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Improving 1D stellar atmosphere models with insights from multi-dimensional simulations II. 1D versus 3D hydrodynamically consistent model comparison for WR stars

T0 review · 2 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read One-dimensional hydrodynamically consistent atmosphere models reproduce the average 3D density structure of classical Wolf-Rayet star simulations, with mass-loss rates only about 0.2 dex higher; small mass or luminosity adjustments within…

desk verdict The paper is a useful, honest 1D-3D WR comparison, but the headline 0.2 dex agreement depends on an unquantified LTE closure in the 3D benchmark. read the letter →

arxiv 2505.16458 v1 pith:ARKBFLED submitted 2025-05-22 astro-ph.SR

G. González-Torà , A. A. C. Sander , N. Moens , J. O. Sundqvist , D. Debnath , L. Delbroek , J. Josiek , R. R. Lefever
show 3 more authors
C. Van der Sijpt O. Verhamme M. Bernini-Peron
This is my paper · ORCID
classification astro-ph.SR
keywords Wolf-Rayetstarsstellaratmospheresradiationhydrodynamicsmasslosswindsnon-LTElinetransferironopacitybump1D-3Dmodelcomparison
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 asks whether a one-dimensional, hydrodynamically consistent atmosphere model can capture what three-dimensional, time-dependent radiation-hydrodynamic simulations reveal about Wolf-Rayet winds launched by the hot iron opacity bump. Using the PoWR_HD branch, the authors show that the 1D models reproduce the time-averaged density stratification of the 3D simulations rather well, with mass-loss rates typically only about 0.2 dex higher. They further show that small adjustments in stellar mass (under 2%) or luminosity (under 0.1 dex), all within the scatter of the time-dependent 3D simulations, bring the mass-loss rates into agreement. The remaining systematic differences - 1D models more radially extended, higher terminal velocities, lower effective temperatures - are traced to methodological choices such as the Doppler velocity in the co-moving-frame transfer and the flux-mean opacity approximation in the 3D code. The upshot is that 1D hydrodynamically consistent models are a viable tool for quantitative spectral analysis of classical WR stars, with quantified uncertainty from the 3D dispersion.

What carries the argument

The central object is the hydrodynamically-consistent branch of the PoWR model atmosphere code (PoWR_HD), which integrates the stationary 1D equation of motion from the sonic (critical) point - using the radiative acceleration obtained from detailed non-LTE co-moving-frame radiative transfer - inwards and outwards, iterating the mass-loss rate and velocity field until flux consistency and conserved Rosseland optical depth are achieved. The comparison benchmark consists of density-weighted, time-averaged radial stratifications from the 3D box-in-a-star radiation-hydrodynamic simulations of Moens et al. (2022a), which use hybrid opacities (OPAL Rosseland mean near the core and CAK-like line forces with a Gayley line-strength cutoff in the supersonic regime). The paper also varies, within the 1D framework, the Doppler velocity used for the CMF opacity profiles, the turbulent pressure in the hydrodynamic solution, and the clumping density contrast, and computes synthetic UV and optical spectra to gauge the observational impact of the stratification differences.

What would settle it

Recompute the Γ3 simulation with the same 3D set-up but with a proper non-LTE treatment of the energy- and Planck-mean opacities (relaxing the flux-mean approximation), then compare the averaged density and temperature stratifications with the PoWR_HD 1D solution; if the density agreement degrades or the temperature discrepancy reverses, the claimed 1D-3D consistency is an artifact of the flux-mean assumption.

Watch

Extended reading notes

Core claim

The central discovery is that the averaged density profile of a 3D radiation-hydrodynamic simulation of a classical Wolf-Rayet star with a wind launched at the hot iron bump can be reproduced by a 1D stationary, spherically symmetric, non-LTE PoWR_HD model that solves the hydrodynamic equation of motion with the radiative acceleration computed from co-moving-frame radiative transfer. For the three models Γ2, Γ3, and Γ4 from Moens et al. (2022a), the 1D models match the 3D density structure at the same inner boundary, while predicting mass-loss rates about 0.2 dex higher; adjusting the stellar mass to about 10.2-11.3 M\odot or lowering the luminosity by 0.02-0.08 dex yields the same mass-loss rate as the 3D models. The authors interpret these adjustments as consistent with the mass-loss and luminosity dispersion of the time-dependent simulations. The 1D models launch the wind slightly further out and reach higher velocities through the hot iron bump, resulting in higher terminal velocities and lower effective temperatures; these differences propagate into synthetic spectra, most visibly in UV P-Cygni lines such as C IV 1550 Å.

Load-bearing premise

The whole comparison rests on the 3D simulations being trustworthy enough that their averaged density and temperature profiles are meaningful, despite their LTE-like flux-mean opacity approximation that forces gas and radiation temperatures to be equal.

Editorial extensions

If this is right

  • Hydrodynamically consistent 1D PoWR_HD models can be used for quantitative spectral analysis of classical WR stars, providing mass-loss rates, terminal velocities, and stellar-mass estimates with an uncertainty set by the 3D dispersion.
  • A mass adjustment of under 2% or a luminosity adjustment of under 0.1 dex reconciles the 1D and 3D mass-loss rates, meaning the 3D averages are compatible with 1D solutions within their inherent time variability.
  • For stars close to the Eddington limit, reducing the Doppler velocity from 100 km s^{-1} to 50 km s^{-1} improves agreement in mass-loss rate, effective temperature, and outer-wind velocity, although the optimal value is regime-dependent.
  • The failure of β-law models to reproduce the outer-wind deceleration seen in the 3D simulations confirms that hydrodynamically consistent 1D models are preferable for WR wind structure.
  • Models sharing the same transformed mass-loss rate produce similar spectra, extending the transformed-rate scaling to hydrodynamically consistent 1D models.

Reading between the lines

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

  • If the flux-mean approximation in the 3D models is responsible for the systematically higher optically-thin gas temperatures, then the 1D non-LTE temperature stratification may better represent the real outer wind, and the 3D temperature profiles could be recalibrated against the 1D solution.
  • The empirically found scaling ∂log Ṁ/∂log v_Dop ≈ 0.26 for WR models suggests that the Doppler velocity acts as a tunable broadening parameter that mimics the effect of unresolved microturbulence on the radiative force; this could be tested against 3D simulations with different velocity dispersion.
  • The failure to converge the lower-luminosity Γ1 model indicates that the current 1D turbulent-pressure treatment cannot capture the inflated, agitated atmosphere of hot stripped stars; extending PoWR_HD with a depth-dependent turbulent pressure may bridge the gap between classical WR stars and hot (sub)dwarfs.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

Summary. This paper compares 1D, stationary, non-LTE PoWR_HD models with the time- and density-averaged stratifications of three 3D radiation-hydrodynamic simulations of classical WR stars, the Γ2, Γ3, and Γ4 models of Moens et al. (2022a). In the PoWR_HD branch, the mass-loss rate and velocity field are iterated to satisfy the momentum equation (Eq. 4) using the CMF radiative acceleration, so both are predictions rather than inputs. Keeping the 3D stellar parameters (M = 10 M_sun, log(L/L_sun) = 5.47–5.74, Rc = 1 R_sun) and tuning the inner Rosseland optical depth to approximately match the 3D models, the authors find that the 1D density stratification closely tracks the averaged 3D density; that the 1D mass-loss rates are higher by 0.03–0.18 dex; that the 1D terminal velocities, and velocities at 6 R*, are higher; and that the 1D effective temperatures are lower by 0.14–0.22 dex. They then test modified inputs: fixing the 1D mass-loss rate to the 3D value requires either a mass increase (to 11.3 M_sun for Γ2 and Γ3, to 10.2 M_sun for Γ4) or a luminosity decrease (0.08 dex for Γ3, 0.02 dex for Γ4; no such solution was found for Γ2). Additional variations of the Doppler velocity, turbulent velocity, and clumping contrast are explored, and synthetic UV/optical spectra are computed for all variants, with the C IV 1550 Å P-Cygni profile identified as the main wind diagnostic.

Significance. If the central comparison is robust, this paper provides a valuable validation of 1D hydrodynamically consistent modeling for classical WR stars: a 1D stationary non-LTE code reproduces the averaged density and, to a lesser degree, the velocity structure of 3D time-dependent simulations at roughly the level of the 3D run-to-run dispersion (~0.2 dex in mass-loss rate). The comparison is non-trivial because the 1D mass-loss rates and terminal velocities are genuine outputs of the momentum equation (Eq. 4) and are not fitted to the 3D results; the luminosity and mass adjustments of Sect. 3.1.2 are therefore sensitivity tests rather than circular refits. The paper is transparent about the known weaknesses of both frameworks, including the flux-mean closure of the 3D models, the boundary spike in the 3D average velocity, the non-monotonic 1D velocity solution for Γ2, and the lack of a converged Γ1 model. The spectral synthesis section makes the model differences falsifiable through specific line diagnostics (C IV 1550 Å, N V 4604 Å, N III 4640 Å).

major comments (2)
  1. [Sect. 2.2 and 3.1.1; Table 1; Eq. (4)] The headline quantitative claim — that 1D PoWR_HD models overproduce the 3D mass-loss rates by at most 0.2 dex and that this is reconciled by physically small adjustments — rests on the 3D reference values, yet the paper does not assess whether the LTE-like closure of the 3D benchmark (energy and Planck mean opacities replaced by the flux mean, forcing T_gas = T_rad; Sect. 2.2 and 3.1.1) biases those reference mass-loss rates. The temperature bias is invoked to explain the 1D–3D temperature differences, but the gas temperature also enters the sound speed and the critical-point condition of Eq. (4), so a systematically distorted T_gas in the wind-launching region could shift the emergent 3D mass flux. Since Fig. 1 shows that the 1D and 3D temperature profiles agree reasonably well inside r ≈ 1.6 Rc, the authors may be able to argue that the launch region is largely insensitive to the closure, but that argument is not made and no quantitative bound is given. Please either locate the sonic point and estimate the implied uncertainty in the 3D mass-loss rate, quantify the sensitivity of the 3D mass flux to the closure with an explicit test, or rephrase the agreement as being relative to the published 3D benchmark with its stated approximation.
  2. [Conclusions vs. Table 2] The concluding statement that 'discrepancies can largely be mitigated by small adjustments of the mass (< 2%)' is inconsistent with Table 2, which lists required masses of 11.3 M_sun for both the Γ2 and Γ3 models — a 13% increase over the nominal 10 M_sun — while only Γ4 requires the roughly 2% change. The quantitative claim about the size of the mass adjustment should be corrected or made model-specific, since the magnitude of the adjustment is part of the paper's reconciliation argument.
minor comments (6)
  1. [Conclusions vs. Table 1] The conclusion that v(6 R*) is overestimated 'by up to ~400 km/s' is not supported by Table 1, which shows differences of 210, 600, and 500 km/s for Γ2, Γ3, and Γ4, respectively; the maximum is about 600 km/s.
  2. [Conclusions vs. Table 1] The claim that effective temperatures are lower 'by 0.2 dex (~30 kK)' holds only for Γ4; Table 1 shows differences of 0.14, 0.17, and 0.22 dex (about 19, 25, and 30 kK), so the range should be stated.
  3. [Sect. 3.1.3; Eq. (5); Fig. B.1] The scaling in Eq. (5) is derived from a small set of Γ3 test models (three Doppler-velocity values, Fig. B.1); the text should state this limited basis explicitly when presenting the scaling, since the next sentence contrasts it with the OB-star scaling of Björklund et al. (2021).
  4. [Various sections] Please fix the typos: 'the the hydrodynamically-consistent' (Sect. 2), 'Noteably' (Sect. 3.1.1), 'preform' (Sect. 2.2), and 'disussed' (Sect. 2.1).
  5. [Table 2 note; Fig. 3 caption] The Table 2 note 'All fundamental parameters are scaled at τ = 2/3' should read 'defined at' or 'evaluated at'; the Fig. 3 caption 'including a turbulence v_turb' would be clearer as 'including different turbulent velocities v_turb'.
  6. [Sect. 3.1.2] For the Γ2 case, Sect. 3.1.2 states that no solution with decreased luminosity could be found; a sentence explaining whether this reflects a physical limit (e.g., the reduced radiative acceleration no longer sustaining the assumed mass-loss rate) or a numerical convergence issue would help the reader interpret the difference from the Γ3 and Γ4 cases.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 1D mass-loss rates and wind velocities are outputs of the hydrodynamic equation driven by radiative acceleration, while the 3D simulations serve as external benchmarks.

full rationale

The paper's central comparison is not a derivation of the 3D results from 1D inputs or vice versa. The PoWR_HD mass-loss rates and terminal velocities are iteratively determined from the stationary hydrodynamic equation, Eq. (4), using radiative accelerations arad(r) from CMF radiative transfer; the paper states: 'Due to this additional iteration scheme, PoWRhd models are able to predict the Mdot and v(r) instead of having them as a free input.' These 1D outputs are therefore computed independently of the 3D benchmark values. The 3D models of Moens et al. (2022a) are time-dependent radiation-hydrodynamic simulations used as external comparison data, not as fitted inputs to the 1D code. The mass- and luminosity-adjusted models in Sect. 3.1.2 are explicitly sensitivity tests in which Mdot is fixed to the 3D value and L or M is varied; the paper presents them as checks of whether the required adjustments lie within the 3D dispersion ('The luminosity-scaled models thus give us an indication whether necessary L adjustments to reproduce the 3D mass-loss rate are within the observed 3D fluctuations'), not as predictions. The remaining discrepancies in temperature and effective temperature are attributed to acknowledged methodological differences, including the 3D LTE-like opacity closure, which is a validity concern about the benchmark rather than circular reasoning. Self-citations to Sander et al. (2017, 2023) and Moens et al. (2022a) support the methodology and provide the benchmark data, but no load-bearing step reduces to an unverified self-citation or to a fitted parameter renamed as a prediction. No equation in the paper is shown to be equivalent by construction to its own input. Accordingly, the circularity score is 0.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim rests mainly on domain assumptions inherited from the two codes, especially the 3D LTE benchmark and the averaging scheme. The free parameters listed are inputs varied in the sensitivity study; none of them introduces a new physical entity or a fit that purports to be a prediction.

free parameters (6)
  • Doppler velocity v_Dop = 100 km/s default; 50 and 20 km/s in test models
    Depth-independent CMF line-broadening parameter; varied in Sect. 3.1.3; the scaling in Eq. (5) quantifies its effect on mass loss.
  • Turbulent velocity v_turb = 0 to 106 km/s; 21 km/s default
    Adds turbulent pressure in Eq. (3); varied in Figs. 3-4; affects mass loss and terminal velocity modestly.
  • Clumping contrast D = 1 (smooth), 2, 6, 10
    Microclumping density contrast; higher D increases mass loss and terminal velocity and worsens 1D-3D agreement.
  • Stellar mass M_star (adjusted models) = 11.3 Msun (Gamma2, Gamma3), 10.2 Msun (Gamma4)
    Increased in Sect. 3.1.2 to force the 1D mass-loss rate to the 3D value; presented as within orbital-mass uncertainties.
  • Stellar luminosity L_star (adjusted models) = log L/Lsun = 5.56 (Gamma3), 5.72 (Gamma4)
    Decreased in Sect. 3.1.2 to match the 3D mass-loss rate; presented as within the 3D luminosity dispersion.
  • Inner Rosseland optical depth tau_max = ~30 (Gamma2), ~35 (Gamma3), ~40 (Gamma4)
    Tuned so that the 1D models have approximately the same total optical depth as the 3D models at the inner boundary (Sect. 3.1.1).
assumptions (5)
  • standard math The stationary 1D momentum equation (Eq. 4) with a critical point at the sonic point yields the correct wind solution once the radiative acceleration is known.
    Assumed in the PoWR_HD branch (Sander et al. 2017); used throughout Sect. 2.1.
  • domain assumption The hybrid opacity approach in the 3D models, using Rosseland OPAL opacities in the core and CAK-like line opacities in the supersonic regime, adequately describes WR wind driving.
    Adopted from Moens et al. (2022a) and Poniatowski et al. (2022), described in Sect. 2.2.
  • domain assumption The 3D models' approximation of using flux-mean opacities for the energy balance, forcing gas and radiation temperatures to be equal, is accurate enough for a benchmark stratification.
    Sect. 2.2 and Sect. 3.1.1; the paper itself identifies this as the likely cause of the 1D-3D temperature mismatch.
  • domain assumption Density-weighted radial averaging of the 3D simulations is the appropriate mapping for comparison with 1D spherically symmetric stationary models.
    Sect. 3.1.1; different averaging choices yield different mass-loss estimates.
  • domain assumption Convective energy transport is negligible in these models, carrying less than 10% of the luminosity at the innermost layers.
    Stated in Sect. 2.1 to justify purely radiative energy transport in the 1D models.

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Pith. "Pith review of Improving 1D stellar atmosphere models with insights from multi-dimensional simulations II. 1D versus 3D hydrodynamically consistent model comparison for WR stars." pith.science (2026). https://pith.science/paper/ARKBFLED

@misc{pith2026250516458,
  author       = {Pith},
  title        = {Pith review of: Improving 1D stellar atmosphere models with insights from multi-dimensional simulations II. 1D versus 3D hydrodynamically consistent model comparison for WR stars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ARKBFLED}},
  note         = {Machine review of arXiv:2505.16458}
}
abstract

Classical Wolf-Rayet (cWR) stars are evolved massive stars that have lost most of their H envelope and exhibit dense, extended atmospheres with strong, line-driven winds. Accurately modeling wind launching from optically thick layers remains a challenge. Two main approaches have advanced our understanding: 1D stationary atmosphere models with consistent hydrodynamics and time-dependent, multi-dimensional radiation-hydrodynamic simulations. Due to high computational demands, multi-dimensional models are limited in scope. Therefore, 1D hydrodynamically consistent models remain essential but must incorporate insights from 3D simulations. We compare averaged stratifications from recent multi-dimensional cWR models with 1D models computed using the hydrodynamically consistent PoWR$^{HD}$ code. We focus on winds driven by the hot iron opacity bump and explore how variations in 1D input parameters affect model outcomes. The 1D models reproduce the average 3D density structure well. While mass-loss rates are typically $\lesssim$0.2 dex higher in 1D models, small adjustments accounting for multi-dimensional dispersion reconcile the differences. 1D models tend to be more radially extended, with higher terminal velocities and lower effective temperatures. They reproduce the general velocity trends of 3D models but launch winds slightly further out and reach higher velocities during the hot iron bump. These differences also manifest in synthetic spectra computed from different 1D model approaches. Despite methodological variations, both 1D and averaged 3D models yield consistent stellar parameters when accounting for the variability seen in time-dependent simulations. For stars near the Eddington limit, reducing Doppler velocities in 1D models improves agreement in mass-loss rates, temperatures, and wind velocities. Matching temperature structures in optically thin layers remains an open challenge.

Figures

Figures reproduced from arXiv: 2505.16458 by the authors.

Figure 1
Figure 1. Profile comparison for the 3D averaged models (solid black) by Moens et al. (2022a) for the Γ2, Γ3 and Γ4 WR stars (from left to right) with 1D PoWRhd models using the same basic parameters (dashed-red). Upper panels: Wind velocity profiles. Middle panels: density profiles Lower panels: gas temperature profiles. dynamically-consistent models can provide a unique handle on the stellar mass. By determining the differe… view at source ↗
Figure 2
Figure 2. Profile comparison for the Γ2, Γ3 and Γ4 stars. Upper panels: Wind velocity profile, in solid black for the 3D averaged model of Moens et al. (2022a), dashed-red for the 1D PoWRhd using the same basic parameters, dotted for the PoWRhd changing the mass, dash-dotted for the PoWRhd changing the luminosity value with respect to Moens et al. (2022a), lighter-red for the PoWRhd with 3Dop = 50 km s−1 and solid-red for the… view at source ↗
Figure 3
Figure 3. Different PoWRhd models for Γ3 with 3Dop = 100 km s−1 (light color) and 3Dop = 50 km s−1 (dark color) including a turbulence 3turb with respect to the log M˙ /M⊙yr−1 , log Teff and 3∞. Sander et al. 2024, for a recent comparison), but in the limit of optically thin clumping with a void interclump medium, as used in this work, D is identical to fcl and the inverse of the volume filling factor [PITH_FULL_IMAGE:figure… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Radial velocity, density and gas temperature profiles for the av￾eraged 3D model from Moens et al. (2022a) in solid black and PoWRhd models for Γ3 3Dop = 100 km s−1 (light color) and 3Dop = 50 km s−1 (dark color) including different turbulence values 3turb. the gas and…
Figure 5
Figure 5. Figure 5: Different PoWRhd models for Γ3 including a turbulence 3turb = 21 km s−1 with respect to the log M˙ /M⊙yr−1 , log Teff and 3∞. 3.1.4. 1D β-law models [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Radial velocity, density and gas temperature profiles for the av￾eraged 3D model from Moens et al. (2022a) in solid black and PoWRhd models for Γ3 including different turbulence values 3turb. discuss in Sect. 3.2, the different temperature profile do not sig￾nificantly…
Figure 7
Figure 7. Figure 7: Computed UV spectra for the PoWR models. From up to down: the β-law approach (blue solid line), the PoWRhd with identical input as Moens et al. (2022a) (red dashed dotted), the PoWRhd with flexible mass (red dotted line) and the PoWRhd with flexible luminosity (red das…
Figure 9
Figure 9. Figure 9: Computed UV spectra for the PoWRhd Γ3 models including different 3turb values. 3800 4000 4200 4400 4600 4800 5000 wavelength (Å) 0 5 10 15 20 Normalized Flux 3 vturb = 0 kms 1 vturb = 21 kms 1 vturb = 35 kms 1 vturb = 53 kms 1 vturb = 71 kms 1 vturb = 88 kms 1 vturb = …
Figure 10
Figure 10. Figure 10: Same as [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Computed UV spectra for the PoWRhd Γ3 models including different density contrast values. 3800 4000 4200 4400 4600 4800 5000 wavelength (Å) 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 Normalized Flux D=1 3 D=2 D=6 D=10 [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: Same as [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]

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Reference graph

Works this paper leans on

48 extracted references · 29 canonical work pages · cited by 1 Pith paper

  1. [1]

    Abbott, D. C. & Conti, P. S. 1987, ARA&A, 25, 113

    work page 1987

  2. [2]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481 Björklund, R., Sundqvist, J. O., Puls, J., & Najarro, F. 2021, A&A, 648, A36

    work page 2009

  3. [3]

    I., Abbott, D

    Castor, J. I., Abbott, D. C., & Klein, R. I. 1975, ApJ, 195, 157

    1975

  4. [4]

    Conti, P. S. 1975, Memoires of the Societe Royale des Sciences de Liege, 9, 193 de Koter, A., Heap, S. R., & Hubeny, I. 1997, ApJ, 477, 792

    work page 1975

  5. [5]

    O., Moens, N., et al

    Debnath, D., Sundqvist, J. O., Moens, N., et al. 2024, A&A, 684, A177

    2024

  6. [6]

    2020, A&A, 641, A26

    Dsilva, K., Shenar, T., Sana, H., & Marchant, P. 2020, A&A, 641, A26

    work page 2020

  7. [7]

    Gayley, K. G. 1995, ApJ, 454, 410 González-Torà, G., Sander, A. A. C., Sundqvist, J. O., et al. 2025, A&A, 694, A269 Gräfener, G. & Hamann, W. R. 2005, A&A, 432, 633 Gräfener, G. & Hamann, W. R. 2008, A&A, 482, 945 Gräfener, G., Koesterke, L., & Hamann, W. R. 2002, A&A, 387, 244 Gräfener, G., Owocki, S. P., & Vink, J. S. 2012, A&A, 538, A40 Gräfener, G. &...

    work page 1995

  8. [8]

    & Noels, A

    Grevesse, N. & Noels, A. 1993, in Origin and Evolution of the Elements, ed. N. Prantzos, E. Vangioni-Flam, & M. Casse, 15–25

    work page 1993

Show all 48 references
  1. [9]

    Hamann, W. R. 1986, A&A, 160, 347

    1986

  2. [10]

    Hamann, W. R. & Gräfener, G. 2003, A&A, 410, 993

    2003

  3. [11]

    Hamann, W. R. & Gräfener, G. 2004, A&A, 427, 697

    2004

  4. [12]

    R., Gräfener, G., & Liermann, A

    Hamann, W. R., Gräfener, G., & Liermann, A. 2006, A&A, 457, 1015

    2006

  5. [13]

    R., Gräfener, G., Oskinova, L., & Liermann, A

    Hamann, W. R., Gräfener, G., Oskinova, L., & Liermann, A. 2008, in Astro- nomical Society of the Pacific Conference Series, V ol. 388, Mass Loss from Stars and the Evolution of Stellar Clusters, ed. A. de Koter, L. J. Smith, & L. B. F. M. Waters, 171

    2008

  6. [14]

    Hamann, W. R. & Koesterke, L. 1998, A&A, 335, 1003

    1998

  7. [15]

    R., Schmutz, W., & Wessolowski, U

    Hamann, W. R., Schmutz, W., & Wessolowski, U. 1988, A&A, 194, 190

    1988

  8. [16]

    Hillier, D. J. 1988, ApJ, 327, 822

    1988

  9. [17]

    Hillier, D. J. 2003, in Astronomical Society of the Pacific Conference Series, V ol. 288, Stellar Atmosphere Modeling, ed. I. Hubeny, D. Mihalas, & K. Werner, 199

    2003

  10. [18]

    J., Lanz, T., Heap, S

    Hillier, D. J., Lanz, T., Heap, S. R., et al. 2003, ApJ, 588, 1039

    2003

  11. [19]

    Hillier, D. J. & Miller, D. L. 1998, ApJ, 496, 407

    1998

  12. [20]

    Hillier, D. J. & Miller, D. L. 1999, ApJ, 519, 354

    1999

  13. [21]

    & Lanz, T

    Hubeny, I. & Lanz, T. 2003, in Astronomical Society of the Pacific Conference

    2003

  14. [22]

    Iglesias, C. A. & Rogers, F. J. 1996, ApJ, 464, 943 Kubát, J., Puls, J., & Pauldrach, A. W. A. 1999, A&A, 341, 587

    1996

  15. [23]

    R., Sander, A

    Lefever, R. R., Sander, A. A. C., Shenar, T., et al. 2023, MNRAS, 521, 1374

    2023

  16. [24]

    Moffat, A. F. J. 2015, in Wolf-Rayet Stars, ed. W.-R. Hamann, A. Sander, & H. Todt, 13–18

    2015

  17. [25]

    & Lamers, H

    Nugis, T. & Lamers, H. J. G. L. M. 2002, A&A, 389, 162 Paczy´nski, B. 1967, Acta Astron., 17, 355

    2002

  18. [26]

    Pauldrach, A., Puls, J., & Kudritzki, R. P. 1986, A&A, 164, 86

    1986

  19. [27]

    G., Kee, N

    Poniatowski, L. G., Kee, N. D., Sundqvist, J. O., et al. 2022, A&A, 667, A113

    2022

  20. [28]

    G., Sundqvist, J

    Poniatowski, L. G., Sundqvist, J. O., Kee, N. D., et al. 2021, A&A, 647, A151

    2021

  21. [29]

    1998, A&A, 334, 505

    Portinari, L., Chiosi, C., & Bressan, A. 1998, A&A, 334, 505

    1998

  22. [30]

    2008, in Massive Stars as Cosmic Engines, ed

    Puls, J. 2008, in Massive Stars as Cosmic Engines, ed. F. Bresolin, P. A. Crowther, & J. Puls, V ol. 250, 25–38

    2008

  23. [31]

    A., Venero, R., et al

    Puls, J., Urbaneja, M. A., Venero, R., et al. 2005, A&A, 435, 669

    2005

  24. [32]

    D., Lee, L., Schaefer, G., et al

    Richardson, N. D., Lee, L., Schaefer, G., et al. 2021, ApJ, 908, L3

    2021

  25. [33]

    2015, A&A, 577, A13

    Sander, A., Shenar, T., Hainich, R., et al. 2015, A&A, 577, A13

    2015

  26. [34]

    Sander, A., Todt, H., Hainich, R., & Hamann, W. R. 2014, A&A, 563, A89

    2014

  27. [35]

    Sander, A. A. C. 2015, PhD thesis, University of Potsdam, Germany

    2015

  28. [36]

    Sander, A. A. C. 2017, in The Lives and Death-Throes of Massive Stars, ed. J. J

    2017

  29. [37]

    Sander, A. A. C., Bouret, J. C., Bernini-Peron, M., et al. 2024, A&A, 689, A30

    2024

  30. [38]

    Sander, A. A. C., Hamann, W. R., Todt, H., Hainich, R., & Shenar, T. 2017, A&A, 603, A86

    2017

  31. [39]

    Sander, A. A. C., Lefever, R. R., Poniatowski, L. G., et al. 2023, A&A, 670, A83

    2023

  32. [40]

    Sander, A. A. C. & Vink, J. S. 2020, MNRAS, 499, 873

    2020

  33. [41]

    Sander, A. A. C., Vink, J. S., & Hamann, W. R. 2020, MNRAS, 491, 4406

    2020

  34. [42]

    R., & Wessolowski, U

    Schmutz, W., Hamann, W. R., & Wessolowski, U. 1989, A&A, 210, 236

    1989

  35. [43]

    2024, arXiv e-prints, arXiv:2410.04436

    Shenar, T. 2024, arXiv e-prints, arXiv:2410.04436

    2024 arXiv

  36. [44]

    2020, A&A, 639, L6

    Shenar, T., Bodensteiner, J., Abdul-Masih, M., et al. 2020, A&A, 639, L6

    2020

  37. [45]

    2014, ARA&A, 52, 487

    Smith, N. 2014, ARA&A, 52, 487

    2014

  38. [46]

    & Conti, P

    Smith, N. & Conti, P. S. 2008, ApJ, 679, 1467

    2008

  39. [47]

    O., Owocki, S

    Sundqvist, J. O., Owocki, S. P., & Puls, J. 2018, A&A, 611, A17 ud-Doula, A., Sundqvist, J. O., Narechania, N., et al. 2025, A&A, 693, A224 van der Hucht, K. A., Conti, P. S., Lundstrom, I., & Stenholm, B. 1981, Space Sci. Rev., 28, 227

    2018

  40. [48]

    2018, ApJS, 234, 30 Article number, page 12 of 16 G

    Xia, C., Teunissen, J., El Mellah, I., Chané, E., & Keppens, R. 2018, ApJS, 234, 30 Article number, page 12 of 16 G. González-Torà et al.: Improving 1D stellar atmosphere models with insights from multi-dimensional simulations Appendix A: Gas, radiative and flux temperatures 0...

    2022

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