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REVIEW 3 major objections 6 minor 163 references

The paper claims that a five-parameter analytic temperature law, derived from radiative-advective-diffusive balance, lets a JWST phase curve of WASP-121b be read as a three-dimensional thermal structure.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-03 14:25 UTC pith:VGQNZWKI

load-bearing objection A genuinely useful analytic 3D temperature parameterization and a clean data-extraction method, with a dynamical interpretation that overshoots what the kinematic model can support. the 3 major comments →

arxiv 2607.29057 v1 pith:VGQNZWKI submitted 2026-07-31 astro-ph.EP

A Physically Driven Parameterisation of Multidimensional Atmospheres: Application to the JWST Phase Curve of WASP-121b

Yuanheng Yang , Guo Chen , Xianyu Tan , Thaddeus D. Komacek , Xi Zhang , Thomas M. Evans-Soma , Fei Yan , Chengzi Jiang
show 1 more author
Fei Dai
This is my paper
classification astro-ph.EP
keywords exoplanet atmosphereshot Jupitersphase curvesatmospheric retrievalthree-dimensional temperature structureWASP-121bradiative-advective-diffusive balanceJWST
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper tries to establish that the large-scale three-dimensional temperature field of a strongly irradiated, tidally locked hot Jupiter can be captured by a closed-form analytic solution to a one-dimensional longitudinal energy-balance equation, with just a few physically meaningful timescale ratios as controls. If true, it gives retrievals a cheap, physically interpretable bridge between disk-integrated phase-curve spectra and 3D atmospheric structure, without running full general circulation models inside the retrieval loop. Applied to the JWST/NIRSpec G395H phase curve of WASP-121b, the framework yields a consistent 3D thermal map: a strong day–night contrast, dayside and partial nightside thermal inversions, limb temperature asymmetry, and pressure-dependent hotspot offsets increasing from about 4° to 9°. The paper further argues that the small offsets favor a magnetically damped circulation over a non-magnetic one, and that the spectra require distinct dayside and nightside chemical states, pointing to disequilibrium chemistry.

Core claim

The central claim is that the three-dimensional temperature field of a strongly irradiated, tidally locked giant planet can be written analytically as the solution of a steady one-dimensional longitudinal balance between Newtonian radiative relaxation, zonal advection, and effective diffusion, with the ratios ε = τ_rad/τ_adv and ψ = τ_rad/τ_diff as control parameters. This closed-form temperature law, combined with a data-driven eclipse-normalisation that reads phase-resolved spectra directly from the time series, lets a Bayesian retrieval recover large-scale thermal structure from a JWST/NIRSpec G395H phase curve. For WASP-121b the retrieval finds a pronounced day–night contrast, dayside an

What carries the argument

The engine is the kinematic energy-balance equation (Eq. 6): ε ∂T/∂θ − ψ ∂²T/∂θ² = T_eq − T, where ε = τ_rad/τ_adv and ψ = τ_rad/τ_diff are the ratios of the radiative relaxation timescale to the advective and effective diffusive timescales. This linear ODE in longitude has a closed-form piecewise solution (Eq. 7) that gives the full T(θ, ϕ, p) field once the substellar and antistellar temperature profiles, T_d(p) and T_n(p), and a longitudinal sharpness parameter α(p) are supplied. The hotspot longitude is defined by ∂T/∂θ = 0 and reduces to θ_h ≈ tan⁻¹(ε/(1+ψ)) in the strong-radiative limit. This single balance is what lets the observable phase-curve morphology be translated into physical

Load-bearing premise

The load-bearing premise, stated in Section 3.1, is that all dynamical transport—including vertical entropy advection—can be folded into a single one-dimensional longitudinal advection–diffusion operator with constant timescales; if vertical or meridional transport contributes significantly, the retrieved ε, ψ, and hotspot offsets are effective quantities and the drag interpretation could be biased.

What would settle it

Generate a synthetic JWST-like phase curve from a general circulation model that includes strong vertical entropy advection, run the same retrieval, and compare the recovered ε(p), ψ(p), and hotspot offsets with the GCM's true values; any systematic mismatch demonstrates that the kinematic operator is absorbing unrepresented transport rather than measuring it.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the parameterisation is correct, phase-curve retrievals can recover the large-scale 3D thermal structure of hot Jupiters at a fraction of the cost of full GCM-based retrieval, making multidimensional characterisation routine for JWST and Ariel phase curves.
  • For WASP-121b, the retrieved structure implies a pressure-dependent dynamical regime: radiation dominates the upper atmosphere while advection and diffusion become progressively more important at depth.
  • The small, pressure-dependent hotspot offsets are more consistent with a magnetically damped circulation (3 G case) than a non-magnetic one, although the paper stresses that Rayleigh drag or other damping cannot be excluded.
  • The strong Bayesian preference for independent dayside and nightside elemental abundances (ΔlnZ ≈ 65 over shared abundances) indicates that a single globally uniform chemical composition is inadequate within the tested model family, implying real chemical inhomogeneity.
  • The retrieved thermal structure implies inhomogeneous cloud condensation conditions, with the cooler nightside and morning limb favouring condensates while the hottest dayside remains largely cloud-free.

Where Pith is reading between the lines

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

  • If the kinematic balance holds, the retrieved vertical profiles of ε(p) and ψ(p) could be mapped onto pressure-dependent zonal wind speeds, giving a direct, testable link between phase-curve retrievals and high-resolution Doppler wind measurements.
  • The eclipse-normalisation extraction is model-free at the reduction stage; a natural extension is to feed the same phase-resolved spectra into high-resolution cross-correlation analyses, potentially breaking degeneracies that disk-integrated photometry alone leaves unresolved.
  • The recovered day/night chemical contrast is better read as a diagnostic of vertical mixing and chemical quench levels than as a literal elemental abundance discontinuity; coupled kinetic models could test whether nightside vertical transport timescales are short enough to maintain the CH4 excess.
  • Because the paper itself notes that 1D-column radiative transfer may bias retrieved gradients, a testable extension is to recompute the favoured WASP-121b model with full 3D radiative transfer and check whether the limb asymmetry and hotspot offsets persist.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper presents a framework for extracting and interpreting multidimensional thermal structure from JWST phase curves, applied to the NIRSpec G395H observations of WASP-121b. It combines an 'eclipse normalisation' method that derives phase-resolved spectra by dividing by a stellar template from secondary eclipse, with an analytic 3D temperature parameterisation obtained by reducing the thermodynamic energy equation to a 1D zonal advection–diffusion balance with Newtonian cooling (Eq. 4). The retrieval, run with petitRADTRANS columns and NAUTILUS nested sampling, favours a model with independent dayside and nightside elemental abundances (DNChem). The preferred retrieval shows a strong day–night contrast, dayside and partial nightside thermal inversions, limb temperature asymmetry, and pressure-dependent hotspot offsets increasing from ~4° to ~9°. The authors argue that the confined dayside hot region and small offsets favour a magnetically damped 3 G GCM over a non-magnetic GCM, and that the enhanced nightside CH4 indicates disequilibrium chemistry.

Significance. If the dynamical interpretation survives scrutiny, the framework would be a valuable, computationally efficient tool for multidimensional retrievals. The paper has several concrete strengths: the source code is publicly available, the eclipse-normalised spectra are compared explicitly with standard light-curve fits, the radiative-transfer approximation is benchmarked against PICASO, and the retrieval uses a standard Bayesian framework with quantitative model comparison. The DNChem preference and the nightside CH4 interpretation are interesting and plausibly robust within the tested model family. However, the central dynamical claims rest on interpreting the fitted parameters ε and ψ as physical transport timescales, and that interpretation is not validated. The GCM comparisons are fits rather than predictions, and no test is offered that the retrieved (ε, ψ) correspond to the actual τ_rad/τ_adv and τ_rad/τ_diff in the GCMs. The paper is therefore useful and publishable in principle, but the dynamical conclusions currently outrun the evidence provided.

major comments (3)
  1. [§3.1, Eq. (4); §4.3, Figs. 11–12; §5.1] The reduction from Eq. (2) to Eq. (4) is a heuristic replacement of the full dynamical transport by a constant-coefficient 1D operator, as the paper itself acknowledges by calling it a 'kinematic model'. But the paper later interprets the fitted ε and ψ as physical timescale ratios and uses them to argue for magnetic drag. Because vertical entropy advection, meridional transport, and thermodynamic feedbacks are absorbed into these effective parameters, the retrieved ε(p), ψ(p) are shape parameters. The paper does not test whether they match the GCM's actual τ_rad/τ_adv and τ_rad/τ_diff. A concrete diagnostic would be to compute these timescale ratios from the GCM wind and radiative fields and compare them with the retrieved profiles, or to run synthetic retrievals on GCM spectra to see whether the true transport timescales are recovered. Without such a test, the magnetic-drag conclusion
  2. [§3.2, Figs. 5–6] The abstract and §3.2 state that the parameterisation 'reproduces' GCM temperature fields. However, the validation is a least-squares fit of the parameterisation to each GCM field; agreement therefore measures expressive flexibility, not physical fidelity. The paper should either provide an out-of-sample test (e.g., fit one GCM and predict another, or withhold a pressure/longitude region) or compare the fitted (ε, ψ) with independently diagnosed GCM transport timescales. Without this, Figs. 5–6 do not establish that Eq. (4) captures the dynamical content claimed in the title and conclusions.
  3. [§4.3, Fig. 11] The monotonic increase of ε and ψ with pressure is interpreted as 'dynamical heat redistribution becomes progressively more efficient at depth' (Fig. 11 caption) and as 'radiative relaxation is faster than advective and diffusive transport at low pressures' (§4.3). This attribution is not justified because ε=τ_rad/τ_adv depends on τ_rad, which in real hot-Jupiter atmospheres increases strongly with pressure. The same retrieved ε(p) profile could result from a radiative-timescale gradient with constant advection. Since τ_rad is not independently constrained—it is absorbed into the dimensionless ratios—the pressure dependence in Fig. 11 alone does not constrain the pressure dependence of transport. The text should be revised to treat ε(p), ψ(p) as phenomenological, or the analysis should include an independent radiative-timescale prior or diagnostic.
minor comments (6)
  1. [Abstract and §3.1] The phrase 'derived from radiative, advective and diffusive energy balance' overstates the status of Eq. (4). The paper itself calls it a kinematic model; the abstract and title should reflect this, e.g., 'inspired by' or 'parameterised following' a simplified energy balance.
  2. [§3.1, Eqs. (13)–(14)] The text says the hotspot offset is 'well approximated' by tan⁻¹(ε/(1+ψ)) in the strong-radiative regime, but retrieved ε and ψ reach values well above unity at depth. Please state explicitly whether the reported offsets come from the numerical solution of Eq. (14) or from the approximation, and verify the approximation against the full solution at the retrieved parameters.
  3. [§2.2] The description of the linear detrending step is vague: it should be stated more precisely how the linear function fitted to in-eclipse data is extrapolated to out-of-eclipse phases and propagated into the uncertainties. This is important for reproducing the eclipse-normalisation pipeline.
  4. [Fig. 3] The bottom panel labelled 'Diff. (σ)' is described as a histogram in the caption but the axes and units are not explained. Please clarify what is plotted.
  5. [Table 2] The Cloud model is reported with ΔlnZ = −17.05. This is weak-to-moderate evidence and should be described with appropriate caution; the text currently says it is 'disfavoured' without qualifying the strength.
  6. [References / typesetting] Several instances read 'W ASP-121b' with an unwanted space, and 'JWSTandAriel' is missing a space. Also, the data DOI is given incompletely as 'doi:10.17909' and should be completed.

Circularity Check

1 steps flagged

Central retrieval is a self-contained inverse analysis; the only circular-adjacent step is the GCM validation being an in-sample fit labelled a reproduction.

specific steps
  1. fitted input called prediction [Section 3.2, Figures 5 and 6]
    "Figure 5 presents a validation case where we fit the 3D temperature distribution of a non-grey, cloud-free, drag-free hot Jupiter GCM simulation from Roth et al. (2024). ... Our parameterised model successfully reproduces these key morphological features."

    The validation fits the parameterisation's free parameters (Tn, Td, alpha, epsilon, psi) directly to the GCM temperature field, then presents the resulting best fit as 'reproduces' the GCM. Because the same field is used both to determine and to test the parameters, the agreement is an in-sample fit rather than an out-of-sample prediction. This is not load-bearing for the WASP-121b retrieval, which fits the same model to JWST spectra, but the abstract's wording 'the parameterisation reproduces the large-scale thermal structures predicted by GCMs' overstates the evidential value.

full rationale

The central derivation chain is self-contained: the temperature parameterisation is set out as an explicit kinematic equation (Eq. 4), solved analytically (Eqs. 6-12), and then used as a forward model in a Bayesian retrieval against JWST/NIRSpec G395H data. The retrieved parameters (epsilon, psi, Tn, Td, alpha) are fitted to spectra, so statements about the retrieved thermal structure, hotspot offsets, and day/night chemical differences are standard inverse-modelling results, not circular. The comparisons to GCMs are external benchmarks, and the magnetic-drag interpretation is a physical interpretation of those comparisons rather than a forced consequence of the model equations. The only mild circular-adjacent step is the GCM validation being an in-sample fit presented as reproduction; this is a modelling-flexibility test, not a prediction, but it does not affect the independent retrieval conclusions. There is no load-bearing self-citation chain and no uniqueness theorem imported from the authors, so the paper does not exhibit material circularity.

Axiom & Free-Parameter Ledger

9 free parameters · 6 axioms · 0 invented entities

The model introduces no new particles or forces. Its free parameters are equilibrium-temperature profiles and timescale ratios fitted to the data. The main modeling choice is the kinematic reduction of the dynamics to a 1D advection-diffusion equation, which is an ad hoc simplification rather than a rigorous derivation.

free parameters (9)
  • Dayside Td(p) profile parameters = logκ_IR,d ≈ -2.00; logγ_d ≈ 0.53; logγ2,d ≈ -2.60; Tα,d ≈ 0.60; Tβ,d ≈ 1.50
    Defines the substellar vertical temperature profile via the Line et al. (2013) analytic atmosphere; fitted to phase-curve data.
  • Nightside Tn(p) profile parameters = logκ_IR,n ≈ -4.12; logγ_n ≈ -1.53; logγ2,n ≈ -2.98; Tα,n ≈ 0.53; Tβ,n ≈ 0.41
    Defines the antistellar vertical temperature profile; fitted to phase-curve data.
  • ε0, ε1 = -0.981, 1.411
    Intercept and slope of log10 ε(p) = ε0 + ε1 ζ(p); set the pressure-dependent radiative-to-advective timescale ratio.
  • ψ0, ψ1 = -0.379, 1.425
    Intercept and slope of log10 ψ(p); set the pressure-dependent radiative-to-diffusive timescale ratio.
  • ζα, Δζα = 0.490, 0.203
    Location and width of the logistic α(p) function that controls the longitudinal sharpness of the dayside forcing.
  • γ_lat = 0.223 (DNChem)
    Latitudinal exponent in cos^γlat φ; controls the meridional concentration of the temperature contrast.
  • Elemental abundances (DNChem) = dayside [O/H]=0.62, [C/H]=0.80, [Si/H]=0.88, [M/H]=0.32; nightside [O/H]=0.51, [C/H]=1.00, [Si/H]=1.69, [M/H]=-0.50
    Control the equilibrium chemistry and opacities for dayside and nightside columns; fitted to data.
  • β error scaling = 1.337
    Global scaling of reported JWST uncertainties in the Gaussian likelihood; fitted to the data.
  • Teff, Kp, Δv = Teff ≈ 6458 K, Kp ≈ 217.6 km/s, Δv ≈ -89 km/s
    Stellar effective temperature, orbital velocity, and velocity shift used in the forward model; fitted with informative priors.
axioms (6)
  • domain assumption Newtonian cooling approximation (Eq. 3)
    Radiative heating/cooling is parameterized as (Teq−T)/τ_rad; standard for hot Jupiters, but an approximation to real radiative transfer.
  • domain assumption Steady-state, no additional sources/sinks (Eq. 2)
    The paper neglects ∂T/∂t and DT to write the time-independent balance; reasonable for short-term dynamics but not exact.
  • ad hoc to paper Kinematic 1D advection-diffusion parameterization (Eq. 4)
    Vertical entropy advection and full 3D dynamics are replaced by a single zonal advection-diffusion operator; this is a modeling choice, not derived from first principles.
  • domain assumption Chemical equilibrium with FastChem (Sec. 3.3)
    Abundances are computed assuming instantaneous chemical equilibrium; invalid where kinetics are slow (e.g., nightside CH4).
  • domain assumption LTE, no scattering, 1D column radiative transfer (Sec. 3.4)
    The emergent intensity is computed in LTE, without scattering, and each column is independent; the paper cites literature showing 1D vs 3D transfer differences.
  • domain assumption Photometrically stable star and linear systematics (Sec. 2.2)
    Assumes WASP-121 is constant over the observing window and that instrument trends are linear functions of time fit to the two eclipses.

pith-pipeline@v1.3.0-daily-deepseek · 33160 in / 18237 out tokens · 159651 ms · 2026-08-03T14:25:34.476842+00:00 · methodology

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read the original abstract

Understanding the multidimensional structure of strongly irradiated exoplanets is essential for interpreting their atmospheric dynamics, chemistry and energy transport, yet current analyses remain limited by the difficulty of extracting reliable phase-resolved spectra and by the lack of physically interpretable parameterisations for retrievals. We combine a data-driven eclipse-normalisation method with an analytical three-dimensional temperature parameterisation derived from radiative, advective and diffusive energy balance and controlled by a few characteristic timescales. Applied to JWST/NIRSpec G395H observations of WASP-121b, the method yields spectra consistent with conventional phase-curve fitting, while the parameterisation reproduces the large-scale thermal structures predicted by general circulation models. The preferred retrieval reveals a pronounced day--night contrast, a dayside thermal inversion extending to both limbs, an inversion over part of the nightside, and limb temperatures differing by several hundred kelvin. Dynamical transport strengthens with pressure, and the hotspot offset increases from $\sim4^\circ$ to $\sim9^\circ$ across the pressures probed by G395H. The confined dayside hot region and the small, pressure-dependent offsets lie closer to the $\sim$3~G GCM than to its non-magnetic counterpart, although Rayleigh drag cannot be excluded. The spectra also favour distinct dayside and nightside chemical states, with more nightside CH$_4$ than the cooler temperatures alone can explain, pointing to disequilibrium chemistry. The retrieved thermal structure further implies an inhomogeneous cloud distribution, with condensation favoured on the nightside and cooler morning limb. The framework provides a computationally efficient, physically interpretable path from spectroscopic phase curves to multidimensional atmospheric structure.

Figures

Figures reproduced from arXiv: 2607.29057 by Chengzi Jiang, Fei Dai, Fei Yan, Guo Chen, Thaddeus D. Komacek, Thomas M. Evans-Soma, Xianyu Tan, Xi Zhang, Yuanheng Yang.

Figure 1
Figure 1. Figure 1: Data cleaning and normalisation process. Top: Iden￾tification and removal of outlier integrations based on the white￾light curve. Bottom: The resulting cleaned and concatenated phase￾resolved spectral matrix, showing the normalised flux ratio at the native instrumental resolution. tematics far more tractable. This shift has revitalised eclipse normalisation as a first-principles methodology, allowing us to… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of dayside thermal emission spectra obtained via eclipse normalisation and standard light-curve fitting. Top: Overplotted spectra from both methods. Bottom: Residuals (difference) between the two spectra and the histogram of their distribution. within measurement uncertainties. This allows us to proceed with atmospheric retrieval without the need for additional pa￾rameters to model complex instr… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of phase-binned thermal emission spectra obtained via eclipse normalisation and standard light-curve fitting [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sensitivity of the parameterised longitudinal temperature profile to variations in the key control parameters. Top: Effect of the sharpness parameter α. Dynamical transport is suppressed in this example by fixing ε = 10−3 and ψ = 10−3 , ensuring the temperature distribution is dominated by radiative processes. Middle: Effect of the dynamical control parameter ε = τrad/τadv. Other parameters are fixed (ψ = … view at source ↗
Figure 5
Figure 5. Figure 5: Validation of the temperature parameterisation against GCM simulations. Left: The longitude-pressure temperature structure at the equator from the hot Jupiter GCM (Roth et al. 2024). Right: The best-fitting temperature field reconstructed using our physically driven parameterisation, characterised by the antistellar and substellar temperatures, Tn and Td, together with the dimensionless parameters α, ε, an… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of longitude-latitude temperature maps at selected pressure levels (0.001, 0.115, and 0.464 bar from top to bottom), between the hot Jupiter GCM (left panels) from Roth et al. (2024) and our temperature parameterisation (right panels). In this validation example, the control parameters (ε, ψ) are allowed to vary with latitude to fully capture the 3D structure. tra are typically on the same order… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison between phase-resolved emission spectra computed using the pseudo-3D code PICASO (dashed lines) and those obtained from our disk-integrating multiple 1D radiative￾transfer framework (solid lines). Different colours correspond to spectra at various orbital phases. Assuming a standard Gaussian log-likelihood: ln L = − 1 2 X i, j    Ri j − Mi j2  βσi j2 + ln 2π  βσi j2   (… view at source ↗
Figure 8
Figure 8. Figure 8: Phase-resolved fit of the DNChem retrieval to the JWST/NIRSpec G395H emission spectra of WASP-121b. The left and middle panels show the observed planet-to-star flux ratio (excluding the transit and secondary eclipse) and the best-fitting model, respectively, as functions of wavelength and orbital phase. The right panel shows the residuals normalised by the observational uncertainties (D − M)/σ, together wi… view at source ↗
Figure 9
Figure 9. Figure 9: Longitude-pressure temperature structure retrieved from the DNChem model. The left column shows the posterior median tempera￾ture field and the right column shows the corresponding 1σ posterior uncertainty. The upper row gives the latitude-weighted mean temperature field, while the lower row shows the equatorial plane. The grey profiles beside the temperature maps show the normalised G395H contribution fun… view at source ↗
Figure 10
Figure 10. Figure 10: Longitude-resolved temperature profiles from the DNChem retrieval compared with representative GCM temperature profiles. The upper and lower rows compare the retrieval with GCM simulations assuming planetary magnetic-field strengths of 0 and 3 G, respectively. In each row, the left panel shows latitude-weighted mean profiles and the right panel shows equatorial-plane profiles. Solid curves denote the retr… view at source ↗
Figure 11
Figure 11. Figure 11: Retrieved vertical profiles of the dimensionless trans￾port parameters ε(p) and ψ(p) from the DNChem model. The bot￾tom blue axis shows ε, and the top red axis shows ψ. Solid curves denote posterior medians, while shaded regions and thin curves show the 1σ uncertainties. Both parameters increase monotonically with pressure, indicating that dynamical heat redistribution becomes progressively more efficient… view at source ↗
Figure 13
Figure 13. Figure 13: Retrieved temperature profiles compared with equi￾librium condensation curves. Panels (a)–(c) show the equatorial, mid-latitude, and high-latitude profiles, respectively. Within each panel, solid coloured curves show the posterior medians from the DNChem retrieval at different longitudes. The colour bar indicates longitude. Shaded bands show the 1σ uncertainties of the retrieved profiles. Labelled dashed … view at source ↗
Figure 12
Figure 12. Figure 12: Pressure-dependent hotspot offsets inferred from the Base and DNChem retrievals. The hotspot longitude is defined as the longitude of the maximum latitude-weighted temperature at each pressure level. Solid curves show the posterior medians, and shaded bands denote the corresponding 1σ uncertainties. Coloured curves show the corresponding hotspot offsets from representative GCM cases with different values … view at source ↗
Figure 14
Figure 14. Figure 14: Retrieved equatorial longitude–pressure abundance distributions of key opacity-bearing species from the DNChem model. The left column shows the posterior median log10 MMR, and the right column shows the corresponding 1σ uncertainty in log10 MMR. Rows show CO, H2O, and CH4 from top to bottom. The distributions reflect both the independently retrieved dayside and nightside elemental abundances and the local… view at source ↗
Figure 15
Figure 15. Figure 15: Performance comparison between MultiNest and NAUTILUS for the multidimensional retrieval. The left axis shows the ln Z, while the right axis shows the |∆ ln Z| as a function of the number of likelihood evaluations. NAUTILUS achieves a given evidence precision with substantially fewer likelihood evaluations than MultiNest, demonstrating its higher sampling efficiency for the present high-dimensional retrie… view at source ↗
Figure 16
Figure 16. Figure 16: Phase-resolved emission spectra of WASP-121b at representative orbital phases for the DNChem retrieval. The points with error bars show the observed planet-to-star flux ratios after excluding the transit and secondary-eclipse intervals. Solid curves show the corresponding best-fitting model spectra. For clarity, the spectra are vertically offset, with the orbital phases indicated on the right. The model c… view at source ↗
Figure 17
Figure 17. Figure 17: Contribution functions for the DNChem model. The pressure–wavelength contribution map shows the atmospheric pressures probed across the NIRSpec/G395H bandpass, while the pressure–longitude contribution map shows how the sensitivity varies with longitude after integrating over wavelength [PITH_FULL_IMAGE:figures/full_fig_p028_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Mass-mixing-ratio profiles of CO, CO2, H2O, and CH4 implied by the DNChem posterior. Orange and blue curves are evaluated at the substellar and antistellar points, respectively. Solid curves adopt the dayside elemental abundances, whereas dashed curves adopt the nightside elemental abundances. The curves show the posterior medians, and the shaded regions denote the 1σ uncertainties. At the antistellar poi… view at source ↗
Figure 19
Figure 19. Figure 19: Posterior distributions of the retrieved parameters for the DNChem model, which assigns independent elemental abundances to the dayside and nightside while retaining local chemical equilibrium within each hemisphere [PITH_FULL_IMAGE:figures/full_fig_p030_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Posterior distributions of the retrieved parameters for the Base model, which assumes a cloud-free atmosphere with globally shared elemental abundances [PITH_FULL_IMAGE:figures/full_fig_p031_20.png] view at source ↗

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