J. M. C., Bromley, S. T., et al. 2022, A&A, 658, A167

    2022

  52. [60]

    N., Yurchenko, S

    Gorman, M. N., Yurchenko, S. N., & Tennyson, J. 2019, MNRAS, 490, 1652

    2019

  53. [61]

    K., Wakeford, H

    Grant, D., Lewis, N. K., Wakeford, H. R., et al. 2023, ApJ, 956, L32

    2023

  54. [62]

    A., Eriksson, K., & Nordlund, A

    Gustafsson, B., Bell, R. A., Eriksson, K., & Nordlund, A. 1975, A&A, 42, 407

    1975

  55. [63]

    2008, A&A, 486, 951

    Gustafsson, B., Edvardsson, B., Eriksson, K., et al. 2008, A&A, 486, 951

    2008

  56. [64]

    & Nissen, P

    Gustafsson, B. & Nissen, P. E. 1972, A&A, 19, 261

    1972

  57. [65]

    J., Gordon, I

    Hargreaves, R. J., Gordon, I. E., Rothman, L. S., et al. 2019, J. Quant. Spectr. Rad. Transf., 232, 35

    2019

  58. [66]

    Hauschildt, P. H. & Baron, E. 1999, Journal of Computational and Applied Math- ematics, 109, 41

    1999

  59. [67]

    2019, Annual Review of Earth and Planetary Sciences, 47, 583

    Helling, C. 2019, Annual Review of Earth and Planetary Sciences, 47, 583

    2019

  60. [68]

    & Fomins, A

    Helling, C. & Fomins, A. 2013, Philosophical Transactions of the Royal Society of London Series A, 371, 20110581

    2013

  61. [69]

    & Woitke, P

    Helling, C. & Woitke, P. 2006, A&A, 455, 325

    2006

  62. [70]

    2020, A&A, 641, A178

    Helling, Ch., Kawashima, Y ., Graham, V ., et al. 2020, A&A, 641, A178

    2020

  63. [71]

    2021, A&A, 649, A44

    Helling, Ch., Lewis, D., Samra, D., et al. 2021, A&A, 649, A44

    2021

  64. [72]

    2023, A&A, 671, A122

    Helling, Ch., Samra, D., Lewis, D., et al. 2023, A&A, 671, A122

    2023

  65. [73]

    1995, A&AS, 112, 143

    Henning, T., Begemann, B., Mutschke, H., & Dorschner, J. 1995, A&AS, 112, 143

    1995

  66. [74]

    & Mutschke, H

    Henning, T. & Mutschke, H. 1997, A&A, 327, 743 Jäger, C., Dorschner, J., Mutschke, H., Posch, T., & Henning, T. 2003, A&A, 408, 193

    1997

  67. [75]

    S., Chang, C., Sedlmayr, E., & Sülzle, D

    Jeong, K. S., Chang, C., Sedlmayr, E., & Sülzle, D. 2000, Journal of Physics B Atomic Molecular Physics, 33, 3417 Article number, page 18 of 27 Campos Estrada et al.: MSG cloudy substellar atmospheres

    2000

  68. [76]

    John, T. L. 1967, ApJ, 149, 449 Jørgensen, U. G. 1992, Rev. Mexicana Astron. Astrofis., 23, 195 Jørgensen, U. G., Amadio, F., Campos Estrada, B., et al. 2024, A&A, 690, A127 Jørgensen, U. G., Hammer, D., Borysow, A., & Falkesgaard, J. 2000, A&A, 361, 283 Jørgensen, U. G., John...

    1967

  69. [77]

    G., & Helling, C

    Juncher, D., Jørgensen, U. G., & Helling, C. 2017, A&A, 608, A70

    2017

  70. [78]

    Karzas, W. J. & Latter, R. 1961, ApJS, 6, 167

    1961

  71. [79]

    & Ikoma, M

    Kawashima, Y . & Ikoma, M. 2018, ApJ, 853, 7

    2018

  72. [80]

    Kirkpatrick, J. D. 2005, ARA&A, 43, 195

    2005

  73. [81]

    & Heng, K

    Kitzmann, D. & Heng, K. 2018, MNRAS, 475, 94

    2018

  74. [82]

    1995, Icarus, 114, 203

    Koike, C., Kaito, C., Yamamoto, T., et al. 1995, Icarus, 114, 203

    1995

  75. [83]

    2015, Journal of Physical Chemistry A, 119, 8944

    Lam, J., Amans, D., Dujardin, C., Ledoux, G., & Allouche, A.-R. 2015, Journal of Physical Chemistry A, 119, 8944

    2015

  76. [84]

    L., Gustafsson, B., Eriksson, K., & Hinkle, K

    Lambert, D. L., Gustafsson, B., Eriksson, K., & Hinkle, K. H. 1986, ApJS, 62, 373

    1986

  77. [85]

    P., Helling, C., & Decin, L

    Lecoq-Molinos, H., Gobrecht, D., Sindel, J. P., Helling, C., & Decin, L. 2024, arXiv e-prints, arXiv:2401.02784

    2024 arXiv

  78. [86]

    2016, A&A, 594, A48

    Lee, E., Dobbs-Dixon, I., Helling, C., Bognar, K., & Woitke, P. 2016, A&A, 594, A48

    2016

  79. [87]

    2015, A&A, 580, A12

    Lee, E., Helling, C., Dobbs-Dixon, I., & Juncher, D. 2015, A&A, 580, A12

    2015

  80. [88]

    Lee, E. K. H., Blecic, J., & Helling, C. 2018, A&A, 614, A126

    2018

  81. [89]

    E., Rothman, L

    Li, G., Gordon, I. E., Rothman, L. S., et al. 2015, ApJS, 216, 15

    2015

  82. [90]

    A., V os, J

    Liu, P., Biller, B. A., V os, J. M., et al. 2024, MNRAS, 527, 6624

    2024

  83. [91]

    1965, Physica, 31, 401

    Looyenga, H. 1965, Physica, 31, 401

    1965

  84. [92]

    G., Allard, F., & Hauschildt, P

    Ludwig, H. G., Allard, F., & Hauschildt, P. H. 2002, A&A, 395, 99

    2002

  85. [93]

    Luna, J. L. & Morley, C. V . 2021, ApJ, 920, 146

    2021

  86. [94]

    I., Hubbard, W

    Lunine, J. I., Hubbard, W. B., & Marley, M. S. 1986, ApJ, 310, 238

    1986

  87. [95]

    M., et al

    Malik, M., Kitzmann, D., Mendonça, J. M., et al. 2019, AJ, 157, 170

    2019

  88. [96]

    S., Saumon, D., Guillot, T., et al

    Marley, M. S., Saumon, D., Guillot, T., et al. 1996, Science, 272, 1919

    1996

  89. [97]

    S., Seager, S., Saumon, D., et al

    Marley, M. S., Seager, S., Saumon, D., et al. 2002, ApJ, 568, 335

    2002

  90. [98]

    2014, A&A, 571, A47

    Masseron, T., Plez, B., Van Eck, S., et al. 2014, A&A, 571, A47

    2014

  91. [99]

    K., Masseron, T., Hoeijmakers, H

    McKemmish, L. K., Masseron, T., Hoeijmakers, H. J., et al. 2019, MNRAS, 488, 2836

    2019

  92. [100]

    K., Yurchenko, S

    McKemmish, L. K., Yurchenko, S. N., & Tennyson, J. 2016, MNRAS, 463, 771

    2016

  93. [101]

    A., Heinze, A., Apai, D., et al

    Metchev, S. A., Heinze, A., Apai, D., et al. 2015, ApJ, 799, 154

    2015

  94. [102]

    1908, Annalen der Physik, 330, 377

    Mie, G. 1908, Annalen der Physik, 330, 377

    1908

  95. [103]

    1965, ApJS, 11, 184

    Mihalas, D. 1965, ApJS, 11, 184

    1965

  96. [104]

    1978, Stellar atmospheres

    Mihalas, D. 1978, Stellar atmospheres

    1978

  97. [105]

    E., Biller, B

    Miles, B. E., Biller, B. A., Patapis, P., et al. 2023, ApJ, 946, L6

    2023

  98. [106]

    Min, M., Dominik, C., & Waters, L. B. F. M. 2004, A&A, 413, L35 Mollière, P., Stolker, T., Lacour, S., et al. 2020, A&A, 640, A131

    2004

  99. [107]

    2022, Research Notes of the American Astronomical Society, 6, 248

    Morello, G., Gerber, J., Plez, B., et al. 2022, Research Notes of the American Astronomical Society, 6, 248

    2022

  100. [108]

    V ., Mukherjee, S., Marley, M

    Morley, C. V ., Mukherjee, S., Marley, M. S., et al. 2024, arXiv e-prints, arXiv:2402.00758

    2024 arXiv

  101. [109]

    E., Fortney, J

    Mukherjee, S., Batalha, N. E., Fortney, J. J., & Marley, M. S. 2023, ApJ, 942, 71

    2023

  102. [110]

    J., Morley, C

    Mukherjee, S., Fortney, J. J., Morley, C. V ., et al. 2024, ApJ, 963, 73

    2024

  103. [111]

    K., Fortney, J

    Nikolov, N., Sing, D. K., Fortney, J. J., et al. 2018, Nature, 557, 526

    2018

  104. [112]

    1974, A&A, 32, 407

    Nordlund, A. 1974, A&A, 32, 407

    1974

  105. [113]

    & Ferguson, F

    Nuth, Joseph A., I. & Ferguson, F. T. 2006, ApJ, 649, 1178

    2006

  106. [114]

    & Okuzumi, S

    Ohno, K. & Okuzumi, S. 2017, ApJ, 835, 261

    2017

  107. [115]

    Ormel, C. W. & Min, M. 2019, A&A, 622, A121

    2019

  108. [116]

    2022, MNRAS, 511, 5448

    Owens, A., Dooley, S., McLaughlin, L., et al. 2022, MNRAS, 511, 5448

    2022

  109. [117]

    Palik, E. D. 1985, Handbook of optical constants of solids

    1985

  110. [118]

    Palik, E. D. 1991, Handbook of optical constants of solids II

    1991

  111. [119]

    T., Yurchenko, S

    Patrascu, A. T., Yurchenko, S. N., & Tennyson, J. 2015, MNRAS, 449, 3613

    2015

  112. [120]

    J., Yurchenko, S

    Paulose, G., Barton, E. J., Yurchenko, S. N., & Tennyson, J. 2015, MNRAS, 454, 1931

    2015

  113. [121]

    2024, ApJ, 966, L11

    Petrus, S., Whiteford, N., Patapis, P., et al. 2024, ApJ, 966, L11

    2024

  114. [122]

    B., Hollenbach, D., Beckwith, S., et al

    Pollack, J. B., Hollenbach, D., Beckwith, S., et al. 1994, ApJ, 421, 615

    1994

  115. [123]

    L., Kyuberis, A

    Polyansky, O. L., Kyuberis, A. A., Zobov, N. F., et al. 2018, MNRAS, 480, 2597

    2018

  116. [124]

    M., Barrientes, J., Blome, C., et al

    Pontoppidan, K. M., Barrientes, J., Blome, C., et al. 2022, ApJ, 936, L14

    2022

  117. [125]

    2003, ApJS, 149, 437

    Posch, T., Kerschbaum, F., Fabian, D., et al. 2003, ApJS, 149, 437

    2003

  118. [126]

    2018, ApJ, 860, 18

    Powell, D., Zhang, X., Gao, P., & Parmentier, V . 2018, ApJ, 860, 18

    2018

  119. [127]

    2023, MNRAS, 524, 835

    Radica, M., Welbanks, L., Espinoza, N., et al. 2023, MNRAS, 524, 835

    2023

  120. [128]

    N., Tennyson, J., & Le Roy, R

    Rivlin, T., Lodi, L., Yurchenko, S. N., Tennyson, J., & Le Roy, R. J. 2015, MN- RAS, 451, 634

    2015

  121. [129]

    Rossow, W. B. 1978, Icarus, 36, 1

    1978

  122. [130]

    Rybicki, G. B. 1971, J. Quant. Spectr. Rad. Transf., 11, 589

    1971

  123. [131]

    L., Richter, M

    Ryde, N., Lambert, D. L., Richter, M. J., & Lacy, J. H. 2002, ApJ, 580, 447

    2002

  124. [132]

    L., et al

    Samra, D., Helling, Ch., Chubb, K. L., et al. 2023, A&A, 669, A142

    2023

  125. [133]

    & Marley, M

    Saumon, D. & Marley, M. S. 2008, ApJ, 689, 1327

    2008

  126. [134]

    Sharp, C. M. & Huebner, W. F. 1990, ApJS, 72, 417

    1990

  127. [135]

    P., Gobrecht, D., Helling, C., & Decin, L

    Sindel, J. P., Gobrecht, D., Helling, C., & Decin, L. 2022, A&A, 668, A35

    2022

  128. [136]

    2016, A&A, 593, A79

    Siqueira-Mello, C., Chiappini, C., Barbuy, B., et al. 2016, A&A, 593, A79

    2016

  129. [137]

    Somerville, W. B. 1964, ApJ, 139, 192

    1964

  130. [138]

    Somerville, W. B. 1965, ApJ, 141, 811

    1965

  131. [139]

    C., Leggett, S

    Stephens, D. C., Leggett, S. K., Cushing, M. C., et al. 2009, ApJ, 702, 154 Suárez, G. & Metchev, S. 2022, MNRAS, 513, 5701

    2009

  132. [140]

    & Showman, A

    Tan, X. & Showman, A. P. 2019, ApJ, 874, 111

    2019

  133. [141]

    2023, MNRAS, 524, 817

    Taylor, J., Radica, M., Welbanks, L., et al. 2023, MNRAS, 524, 817

    2023

  134. [142]

    B., Turco, R

    Toon, O. B., Turco, R. P., Hamill, P., Kiang, C. S., & Whitten, R. C. 1979, Journal of the Atmospheric Sciences, 36, 718

    1979

  135. [143]

    S., Chabrier, G., et al

    Tremblin, P., Amundsen, D. S., Chabrier, G., et al. 2016, ApJ, 817, L19

    2016

  136. [144]

    S., Mourier, P., et al

    Tremblin, P., Amundsen, D. S., Mourier, P., et al. 2015, ApJ, 804, L17

    2015

  137. [145]

    W., et al

    Tremblin, P., Padioleau, T., Phillips, M. W., et al. 2019, ApJ, 876, 144

    2019

  138. [146]

    1998, Journal of Physics Condensed Matter, 10, 3669

    Ueda, K., Yanagi, H., Noshiro, R., Hosono, H., & Kawazoe, H. 1998, Journal of Physics Condensed Matter, 10, 3669

    1998

  139. [147]

    S., Tennyson, J., Yurchenko, S

    Underwood, D. S., Tennyson, J., Yurchenko, S. N., et al. 2016, MNRAS, 459, 3890

    2016

  140. [148]

    K., Tennyson, J., & Yurchenko, S

    Upadhyay, A., Conway, E. K., Tennyson, J., & Yurchenko, S. N. 2018, MNRAS, 477, 1520 V os, J. M., Biller, B. A., Bonavita, M., et al. 2019, MNRAS, 483, 480 V os, J. M., Burningham, B., Faherty, J. K., et al. 2023, ApJ, 944, 138 V os, J. M., Faherty, J. K., Gagné, J., et al. 20...

    2018

  141. [149]

    Wende, S., Reiners, A., Seifahrt, A., & Bernath, P. F. 2010, A&A, 523, A58

    2010

  142. [150]

    Witte, S., Helling, C., Barman, T., Heidrich, N., & Hauschildt, P. H. 2011, A&A, 529, A44

    2011

  143. [151]

    Witte, S., Helling, C., & Hauschildt, P. H. 2009, A&A, 506, 1367

    2009

  144. [152]

    & Helling, C

    Woitke, P. & Helling, C. 2003, A&A, 399, 297

    2003

  145. [153]

    & Helling, C

    Woitke, P. & Helling, C. 2004, A&A, 414, 335

    2004

  146. [154]

    H., et al

    Woitke, P., Helling, C., Hunter, G. H., et al. 2018, A&A, 614, A1

    2018

  147. [155]

    & V oshchinnikov, N

    Wolf, S. & V oshchinnikov, N. V . 2004, Computer Physics Communications, 162, 113

    2004

  148. [156]

    & Bernath, P

    Yousefi, M. & Bernath, P. F. 2018, ApJS, 237, 8

    2018

  149. [157]

    F., Hodges, J., & Masseron, T

    Yousefi, M., Bernath, P. F., Hodges, J., & Masseron, T. 2018, J. Quant. Spectr. Rad. Transf., 217, 416

    2018

  150. [158]

    N., Amundsen, D

    Yurchenko, S. N., Amundsen, D. S., Tennyson, J., & Waldmann, I. P. 2017, A&A, 605, A95

    2017

  151. [159]

    N., Mellor, T

    Yurchenko, S. N., Mellor, T. M., Freedman, R. S., & Tennyson, J. 2020, MN- RAS, 496, 5282

    2020

  152. [160]

    N., Tennyson, J., Syme, A.-M., et al

    Yurchenko, S. N., Tennyson, J., Syme, A.-M., et al. 2022, MNRAS, 510, 903

    2022

  153. [161]

    2011, A&A, 526, A68 Article number, page 19 of 27 A&A proofs: manuscript no

    Zeidler, S., Posch, T., Mutschke, H., Richter, H., & Wehrhan, O. 2011, A&A, 526, A68 Article number, page 19 of 27 A&A proofs: manuscript no. main Appendix A: Continuum opacity sources and molecular & atomic line lists references Table A.1. Continuum opacity data sources. Ion ...

    1966

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.