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REVIEW 3 major objections 8 minor 72 references

Atmospheric Escape Rates of Planets in Stellar Tidal Fields from 3-D Hydrodynamic Simulations

T0 review · 3 major / 8 minor · reviewed 2026-07-31 · grok-4.5

Pith's one-line read A mixture of spherical and tidal-tail escape formulas, trained on 3D runs, beats 1D Parker models for planetary mass-loss rates.

desk verdict Solid 3D regime map of tidal vs thermal escape; the Mixture Model is a useful in-grid fit but its flashiest population corrections rest on unvalidated extrapolation of g(λ_p). read the letter →

arxiv 2607.24733 v2 pith:TDAEMJCI submitted 2026-07-27 astro-ph.EP

classification astro-ph.EP
keywords atmosphericescapephotoevaporationcore-poweredmasslossRochelobeParkerwind3Dhydrodynamicsexoplanettidaltails
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

Close-in exoplanets lose atmosphere through thermal winds, and almost every population study still uses one-dimensional Parker-type formulas that assume the flow is spherical. This paper runs a grid of three-dimensional hydrodynamic simulations of a Jupiter-sized planet in a solar-type star’s tidal field, varying how full the Roche lobe is and how hot the wind is. The runs show a clean morphological change: weak tides and hot winds stay nearly spherical, while strong tides and cooler winds funnel into two tails through the L1 and L2 points. Standard Parker winds only match the simulations in the weak-tide limit; even a tidally corrected 1D model gets the total mass-loss rate roughly right but cannot capture the angle-dependent structure. The authors therefore build a Mixture Model that interpolates between a spherical formula and a two-nozzle formula, calibrate it on the 3D grid, and show that it recovers the simulated rates across the whole parameter space better than existing 1D tools.

What carries the argument

The Mixture Model: a weighted geometric mean of a modified spherical Parker wind and a modified tidal two-tail (nozzle) rate, with the weight set by a sigmoid of the hydrodynamic escape parameter and the dimensionless L1 barrier height, calibrated directly on the 3D simulation grid.

What would settle it

Run the same 3D setup at a handful of points inside the grid with a realistic energy equation (photoionization heating, radiative cooling, multi-species chemistry) and check whether the Mixture Model still recovers the measured mass-loss rates to the same accuracy.

Watch

Extended reading notes

Core claim

Three-dimensional hydrodynamic simulations of thermally driven planetary outflows demonstrate that mass-loss morphology and rate transition smoothly from nearly isotropic Parker-like winds (weak tides, hot atmospheres) to anisotropic two-tailed streams through the Lagrange points (strong tides, cooler atmospheres). A calibrated Mixture Model that geometrically averages a modified spherical Parker rate and a modified L1/L2 nozzle rate reproduces the simulated mass-loss rates across the explored grid and outperforms both pure Parker and tidally corrected 1D models.

Load-bearing premise

The entire thermodynamic state of the atmosphere is collapsed to a single nearly isothermal sound speed with fixed surface density and no stellar wind, magnetic fields, or detailed heating and cooling.

Editorial extensions

If this is right

  • Most observed close-in planets lie where Parker and Mixture rates differ by less than 30 percent, so existing demographic calculations remain first-order safe.
  • A minority of strongly Roche-filling, tightly bound systems can have true mass-loss rates several times higher than pure Parker estimates.
  • A larger minority of hot, loosely bound small planets can have true rates far lower than pure Parker estimates, affecting radius-valley and desert models.
  • Mean radial density profiles from 1D tidal codes remain usable even when the flow is anisotropic; only the angular structure is lost.

Reading between the lines

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

  • Coupling the Mixture Model into long-term orbital and structural evolution codes would change predicted lifetimes most for the hottest, lowest-mass close-in planets and for near-Roche-lobe gas giants.
  • Transit spectroscopy of leading versus trailing tails could observationally flag the strong-tide regime where the Mixture weight approaches unity.
  • Extending the calibration grid below λ_p ≈ 2.5 and above ≈ 7.5 would immediately widen the model’s safe domain for super-Earths and cooler giants.
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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

3 major / 8 minor

Summary. The paper presents a suite of 45 three-dimensional hydrodynamic simulations (Athena++), on a grid of Roche-lobe-filling factor f_ϕ ∈ [0.01, 5] and hydrodynamic escape parameter λ_p ∈ {2.5, 5, 7.5}, of thermal atmospheric outflows from a Jupiter-mass planet in the corotating Roche potential of a solar-mass star. The authors document a smooth morphological transition from quasi-spherical, isotropic winds (weak tides, hot outflows) to anisotropic two-tail escape through the L1/L2 channels (strong tides, cool outflows), supported by density maps, Mach-number/sonic-surface maps, and angular mass-flux distributions on the Hill sphere (Figs. 1–3). They then compare measured mass-loss rates with five analytic models — Parker wind, an empirically corrected "Modified Spherical" model, an L1/L2 "Nozzle" model, a "Modified Tidal Two-Tail" model with a fitted L2 attenuation, and the p-winds 1D tidal model — and combine the spherical and tidal limits into a "Mixture Model" (a logistic-weighted geometric mean) calibrated on the same simulation grid. Finally, they apply the Mixture Model to the observed exoplanet population using sunset-catalog outflow temperatures, finding that the Parker wind agrees with the Mixture Model for ~84% of planets but can deviate strongly for tide-dominated tightly bound systems and for hot, loosely bound ones.

Significance. If the simulation results hold, this is a useful and timely contribution. Hydrodynamic escape rates remain uncertain at the order-of-magnitude level in population studies, and a systematic 3D benchmark isolating tidal effects is directly relevant to interpreting the radius valley, the hot-Neptune desert, and He 1083 nm / Lyα transit observations. Notable strengths: a 45-run grid with a resolution convergence test (Appendix A) and a surface-integral Ṁ diagnostic verified to be radius-independent over 0.2–0.5a; explicit, equation-level comparisons against Parker, modified-spherical, nozzle, modified-tail, and 1D tidal (p-winds) models; and an open-source implementation of the Mixture Model with the full mass-loss table archived on Zenodo, making the results reproducible and the model falsifiable against future simulations. The morphology diagnostics (spherical-to-two-tail transition, sonic-surface deformation, angular flux maps) are robust, non-circular outputs of the hydro runs and constitute, in my view, the paper's most durable contribution. The credibility of the population-level application in §5.1, however, depends on resolving the extrapolation and in-sample-validation issues

major comments (3)
  1. [§5.1, Fig. 6 (bottom); Eq. (13)] The most dramatic application result — the bottom panel of Fig. 6 and the statement in §5.1 that 'Ṁ_mix/Ṁ_PW reaches values as low as 10^-5, implying that the Parker wind model may overestimate escape rates by several orders of magnitude' — is generated entirely by extrapolating the empirical correction g(λ_p)=exp(−k_sph/λ_p²) with k_sph=2.47 below the calibrated range. The simulated grid spans only 2.5≤λ_p≤7.5 (Table 1), over which g varies modestly (0.67–0.96). For the small-λ_p, low-η planets in question, the fitted logit (β0=−7.00, β1=6.13, β2=−4.03) drives w→0, so Ṁ_mix→g(λ_p)Ṁ_PW; at λ_p≈0.5 this gives exp(−2.47/0.25)≈5×10^-5 — i.e., the headline correction is numerically identical to the value of an unvalidated exponential at λ_p values never simulated. Worse, for λ_p<2 the isothermal Parker sonic radius r_s=R_p(λ_p/2) (§3.2) lies inside the planet, so no transonic Parker solution
  2. [§4.1–4.4, Fig. 4, Table 2] The central claim that the Mixture Model 'accurately predicts mass-loss rates across the parameter space explored and outperforms 1D model with tidal corrections' (Abstract; §4.4; Fig. 4) is established entirely in-sample. Five free parameters (k_sph, k_tail, β0, β1, β2) are least-squares fit to the same 45 Ṁ_sim values listed in Table 1 and then plotted against those same values in Fig. 4. With only three λ_p rows and smooth monotonic trends in f_ϕ, a five-parameter smooth interpolant is nearly guaranteed to agree, so Fig. 4 is a demonstration of fitting, not of predictive performance. A minimal fix that would substantially strengthen the paper: a hold-out test, e.g., calibrate on two of the three λ_p rows (or a subset of f_ϕ values) and predict the withheld runs. At minimum the authors should report per-point residuals, soften 'accurately predicts'/'outperforms' to reflect in-sample ca
  3. [§4.1, Eq. (13); §4.2, Eq. (17)] The functional form g(λ_p)=exp(−k_sph/λ_p²) is introduced without justification beyond the qualitative statement that the sonic point lies close to the surface at small λ_p. Within the grid, k_sph=2.47 is constrained almost entirely by the λ_p=2.5 row: at λ_p=5 and 7.5 the unmodified PW already under- or matches-predicts (Table 1), so the λ_p-dependence of g is essentially unconstrained by the data — yet it is this unconstrained λ_p-dependence that dominates the low-λ_p extrapolation behind Major Comment 1. The same concern applies, more mildly, to h(λ_p)=exp(−k_tail/Ro²): the claimed Ro^-2 scaling of the L2 suppression is motivated by a physical argument (§4.2) but the functional form is one of many consistent with the grid. Please report the fit residuals as a function of λ_p for both corrections, and either demonstrate that alternative forms (e.g., g constant, g∝exp(−k/λ_p)) are disfa
minor comments (8)
  1. [Table 1] First data row: for (f_ϕ=0.010, λ_p=2.50), Ṁ_sim is listed as 1.00×10^-4 while the adjacent rows are ~9.9×10^-4 and Ṁ_mix=9.86×10^-4 exceeds it by an order of magnitude. This appears to be a typo for 1.00×10^-3; since Table 1 is the benchmark dataset (and is distributed on Zenodo), please verify.
  2. [§5.2 / Fig. 7 caption] The text twice refers to 'Figure 3 (blue curve)' and 'Figure 3, left' where Figure 7 is clearly meant; the Fig. 7 caption also says 'shown in Figure 3 (blue curve)'. Please correct the cross-references.
  3. [§5.1] In computing c_s=√(γk_BT/μm_p) for observed planets, γ=5/3 is adopted, whereas the simulations use γ=1.01. The mapping of a sunbather/sunset temperature onto an effective λ_p is therefore convention-dependent at the factor-of-~1.6 level in c_s², which shifts planets appreciably in λ_p. The qualitative caveat is noted, but a sentence quantifying the sensitivity (or showing the population statistics of §5.1 for both conventions) would be useful.
  4. [Appendix A / Fig. 8] The convergence test is shown for a single (unstated) model. Since the smallest effective planet is resolved by only ~11 zones across its diameter, please state which (f_ϕ, λ_p) case is tested and, ideally, show convergence for the most extreme case (smallest R_eff, e.g., f_ϕ=0.01, λ_p=7.5), where resolution effects should be strongest.
  5. [Fig. 4] Fig. 4 would benefit from a quantitative summary (RMS and maximum log-space residual per model), particularly to substantiate the Table 2 entry 'Matches simulations' for the Mixture Model relative to the ~1.4–1.6× systematics quoted for the 1D Tidal Model.
  6. [§4.2, Eq. (15)] The nozzle curvature values (ϕ^1_yy=3.4538, etc.) are specific to q=M_p/M_⋆=10^-3; a brief note of how they scale with mass ratio (or a pointer to an analytic formula) would help users of the released code apply the model to other systems.
  7. [§4.4 / §5.1] Uncertainties are reported for β0, β1, β2 but are not propagated into the population-level ratios Ṁ_mix/Ṁ_PW in Figs. 5–6. Even an indicative uncertainty band on the ratio for a few representative planets would clarify which of the deviant systems are significant.
  8. [Abstract (typography)] The Abstract phrase 'outperforms 1D model with tidal corrections' needs an article ('the 1D model') and, per Major Comment 2, an in-sample qualification.

Circularity Check

3 steps flagged · score 5.0 of 10

Mixture Model ‘predictions’ and outperformance vs 1D are in-sample fits of g, h, and mixing weights to the same 45 ˙M_sim values used as the benchmark; morphology results are independent.

  1. fitted input called prediction [§4.1 Eqs. 12–13; k_sph calibration]
    "To account for this, we introduce an empirical correction factor, g(λ_p) to Eq. 12 and define a Modified Spherical Model... where we adopt g(λ_p)=exp(−k_sph/λ_p²). Here k_sph is a constant calibrated by fitting ˙M_sph to the simulation obtained mass-loss rates in the regime where the flow remains quasi-spherical. We find k_sph=2.47."

    k_sph is fit directly to ˙M_sim in the quasi-spherical regime; ˙M_sph is then compared to those same simulations as if it were an independent mass-loss prediction. The correction that makes the spherical model ‘work’ is forced by the benchmark it is scored against.

  2. fitted input called prediction [§4.2 Eqs. 16–17; k_tail calibration]
    "Motivated by this scaling, we introduce an attenuation factor, h(λ_p) that acts only on the L_2 nozzle term... h(λ_p)=exp(−k_tail/Ro²), where k_tail is a constant calibration parameter, obtained by fitting the ˙M_tail to the mass-loss rates measured in the simulations, restricting the fit to the regime where the outflow exhibits a clear tidal-tail-like morphology. We obtain k_tail=0.75."

    Same pattern as g: k_tail is fit to ˙M_sim in the two-tail regime, then ˙M_tail is presented as reproducing those simulations. The L2 suppression that ‘matches’ 3D rates is the fitted attenuation, not an a-priori prediction.

1 more flagged steps
  1. fitted input called prediction [§4.4 Eq. 20; β fit; Abstract / Fig. 4 claim]
    "We therefore define the global mass-loss rate as a weighted geometric mean of the two models: ˙M_mix=˙M_sph^{1−w} ˙M_tail^w=... We determine {β_i} by performing a least-squares fit in log space to the simulation results. The best-fit coefficients are β_0=−7.00±0.69, β_1=6.13±0.59, and β_2=−4.03±0.54. The Mixture Model... shows excellent agreement across the full parameter space and also improves upon the predictions from the 1D Tidal Model."

    Five free parameters (k_sph, k_tail, β0–β2) are fit to the 45 ˙M_sim values; ˙M_mix is then said to ‘accurately predict’ and ‘outperform’ 1D models on that same grid (Abstract, Fig. 4, Table 2). With more degrees of freedom tuned to the benchmark, superior in-sample agreement is statistically expected and is not an out-of-sample prediction.

full rationale

The 3D morphological and kinematic results (spherical vs two-tail transition, sonic-surface anisotropy, angular mass-flux maps) are direct simulation outputs and are not circular. Circularity appears only in the mass-loss modeling chain of §4. The Modified Spherical and Modified Tidal Two-Tail models introduce empirical factors g(λ_p)=exp(−k_sph/λ_p²) and h(λ_p)=exp(−k_tail/Ro²) whose constants k_sph=2.47 and k_tail=0.75 are least-squares fit to ˙M_sim in selected regimes; the Mixture Model then adds three more free coefficients (β0, β1, β2) fit in log space to the full simulation grid, and defines ˙M_mix as a weighted geometric mean of those calibrated limits. Fig. 4 and the Abstract/§4.4 claim that this model ‘accurately predicts’ mass-loss rates and ‘outperforms’ the 1D Tidal Model are evaluations on the same 45 runs used for calibration—i.e., a multi-parameter fit scored on its training set. That is partial circularity (fitted input called prediction), not definitional identity: the spherical Parker and nozzle building blocks have independent physical content, and the paper labels the construction as calibrated. Self-citations to MacLeod et al. supply methodology, not a load-bearing uniqueness theorem. Extrapolation of g(λ_p) below λ_p=2.5 in the population application is an overclaim/validation gap, not an additional circular reduction.

Assumptions & free parameters 5 free parameters · 6 assumptions · 1 invented entities

The load-bearing physics is standard inviscid hydro in the Roche potential plus several modeling choices that freeze thermodynamics and omit stellar wind/MHD. The Mixture Model’s claimed accuracy rests on five numbers fit to the authors’ own ˙M_sim grid plus empirical correction forms chosen by hand. No new physical entities are postulated; the ‘mixture weight’ is an interpolating construct, not an independent object.

free parameters (5)
  • k_sph = 2.47
    Empirical factor in g(λ_p)=exp(-k_sph/λ_p²) multiplying the Parker rate in the Modified Spherical Model; fit to quasi-spherical simulation rates.
  • k_tail = 0.75
    Empirical attenuation of the L2 nozzle via h(λ_p)=exp(-k_tail/Ro²); fit in the clear tidal-tail regime.
  • β0, β1, β2 (mixture logit coefficients) = β0=-7.00±0.69, β1=6.13±0.59, β2=-4.03±0.54
    Coefficients of z=β0+β1 ln λ_p+β2 ln(1+η) that set the sigmoid weight w between spherical and tail limits; least-squares fit in log space to all simulation ˙M.
  • γ (adiabatic index) = 1.01
    Fixed to 1.01 to force nearly isothermal behavior along adiabats; not derived from microphysics.
  • Surface density normalization ρ_s = 1 (code units)
    Set to M/a³=1 without loss of generality because gas self-gravity is neglected; absolute ˙M scale is set by this choice times the thermal structure.
assumptions (6)
  • domain assumption Inviscid compressible Euler equations in a corotating frame with point-mass star+planet gravity, centrifugal, and Coriolis terms adequately describe the escape flow.
    Stated in §2.1.1 Eqs. (1)–(2); MHD and viscosity are explicitly dropped.
  • domain assumption A single sound speed (via λ_p) captures the thermal state; microphysical photoheating, cooling, chemistry, and ionization can be ignored for rate and morphology trends.
    §1–2.1.2: authors remain ‘agnostic to the underlying heating mechanism’ and parametrize only c_s.
  • domain assumption No stellar wind or external ambient medium; diode outer boundaries; planet surface fixed on a Φ_eff isosurface with zero velocity in the corotating frame.
    §2.1–2.2; contrasts with MacLeod et al. 2025 bubble/stream setup that includes stellar wind.
  • domain assumption Escape remains collisional hydrodynamic (mean free path ≪ R_p); Jeans escape is outside scope.
    §2.1.2.
  • standard math Mass-loss rate is the steady surface integral of ρ v through a sphere ~0.2–0.5 a around the planet.
    §2.3 Eq. (10); standard flux diagnostic.
  • ad hoc to paper Mixture rate is a geometric mean ˙M_mix=˙M_sph^(1-w) ˙M_tail^w with logistic w(λ_p,η), rather than another interpolant.
    §4.4 Eq. (20); functional form motivated by multiplicative area/barrier changes but chosen and fit by the authors.
invented entities (1)
  • Mixture Model morphology weight w(λ_p, η)
    purpose: Scalar interpolant between Modified Spherical and Modified Tidal Two-Tail mass-loss formulas.
    Not a new physical field; an empirical construct defined so w→0 isotropic and w→1 two-tail, with η=f_ϕ λ_Hill. independent_evidence false because w is only constrained by fitting this paper’s simulations.

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Cite this review

Pith. "Pith review of Atmospheric Escape Rates of Planets in Stellar Tidal Fields from 3-D Hydrodynamic Simulations." pith.science (2026). https://pith.science/paper/TDAEMJCI

@misc{pith2026260724733,
  author       = {Pith},
  title        = {Pith review of: Atmospheric Escape Rates of Planets in Stellar Tidal Fields from 3-D Hydrodynamic Simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TDAEMJCI}},
  note         = {Machine review of arXiv:2607.24733}
}
read the original abstract

Thermally driven atmospheric escape, including photo-evaporation and core-powered mass-loss, plays a key role in shaping the evolution of close-in exoplanets, yet most current models rely on simplified one-dimensional descriptions of atmospheric escape. In this work, we perform 3D hydrodynamic simulations of atmospheric outflows from a Jupiter-sized planet embedded in the gravitational potential of a solar-type host star, and compare these results with 1D models to identify the regimes where they perform well and where they break down. We explore a range of configurations by varying the degree of Roche-lobe filling and the thermal state of the outflow. We find that systems with weak tidal influence and high-temperature winds produce nearly spherical and isotropic outflows, whereas more Roche-lobe-filling and cooler winds develop strong anisotropy and form two-tailed structures. We show that the commonly used 1D Parker wind model performs well only in the weak-tides regime, while including tidal corrections yields reasonable estimates of mass-loss rates and captures the mean radial density profile across all regimes, but fails to reproduce the intrinsically three-dimensional, angle-dependent nature of the flow as the outflow transitions from spherical to tidally structured tails. Motivated by these results, we develop a physically informed Mixture Model, calibrated using our 3D simulations, that accurately predicts mass-loss rates across the parameter space explored and outperforms 1D model with tidal corrections.

Figures

Figures reproduced from arXiv: 2607.24733 by the authors.

Figure 1
Figure 1. Wind morphology in six representative models spanning (fϕ, λp). Shown are slices of the gas density in the star–planet equatorial plane, normalized by the total mass-loss rate, for three values of the λp (columns) and two degrees of Roche-lobe filling, fϕ (rows). The top row (fϕ = 0.01) corresponds to a more Roche-lobe-filling configuration, while the bottom row (fϕ = 5.00) shows a planet deeper within its Roche lob… view at source ↗
Figure 2
Figure 2. Star-planet equatorial plane slices for the same six models shown in [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Angular distribution of the normalized radial mass flux on the Hill surface for the same six models shown in Figures 1 & 2. Each panel shows a latitude–longitude map of Z(θ, ϕ) ≡ Fr/F¯, evaluated on the Hill surface. Longitude, ϕ is measured in the orbital plane; the L1 and L2 points lie at latitude 0◦ , at ϕ = π, and ϕ = 0 (or 2π), respectively. frame) are overplotted as a quiver map. The Mrot = 1 contour (in black… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Figure comparing the mass-loss rates predicted by the analytic models described in §4 with those measured from our 3D hydrodynamic simulations, which are shown by the grey markers. thermal pressure, passes through a sonic point, and ap￾proaches a steady radial wind wit…
Figure 5
Figure 5. Figure 5: Ratio of Hill radius to planetary radius charac￾terizing the degree of Roche-lobe filling vs. the planetary gravitational potential, correlated to how deeply the atmo￾sphere is bound within the planet’s potential well. Colored points represent exoplanets with measured …
Figure 6
Figure 6. Figure 6: Observed exoplanets in P-Rp space and col￾or-coded by parameters connecting them to our simula￾tions. The top panel shows fϕ, which traces the degree of Roche-lobe filling; the middle panel shows the inferred λp, which reflects the combined effect of outflow tempera￾tu…
Figure 7
Figure 7. Figure 7: Radial density profiles of the outflowing atmosphere for representative simulated planets in the weak-tide, hot-wind regime (Left), an intermediate case (Middle), and the strong-tide, cooler-wind regime (Right). The densities are normalized by the planetary surface den…
Figure 8
Figure 8. Figure 8: Resolution test for mass-loss rates from 3D hydrodynamic simulations. The points show M˙ as a function of the smallest zone size, ∆x in the refined region for resolutions N = 963 , 1283 , 1923 , and 2563 . The weak dependence of M˙ on ∆x indicates that the inferred mas…

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Works this paper leans on

72 extracted references · 15 canonical work pages

  1. [1]

    L., Chen, X., Ciardi, D., et al

    Akeson, R. L., Chen, X., Ciardi, D., et al. 2013, PASP, 125, 989, doi: 10.1086/672273

  2. [2]

    2025, Nature Communications, 16, 10822, doi: 10.1038/s41467-025-66628-5

    Allart, R., Coulombe, L.-P., Carteret, Y., et al. 2025, Nature Communications, 16, 10822, doi: 10.1038/s41467-025-66628-5

  3. [3]

    2013, A&A, 557, A124, doi: 10.1051/0004-6361/201321551

    Bourrier, V., & Lecavelier des Etangs, A. 2013, A&A, 557, A124, doi: 10.1051/0004-6361/201321551

  4. [4]

    I., Murray-Clay, R., McCann, J

    Broome, M. I., Murray-Clay, R., McCann, J. R., & Owen, J. E. 2025, ApJ, 995, 198, doi: 10.3847/1538-4357/ae14f4

  5. [5]

    2021, A&A, 655, A30, doi: 10.1051/0004-6361/202141497

    Caldiroli, A., Haardt, F., Gallo, E., et al. 2021, A&A, 655, A30, doi: 10.1051/0004-6361/202141497

  6. [6]

    2022, A&A, 663, A122, doi: 10.1051/0004-6361/202142763

    Caldiroli, A., Haardt, F., Gallo, E., et al. 2022, A&A, 663, A122, doi: 10.1051/0004-6361/202142763

  7. [7]

    2017, MNRAS, 466, 2458, doi: 10.1093/mnras/stw3307

    Carroll-Nellenback, J., Frank, A., Liu, B., et al. 2017, MNRAS, 466, 2458, doi: 10.1093/mnras/stw3307

  8. [8]

    2016, ApJ, 820, 3, doi: 10.3847/0004-637X/820/1/3

    Christie, D., Arras, P., & Li, Z.-Y. 2016, ApJ, 820, 3, doi: 10.3847/0004-637X/820/1/3

Show all 72 references
  1. [9]

    J., Kashyap, V

    Cohen, O., Drake, J. J., Kashyap, V. L., et al. 2009, ApJL, 704, L85, doi: 10.1088/0004-637X/704/2/L85 Hydrodynamic Atmospheric Escape in Planets19

  2. [10]

    2024, A&A, 692, A230, doi: 10.1051/0004-6361/202451003

    Czesla, S., Nail, F., Lavail, A., et al. 2024, A&A, 692, A230, doi: 10.1051/0004-6361/202451003

  3. [11]

    2019, MNRAS, 483, 1481, doi: 10.1093/mnras/sty3212 Dos Santos, L

    Debrecht, A., Carroll-Nellenback, J., Frank, A., et al. 2019, MNRAS, 483, 1481, doi: 10.1093/mnras/sty3212 Dos Santos, L. A. 2023, in IAU Symposium, Vol. 370, Winds of Stars and Exoplanets, ed. A. A. Vidotto, L. Fossati, & J. S. Vink, 56–71, doi: 10.1017/S1743921322004239 Dos ...

  4. [12]

    V., Kulikov, Y

    Erkaev, N. V., Kulikov, Y. N., Lammer, H., et al. 2007, A&A, 472, 329, doi: 10.1051/0004-6361:20066929

  5. [13]

    V., Lammer, H., Odert, P., et al

    Erkaev, N. V., Lammer, H., Odert, P., et al. 2016, MNRAS, 460, 1300, doi: 10.1093/mnras/stw935

  6. [14]

    R., Haswell, C

    Fossati, L., Ayres, T. R., Haswell, C. A., et al. 2013, ApJL, 766, L20, doi: 10.1088/2041-8205/766/2/L20

  7. [15]

    J., Petigura, E

    Fulton, B. J., Petigura, E. A., Howard, A. W., et al. 2017, AJ, 154, 109, doi: 10.3847/1538-3881/aa80eb Garc ´ ıa Mu˜ noz, A. 2007, Planet. Space Sci., 55, 1426, doi: 10.1016/j.pss.2007.03.007

  8. [16]

    E., & Sari, R

    Ginzburg, S., Schlichting, H. E., & Sari, R. 2018, MNRAS, 476, 759, doi: 10.1093/mnras/sty290

  9. [17]

    2020, Journal of Geophysical Research (Space Physics), 125, e27639, doi: 10.1029/2019JA027639

    Gronoff, G., Arras, P., Baraka, S., et al. 2020, Journal of Geophysical Research (Space Physics), 125, e27639, doi: 10.1029/2019JA027639

  10. [18]

    V., Luna, J., et al

    Gully-Santiago, M., Morley, C. V., Luna, J., et al. 2024, AJ, 167, 142, doi: 10.3847/1538-3881/ad1ee8

  11. [19]

    2018, A&A, 614, L3, doi: 10.1051/0004-6361/201832934

    Gunell, H., Maggiolo, R., Nilsson, H., et al. 2018, A&A, 614, L3, doi: 10.1051/0004-6361/201832934

  12. [20]

    Gupta, A., & Schlichting, H. E. 2019, MNRAS, 487, 24, doi: 10.1093/mnras/stz1230

  13. [21]

    2026, ApJ, 997, 139, doi: 10.3847/1538-4357/adfb75

    Hallatt, T., & Millholland, S. 2026, ApJ, 997, 139, doi: 10.3847/1538-4357/adfb75

  14. [22]

    2023, ApJ, 951, 123, doi: 10.3847/1538-4357/accd5e

    Huang, C., Koskinen, T., Lavvas, P., & Fossati, L. 2023, ApJ, 951, 123, doi: 10.3847/1538-4357/accd5e

  15. [23]

    2017, ApJ, 835, 145, doi: 10.3847/1538-4357/835/2/145

    Jackson, B., Arras, P., Penev, K., Peacock, S., & Marchant, P. 2017, ApJ, 835, 145, doi: 10.3847/1538-4357/835/2/145

  16. [24]

    M.-R., & Knutson, H

    Kempton, E. M.-R., & Knutson, H. A. 2024, Reviews in Mineralogy and Geochemistry, 90, 411, doi: 10.2138/rmg.2024.90.12

  17. [25]

    T., Lavvas, P., Huang, C., et al

    Koskinen, T. T., Lavvas, P., Huang, C., et al. 2022, ApJ, 929, 52, doi: 10.3847/1538-4357/ac4f45

  18. [26]

    Kubyshkina, D., Fossati, L., & Erkaev, N. V. 2024, A&A, 684, A26, doi: 10.1051/0004-6361/202347837

  19. [27]

    V., et al

    Kubyshkina, D., Fossati, L., Erkaev, N. V., et al. 2018, A&A, 619, A151, doi: 10.1051/0004-6361/201833737

  20. [28]

    I., & Fossati, L

    Kubyshkina, D. I., & Fossati, L. 2021, Research Notes of the American Astronomical Society, 5, 74, doi: 10.3847/2515-5172/abf498

  21. [29]

    Lamers, H. J. G. L. M., & Cassinelli, J. P. 1999, Introduction to Stellar Winds

  22. [30]

    2003, ApJL, 598, L121, doi: 10.1086/380815 Lecavelier Des Etangs, A

    Lammer, H., Selsis, F., Ribas, I., et al. 2003, ApJL, 598, L121, doi: 10.1086/380815 Lecavelier Des Etangs, A. 2007, A&A, 461, 1185, doi: 10.1051/0004-6361:20065014

  23. [31]

    2025, A&A, 698, A112, doi: 10.1051/0004-6361/202452431

    Linssen, D., Oklopˇ ci´ c, A., & MacLeod, M. 2025, A&A, 698, A112, doi: 10.1051/0004-6361/202452431

  24. [32]

    2024, A&A, 688, A43, doi: 10.1051/0004-6361/202450240

    Linssen, D., Shih, J., MacLeod, M., & Oklopˇ ci´ c, A. 2024, A&A, 688, A43, doi: 10.1051/0004-6361/202450240

  25. [33]

    C., & Oklopˇ ci´ c, A

    Linssen, D. C., & Oklopˇ ci´ c, A. 2023, A&A, 675, A193, doi: 10.1051/0004-6361/202346583

  26. [34]

    2019, A&A, 624, A101, doi: 10.1051/0004-6361/201834491

    Locci, D., Cecchi-Pestellini, C., & Micela, G. 2019, A&A, 624, A101, doi: 10.1051/0004-6361/201834491

  27. [35]

    D., & Fortney, J

    Lopez, E. D., & Fortney, J. J. 2013, ApJ, 776, 2, doi: 10.1088/0004-637X/776/1/2

  28. [36]

    H., & Shu, F

    Lubow, S. H., & Shu, F. H. 1975, ApJ, 198, 383, doi: 10.1086/153614

  29. [37]

    2020, ApJ, 902, 85, doi: 10.3847/1538-4357/abb313

    MacLeod, M., & Loeb, A. 2020, ApJ, 902, 85, doi: 10.3847/1538-4357/abb313

  30. [38]

    2024, arXiv e-prints, arXiv:2411.12895, doi: 10.48550/arXiv.2411.12895

    MacLeod, M., Oklopˇ ci´ c, A., Nail, F., & Linssen, D. 2024, arXiv e-prints, arXiv:2411.12895, doi: 10.48550/arXiv.2411.12895

  31. [39]

    2025, ApJ, 988, 63, doi: 10.3847/1538-4357/ade0b7

    MacLeod, M., Oklopˇ ci´ c, A., Nail, F., & Linssen, D. 2025, ApJ, 988, 63, doi: 10.3847/1538-4357/ade0b7

  32. [40]

    2016, A&A, 589, A75, doi: 10.1051/0004-6361/201528065

    Mazeh, T., Holczer, T., & Faigler, S. 2016, A&A, 589, A75, doi: 10.1051/0004-6361/201528065

  33. [41]

    A., Kratter, K., & Krumholz, M

    McCann, J., Murray-Clay, R. A., Kratter, K., & Krumholz, M. R. 2019, ApJ, 873, 89, doi: 10.3847/1538-4357/ab05b8

  34. [42]

    A., Chiang, E

    Murray-Clay, R. A., Chiang, E. I., & Murray, N. 2009, ApJ, 693, 23, doi: 10.1088/0004-637X/693/1/23

  35. [43]

    2025, A&A, 695, A186, doi: 10.1051/0004-6361/202452740

    Nail, F., MacLeod, M., Oklopˇ ci´ c, A., et al. 2025, A&A, 695, A186, doi: 10.1051/0004-6361/202452740

  36. [44]

    2024, A&A, 684, A20, doi: 10.1051/0004-6361/202347709 Oklopˇ ci´ c, A., & Hirata, C

    Nail, F., Oklopˇ ci´ c, A., & MacLeod, M. 2024, A&A, 684, A20, doi: 10.1051/0004-6361/202347709 Oklopˇ ci´ c, A., & Hirata, C. M. 2018, ApJL, 855, L11, doi: 10.3847/2041-8213/aaada9 Oklopˇ ci´ c, A., Silva, M., Montero-Camacho, P., & Hirata, C. M. 2020, ApJ, 890, 88, doi: 10.3...

  37. [45]

    Owen, J. E. 2019, Annual Review of Earth and Planetary Sciences, 47, 67, doi: 10.1146/annurev-earth-053018-060246

  38. [46]

    E., & Jackson, A

    Owen, J. E., & Jackson, A. P. 2012, MNRAS, 425, 2931, doi: 10.1111/j.1365-2966.2012.21481.x

  39. [47]

    E., & Wu, Y

    Owen, J. E., & Wu, Y. 2013, ApJ, 775, 105, doi: 10.1088/0004-637X/775/2/105

  40. [48]

    E., & Wu, Y

    Owen, J. E., & Wu, Y. 2017, ApJ, 847, 29, doi: 10.3847/1538-4357/aa890a

  41. [49]

    E., Murray-Clay, R

    Owen, J. E., Murray-Clay, R. A., Schreyer, E., et al. 2023, MNRAS, 518, 4357, doi: 10.1093/mnras/stac3414 20Sethi et. al

  42. [50]

    Parker, E. N. 1960, ApJ, 132, 821, doi: 10.1086/146985

  43. [51]

    G., Gupta, A., Owen, J

    Rogers, J. G., Gupta, A., Owen, J. E., & Schlichting, H. E. 2021, MNRAS, 508, 5886, doi: 10.1093/mnras/stab2897

  44. [52]

    A., et al

    Saidel, M., Vissapragada, S., Knutson, H. A., et al. 2026, AJ, 171, 257, doi: 10.3847/1538-3881/ae4e17

  45. [53]

    2015, A&A, 576, A21, doi: 10.1051/0004-6361/201424330

    Salz, M., Banerjee, R., Mignone, A., et al. 2015, A&A, 576, A21, doi: 10.1051/0004-6361/201424330

  46. [54]

    C., & Schmitt, J

    Salz, M., Czesla, S., Schneider, P. C., & Schmitt, J. H. M. M. 2016, A&A, 586, A75, doi: 10.1051/0004-6361/201526109

  47. [55]

    E., Spake, J

    Schreyer, E., Owen, J. E., Spake, J. J., Bahroloom, Z., & Di Giampasquale, S. 2024, MNRAS, 527, 5117, doi: 10.1093/mnras/stad3528

  48. [56]

    Schulik, M., & Booth, R. A. 2023, MNRAS, 523, 286, doi: 10.1093/mnras/stad1251

  49. [57]

    J., Sing, D

    Spake, J. J., Sing, D. K., Evans, T. M., et al. 2018, Nature, 557, 68, doi: 10.1038/s41586-018-0067-5

  50. [58]

    2023, AJ, 165, 200, doi: 10.3847/1538-3881/acc336

    Spinelli, R., Gallo, E., Haardt, F., et al. 2023, AJ, 165, 200, doi: 10.3847/1538-3881/acc336

  51. [59]

    Simon, J. B. 2008, ApJS, 178, 137, doi: 10.1086/588755

  52. [60]

    M., Tomida, K., White, C

    Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, ApJS, 249, 4, doi: 10.3847/1538-4365/ab929b

  53. [61]

    Krumholz, M. R. 2015, ApJ, 808, 173, doi: 10.1088/0004-637X/808/2/173

  54. [62]

    D., Wynn, G

    Turnpenney, S., Nichols, J. D., Wynn, G. A., & Jia, X. 2020, MNRAS, 494, 5044, doi: 10.1093/mnras/staa824

  55. [63]

    Rogers, L. A. 2015, ApJ, 813, 101, doi: 10.1088/0004-637X/813/2/101

  56. [64]

    2003, Nature, 422, 143, doi: 10.1038/nature01448

    Vidal-Madjar, A., Lecavelier des Etangs, A., D´ esert, J.-M., et al. 2003, Nature, 422, 143, doi: 10.1038/nature01448

  57. [65]

    2025, AJ, 169, 117, doi: 10.3847/1538-3881/ada143

    Vissapragada, S., & Behmard, A. 2025, AJ, 169, 117, doi: 10.3847/1538-3881/ada143

  58. [66]

    A., dos Santos, L

    Vissapragada, S., Knutson, H. A., dos Santos, L. A., Wang, L., & Dai, F. 2022a, ApJ, 927, 96, doi: 10.3847/1538-4357/ac4e8a

  59. [67]

    A., Greklek-McKeon, M., et al

    Vissapragada, S., Knutson, H. A., Greklek-McKeon, M., et al. 2022b, AJ, 164, 234, doi: 10.3847/1538-3881/ac92f2

  60. [68]

    2018, ApJ, 860, 175, doi: 10.3847/1538-4357/aac1c0

    Wang, L., & Dai, F. 2018, ApJ, 860, 175, doi: 10.3847/1538-4357/aac1c0

  61. [69]

    J., Donahue, T

    Watson, A. J., Donahue, T. M., & Walker, J. C. G. 1981, Icarus, 48, 150, doi: 10.1016/0019-1035(81)90101-9

  62. [70]

    2019, ApJ, 874, 91, doi: 10.3847/1538-4357/ab06f8

    Wu, Y. 2019, ApJ, 874, 91, doi: 10.3847/1538-4357/ab06f8

  63. [71]

    Yelle, R. V. 2004, Icarus, 170, 167, doi: 10.1016/j.icarus.2004.02.008

  64. [72]

    V., Gully-Santiago, M., et al

    Zhang, Z., Morley, C. V., Gully-Santiago, M., et al. 2023, Science Advances, 9, eadf8736, doi: 10.1126/sciadv.adf8736

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