Pith. sign in

REVIEW 3 major objections 5 minor 119 references

A Next-Generation Exoplanet Atmospheric Retrieval Framework for Transmission Spectroscopy (NEXOTRANS): Comparative Characterization for WASP-39 b Using JWST NIRISS, NIRSpec PRISM, and MIRI Observations

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read A hybrid Bayesian-plus-machine-learning retrieval framework reads three JWST instruments at once and finds that WASP-39 b's atmosphere is super-solar in carbon, oxygen, and sulfur.

desk verdict A credible retrieval-framework benchmark undercut by a model-selection choice that contradicts the paper's own Bayesian evidence; the WASP-39 b abundance headline should not be taken at face value. read the letter →

arxiv 2504.18815 v2 pith:BNFZPSN3 submitted 2025-04-26 astro-ph.EP astro-ph.IM

Tonmoy Deka , Tasneem Basra Khan , Swastik Dewan , Priyankush Ghosh , Debayan Das , Liton Majumdar This is my paper · ORCID
classification astro-ph.EPastro-ph.IM
keywords exoplanetatmospherestransmissionspectroscopyatmosphericretrievalBayesianinferencemachinelearningWASP-39bJWSTequilibriumchemistry
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

This paper claims that NEXOTRANS, a retrieval framework that couples Bayesian nested sampling with four machine-learning regressors, can extract reliable atmospheric abundances from the combined JWST NIRISS, NIRSpec PRISM, and MIRI transmission spectra of the hot Saturn WASP-39 b. Applied to the full 0.6–12 µm dataset, the framework's best-fit modified hybrid equilibrium model returns super-solar elemental abundances: $\mathrm{O/H} = 14.12^{+2.86}_{-1.82}$ times solar, $\mathrm{C/H} = 21.37^{+4.93}_{-3.18}$ times solar, $\mathrm{S/H} = 5.37^{+0.79}_{-0.65}$ times solar, and $\mathrm{C/O} = 1.35^{+0.05}_{-0.02}$ times solar. The paper also shows that pure equilibrium chemistry cannot reproduce the observed SO$_2$ feature, that disequilibrium chemistry plus high-altitude ZnS and MgSiO$_3$ aerosols are needed, and that the machine-learning retrievals agree with the Bayesian ones, which would make fast comparative exoplanetology practical in the JWST era.

What carries the argument

The load-bearing machinery is the NEXOTRANS pipeline itself. Its forward model converts layer-by-layer opacities into a transmission spectrum using the 1-D path distribution method of Robinson (2017), with absorption cross-sections from the POSEIDON opacity database and opacity contributions from Rayleigh scattering, collision-induced absorption, patchy grey clouds with hazes, and Mie-scattering aerosols (ZnS and MgSiO$_3$). Chemistry is handled by four prescriptions—free chemistry, NEXOCHEM equilibrium (a Gibbs free-energy minimizer benchmarked against FastChem), modified hybrid equilibrium, and modified equilibrium-offset—and the retrieval layer combines nested sampling (PyMultiNest/UltraNest) with a stacking regressor (Random Forest, Gradient Boosting, and k-Nearest Neighbor feeding a Ridge meta-model) trained on 60,000 generated spectra after wavelength-based feature reduction. The paper's central identity is the corrected combined 0.6–12 µm spectrum, which under the hybrid equilibrium model returns the reported super-solar elemental ratios.

What would settle it

Re-run the hybrid equilibrium retrieval on the same combined dataset with the offsets left free or modelled as wavelength-dependent, and check whether the recovered $\mathrm{O/H}$, $\mathrm{C/H}$, $\mathrm{S/H}$, and $\mathrm{C/O}$ move outside the quoted 1$\sigma$ intervals; if they do, the super-solar abundances are artifacts of the fixed-offset correction.

Watch

Extended reading notes

Core claim

The central discovery is that a hybrid retrieval framework—Bayesian inference run alongside a stacking-regressor machine-learning surrogate—recovers a consistent chemical picture of WASP-39 b from three JWST instruments, and that the statistically preferred modified hybrid equilibrium model yields a super-solar and carbon-enhanced composition. For the best-fit model the paper reports volume mixing ratios for H$_2$O, CO$_2$, CO, H$_2$S, and SO$_2$, with SO$_2$ log VMR between $-6.25$ and $-5.73$ across all non-equilibrium models, and elemental ratios $\mathrm{O/H} = 14.12^{+2.86}_{-1.82}\times$ solar, $\mathrm{C/H} = 21.37^{+4.93}_{-3.18}\times$ solar, $\mathrm{S/H} = 5.37^{+0.79}_{-0.65}\times$ solar, and $\mathrm{C/O} = 1.35^{+0.05}_{-0.02}\times$ solar. The equilibrium-only model fails by underpredicting SO$_2$, which the authors interpret as evidence of photochemical disequilibrium. Benchmarked against eight other retrieval codes on MIRI data (Powell et al. 2024), NEXOTRANS returns consistent water and SO$_2$ abundances, and the ML predictions agree with the Bayesian posteriors, so the framework is put forward as a validated tool for JWST-era comparative exoplanetology.

Load-bearing premise

The load-bearing premise is that the NIRISS and MIRI spectra can be aligned to NIRSpec PRISM by two constant multiplicative offsets (57 ppm and 311.13 ppm) derived from an initial free-chemistry retrieval, and that all later retrievals on the corrected data are unaffected by any error in those offsets.

Editorial extensions

If this is right

  • If the hybrid equilibrium result holds, WASP-39 b's bulk elemental enrichment (O/H roughly 14 times solar, C/H roughly 21 times solar) is higher than earlier HST-era estimates, implying a more metal-rich formation environment for hot Saturns.
  • The failure of equilibrium chemistry to produce the observed SO$_2$ feature strengthens the case that photochemical and other disequilibrium processes are essential ingredients in JWST retrievals of hot giant atmospheres.
  • The consistency between the stacking-regressor ML retrievals and the Bayesian posteriors suggests that ML surrogates can replace or accelerate nested sampling for large JWST samples, provided their smaller error bars are interpreted carefully.
  • The per-instrument reduced chi-square values (2.67, 3.19, and 2.14 for NIRISS, PRISM, and MIRI) indicate that a single global model does not fit all three instruments equally well, so joint-instrument retrievals need per-instrument noise or model flexibility.
  • The constraints on ZnS and MgSiO$_3$ aerosols with modal sizes and terminator coverage fractions provide a path toward linking condensate chemistry to atmospheric dynamics in transmission spectra.

Reading between the lines

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

  • If the constant-offset alignment is a fair approximation, the same hybrid Bayesian-plus-ML pipeline could be applied to other JWST targets with multi-instrument data, but the per-instrument chi-square disparity warns that cross-instrument systematics may dominate the error budget in such combined retrievals.
  • The paper's reported C/O ratio (0.80 absolute, 1.35 times solar) is consistent with a carbon-enriched but not carbon-dominated atmosphere; a direct testable extension would be to search for C$_2$H$_2$ or HCN features shortward of 5 µm, where the model predicts enhanced carbon chemistry at super-solar C/O.
  • Because the ML posterior is built from ±10% perturbations around the observed transit depths, the ML error bars likely underestimate true parameter uncertainty; a comparison with the Bayesian widths provides a cheap way to calibrate ML confidence intervals for future use.
  • The offset values (57 ppm for NIRISS, 311.13 ppm for MIRI) differ by an order of magnitude; if MIRI's offset were partly astrophysical (e.g., a different terminator or wavelength-dependent spot contamination), the retrieved S/H and SO$_2$ abundances from MIRI absorption features could be biased, a hypothesis testable by fitting the MIRI data alone with and without the offset.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The manuscript presents NEXOTRANS, a new atmospheric retrieval framework that combines Bayesian nested sampling (PyMultiNest and UltraNest) with four machine-learning regressors, and applies it to JWST transmission spectroscopy of WASP-39 b using NIRISS (0.6-2.8 micron), NIRSpec PRISM (2.0-5.3 micron), and MIRI (5-12 micron). Four chemistry treatments are explored: free, equilibrium, modified hybrid equilibrium, and modified equilibrium-offset chemistry. The paper's central claims are that NEXOTRANS is validated as a hybrid Bayesian plus ML framework, that WASP-39 b's atmosphere is super-solar in O, C, and S, and that the best-fit model is the modified hybrid equilibrium chemistry, yielding O/H = 14.12 (+2.86/-1.82) x solar, C/H = 21.37 (+4.93/-3.18) x solar, S/H = 5.37 (+0.79/-0.65) x solar, and C/O = 1.35 (+0.05/-0.02) x solar. The authors also report constraints on SO2, Na, K, aerosols (ZnS, MgSiO3), and terminator cloud properties.

Significance. If the framework and the WASP-39 b results hold, NEXOTRANS would be a useful community tool: the MIRI benchmark in Table 5, comparing against eight independent retrieval codes across three pipeline reductions, provides strong external evidence that the forward model and sampler are sound, and the NEXOCHEM comparison against FastChem in Appendix 6.1 is a concrete, reproducible validation. The ML-Bayesian consistency checks are also a useful contribution. However, the headline elemental abundances are not reliable as presented because the model-selection step contradicts the paper's own Bayesian evidence, and because the instrument offsets used to align the datasets are derived from the same data and their uncertainties are not propagated. The scientific conclusions therefore require substantial reworking before the paper can be accepted.

major comments (3)
  1. [Section 4.1.1] The model-selection step is internally inconsistent with the paper's Bayesian framework. The text reports ln(Z) = 3148.86 for hybrid equilibrium and ln(Z) = 3163.09 for equilibrium offset under the same data and priors, a Bayes factor of roughly e^14 strongly favoring the equilibrium-offset model. The paper nevertheless selects hybrid equilibrium as the best statistical fit because its reduced chi-squared (2.97) is marginally lower than 2.98. These two reduced chi-squared values are statistically indistinguishable, and when Bayesian evidence has been computed, it is not justified to overturn the evidence ranking with an essentially flat chi-squared comparison. This choice directly affects the abstract's headline abundances, which are quoted for the hybrid model; the evidence-favored equilibrium-offset model gives different values, including log O/H of -1.72 versus -2.16 and C/O of 0.89 versus 0.80 (both still super-solar O/C/H/S, but shifted). The authors should either present the equilibrium-offset model as the primary result or provide a statistically justified argument, based on the computed evidences, for preferring the hybrid model.
  2. [Section 3, offset correction paragraph] The alignment of the three instruments rests on constant multiplicative offsets (57 ppm for NIRISS and 311.13 ppm for MIRI, relative to NIRSpec PRISM) that were themselves retrieved from an initial free-chemistry retrieval on the same combined dataset; all subsequent retrievals are then run on the corrected data. This is circular in an important sense: the offsets absorb any absolute-scale or wavelength-independent systematics, but their uncertainties are not carried into the final posterior distributions, and any wavelength-dependent residual between instruments would be misattributed to chemistry. The authors should fit the relative offsets as nuisance parameters simultaneously in all models, or demonstrate explicitly that the retrieved mixing ratios, C/O, and elemental abundances are insensitive to the offset values and their uncertainties. As written, the quoted error bars on the headline abundances omit this systematic term.
  3. [Section 4.2] The goodness-of-fit criterion itself is weakened by the fact that all combined-dataset reduced chi-squared values are near 3 (2.97-3.35), with per-instrument values of 2.67 (NIRISS), 3.19 (PRISM), and 2.14 (MIRI). These values indicate that the models underfit the data, yet the paper does not quote chi-squared probabilities, model the noise covariance, or rescale the error bars. Under these conditions, the small difference between reduced chi-squared values used for model ranking (2.97 versus 2.98) is not statistically meaningful. This reinforces Major Comment 1 and needs to be addressed by a proper noise model or by an explicit discussion of the underfitting and its consequences for parameter uncertainties.
minor comments (5)
  1. [Section 6.2.4 and Table 4] The text states that the Stacking Regressor achieves the highest R^2 score of 0.855, but Table 4 reports R^2 = 0.76 for the Stacking Regressor; this numerical inconsistency should be corrected.
  2. [Section 3.1.3] The ZnS modal particle radius is printed as 'log(rc/um) = 1.29' with a positive sign, whereas all comparable values elsewhere in the paper are negative (e.g., -1.29 in the free-chemistry retrieval); this is likely a sign error and should be corrected.
  3. [Throughout] The planet name is written inconsistently as both 'WASP-39 b' and 'WASP-39b'; the manuscript should adopt one convention.
  4. [Table 5 caption] The table header 'Redchi2' should be written as 'reduced chi-squared', and the caption should clarify which results correspond to NEXOTRANS's three cloud parametrizations for each reduction.
  5. [Section 4.2 and Appendix 6.4] The text says the per-dataset reduced chi-squared values were obtained by performing retrievals with the global best-fit hybrid equilibrium model; it should be stated explicitly whether these values come from fixed global best-fit parameters re-evaluated on each dataset or from re-fits to each individual dataset, since the interpretation differs.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: external benchmarks ground the forward model, and the flagged issues are statistical or consistency concerns rather than derivation-by-construction.

full rationale

The paper's load-bearing derivation is the NEXOTRANS forward model, not a self-referential prediction. Section 2.4 benchmarks NEXOTRANS on JWST MIRI data against eight independent retrieval codes from Powell et al. (2024), including POSEIDON, with consistent 1-sigma posteriors, and Appendix 6.1 benchmarks NEXOCHEM against FastChem over a large grid with stated T-P-C/O-metallicity ranges. These are external, parameter-free checks, so the framework claim does not reduce to its own inputs. The ML-versus-Bayesian agreement in Section 3.1 is an internal consistency check, because both use the same forward model, but it is not load-bearing: the framework's validation rests on the external MIRI benchmark. The instrument-offset correction in Section 3 (57 ppm NIRISS, 311.13 ppm MIRI) is a data-preprocessing step that may double-use the data and could bias model comparison, but the headline abundances are not equal by construction to those offsets, so this is a statistical and correctness concern rather than circularity. Similarly, choosing hybrid equilibrium as the 'best fit' by reduced chi-squared (2.97 vs 2.98) while reporting Bayesian evidence that favors equilibrium offset (ln Z = 3163.09 vs 3148.86) is an internal model-selection inconsistency and should be evaluated as a correctness risk, not as a circular reduction. The elemental abundance claims are retrieved parameters from a clearly specified forward model, not first-principles predictions, so there is no self-definitional step.

Assumptions & free parameters 16 free parameters · 8 assumptions · 0 invented entities

No new physical entities, forces, or conserved quantities are introduced. The novelty is in software architecture (NEXOTRANS, NEXOCHEM) and parametrization choices, not in new physics. All free parameters listed above are fitted to the JWST data, and the central abundance claims depend directly on them.

free parameters (16)
  • C/O ratio (hybrid equilibrium) = 0.80 (+0.03/-0.01)
    Fitted by PyMultiNest; drives the equilibrium chemistry grid and the headline super-solar C/O = 1.35 x solar.
  • Metallicity log[M/H] (hybrid) = 1.19 (+0.05/-0.04)
    Scales elemental inventory in NEXOCHEM; directly sets O/H, C/H, S/H enhancements.
  • log VMR SO2 (free in hybrid) = -5.80 (+0.11/-0.13)
    Free parameter in hybrid model to reproduce the SO2 feature.
  • log VMR Na (free in hybrid) = -7.97 (+0.44/-0.50)
    Free alkali abundance.
  • log VMR K (free in hybrid) = -8.74 (+0.15/-0.16)
    Free alkali abundance.
  • log VMR ZnS aerosol = -2.54 (+0.74/-0.85)
    Aerosol mixing ratio from Mie scattering model.
  • log VMR MgSiO3 aerosol = -9.42 (+3.71/-4.87)
    Aerosol mixing ratio from Mie scattering model.
  • ZnS modal particle radius log(rc/um) = 1.29 (+0.005/-0.005)
    Particle size controls Mie scattering slope.
  • MgSiO3 modal particle radius log(rc/um) = -1.74 (+0.40/-0.51)
    Particle size controls Mie scattering slope.
  • hc (aerosol scale-height factor) = 0.78 (+0.01/-0.01)
    Vertical extent of aerosol profile.
  • fc (terminator cloud fraction) = 0.59 (+0.01/-0.01)
    Coverage fraction of aerosols at terminator.
  • Madhusudhan P-T parameters (T0, alpha1, alpha2, log P1, log P2, log P3, log P_ref) = not tabulated; see corner plots (Figures 16-17)
    Parametric temperature profile parameters affecting scale heights and chemistry.
  • NIRISS relative offset = 57 ppm
    Derived from initial retrieval and applied as a constant correction; uncertainty not propagated.
  • MIRI relative offset = 311.13 ppm
    Derived from initial retrieval and applied as a constant correction; uncertainty not propagated.
  • Equilibrium offset factors (H2O, CO, CO2, H2S, CH4) = 1.38, 0.94, 0.33, 1.23, 1.73
    Multiplicative offsets in equilibrium-offset chemistry model.
  • C/O and log[M/H] (equilibrium-offset model) = 0.89 (+0.01/-0.02) and 1.66 (+0.09/-0.10)
    Alternate chemistry model parameters.
assumptions (8)
  • domain assumption H2/He dominated atmosphere with He/H2 = 0.17
    Section 3 states the atmosphere is H2+He with He/H2=0.17; if the mean molecular weight is wrong, retrieved VMRs shift.
  • domain assumption Plane-parallel geometry, geometric limit, no refraction or scattering in radiative transfer
    Section 2.1.1 uses Robinson (2017) with straight rays; strong scattering or refraction would change transit depth.
  • domain assumption 100-layer log-uniform pressure grid from 1e-7 to 100 bar
    Section 3; coarse or mis-specified grid resolution may affect retrieval of sharp spectral features.
  • domain assumption Madhusudhan & Seager (2009) P-T parameterization is adequate
    Section 2.1.2; a poor parameterization can bias abundance estimates.
  • ad hoc to paper Equilibrium chemistry grid from NEXOCHEM covers T=300-4000 K, P=1e-7-1e2 bar, C/O=0.2-2, [Fe/H]=0.1-1000 solar
    Appendix 6.1.2; retrievals can approach grid boundaries (e.g., equilibrium C/O=0.23 near the grid edge), which may truncate posteriors.
  • domain assumption Opacity line lists from the POSEIDON database are accurate
    Section 2.1.4; line list errors directly propagate into abundance errors.
  • standard math Likelihood uses independent Gaussian errors and ignores covariance between wavelength bins
    Equation 25; correlated systematic errors from data reduction would inflate evidence values and shrink quoted uncertainties.
  • ad hoc to paper ML training spectra at R=158, generated by the same forward model, are sufficient training data
    Section 3; feature reduction and the average 0.0562 um wavelength mismatch may hide model-data discrepancies and bias ML predictions.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Next-Generation Exoplanet Atmospheric Retrieval Framework for Transmission Spectroscopy (NEXOTRANS): Comparative Characterization for WASP-39 b Using JWST NIRISS, NIRSpec PRISM, and MIRI Observations." pith.science (2026). https://pith.science/paper/BNFZPSN3

@misc{pith2026250418815,
  author       = {Pith},
  title        = {Pith review of: A Next-Generation Exoplanet Atmospheric Retrieval Framework for Transmission Spectroscopy (NEXOTRANS): Comparative Characterization for WASP-39 b Using JWST NIRISS, NIRSpec PRISM, and MIRI Observations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BNFZPSN3}},
  note         = {Machine review of arXiv:2504.18815}
}
read the original abstract

The advent of JWST has marked a new era in exoplanetary atmospheric studies, offering higher-resolution data and greater precision across a broader spectral range than previous space-based telescopes. Accurate analysis of these datasets requires advanced retrieval frameworks capable of navigating complex parameter spaces. We present NEXOTRANS, an atmospheric retrieval framework that integrates Bayesian inference using UltraNest/PyMultiNest with four machine learning algorithms: Random Forest, Gradient Boosting, K-Nearest Neighbor, and Stacking Regressor. This hybrid approach enables a comparison between traditional Bayesian methods and computationally efficient machine learning techniques. Additionally, NEXOTRANS incorporates NEXOCHEM, a module for solving equilibrium chemistry. We applied NEXOTRANS to JWST observations of the Saturn-mass exoplanet WASP-39 b, spanning wavelengths from 0.6 microns to 12.0 microns using NIRISS, NIRSpec PRISM, and MIRI. Four chemistry models - free, equilibrium, modified hybrid equilibrium, and modified equilibrium-offset chemistry - were explored to retrieve precise Volume Mixing Ratios (VMRs) for H2O, CO2, CO, H2S, and SO2. Absorption features in both NIRSpec PRISM and MIRI data constrained SO2 log VMRs to values between -6.25 and -5.73 for all models except equilibrium chemistry. High-altitude aerosols, including ZnS and MgSiO3, were inferred, with constraints on their VMRs, particle sizes, and terminator coverage fractions, providing insights into cloud composition. For the best-fit modified hybrid equilibrium model, we derived super-solar elemental abundances of O/H = 14.12 (+2.86/-1.82) x solar, C/H = 21.37 (+4.93/-3.18) x solar, and S/H = 5.37 (+0.79/-0.65) x solar, along with a C/O ratio of 1.35 (+0.05/-0.02) x solar, demonstrating NEXOTRANS's potential for atmospheric characterization in the JWST era and beyond.

Figures

Figures reproduced from arXiv: 2504.18815 by the authors.

Figure 1
Figure 1. A schematic overview of the retrieval framework implemented in NEXOTRANS. This framework consists of two primary compo￾nents: the Forward Model and the Retrieval Framework. The Forward Model simulates the exoplanetary atmosphere and produces a model transmission spectrum, while Bayesian inference and machine learning techniques are employed to perform robust parameter estimation. 2.1.1. Radiative Transfer NEXOTRANS … view at source ↗
Figure 2
Figure 2. An illustration of the four atmospheric chemistry methods implemented in NEXOTRANS. (a) shows the free chemistry approach, in which the mixing ratios of all species remain constant with altitude. (b) shows the equilibrium assumption, where the mixing ratio profiles are obtained from NEXOCHEM for a particular C/O and M/H value. (c) shows the modified hybrid chemistry approximation, where the mixing ratio profiles are… view at source ↗
Figure 3
Figure 3. Schematic representation of the machine learning train￾ing structure used in NEXOTRANS. Training data, obtained af￾ter data generation, is used to train the base models individually, after which the Ridge Regressor is employed for the final prediction. This method is called supervised ensemble learning (Stacking Regressor). . In this matrix, each row corresponds to a single spectrum Sr, where r is the number of spec… view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Best-fit retrieved spectrum on Eureka! reduced data with NEXOTRANS and POSEIDON, both with the median and 1σ error envelope. The model corresponds to the patchy grey cloud and haze assumption run with a resolution of 15,000 and binned to 100 for plotting. The NEXOTRANS…
Figure 5
Figure 5. Figure 5: Corner plots comparing the posterior distributions of parameters retrieved with NEXOTRANS and POSEIDON for the Eureka! reduced MIRI data. The reference abundance constraints stated above each histogram correspond to results from the POSEIDON framework. across global pe…
Figure 6
Figure 6. Figure 6: Retrieved Volume Mixing Ratio profiles for: a) free chemistry and c) equilibrium chemistry, along with the corresponding P-T profiles retrieved. The dotted horizontal lines represent the retrieved median reference pressures for each model. The 1σ region for the VMR pro…
Figure 7
Figure 7. Figure 7: Retrieved Volume Mixing Ratio Profiles for: a) Hybrid Equilibrium Chemistry and c) Equilibrium Offset Chemistry, along with the corresponding PT profiles retrieved. The dotted horizontal lines represent the retrieved median reference pressures for that model. The 1σ re…
Figure 8
Figure 8. Figure 8: The retrieved spectra using the hybrid equilibrium chemistry model and machine learning approaches are shown. Observations from JWST instruments are illustrated with different colored error bars as indicated in the legend. 4. DISCUSSIONS Retrievals conducted using both…
Figure 9
Figure 9. Figure 9: The retrieved spectrum using the equilibrium offset chemistry model is shown, with the black line representing the median fit. The orange contour marks the corresponding 1σ uncertainty interval. Observations from JWST instruments are illustrated with different colored …
Figure 10
Figure 10. Figure 10: The retrieved elemental abundance ratios for oxygen (O), carbon (C), and sulfur (S) corresponding to the best-fit models are displayed. Figures (a) and (b) illustrate values retrieved using Bayesian inference and machine learning, respectively. Additionally, the mass￾…
Figure 11
Figure 11. Figure 11: Schematic representation of the computational workflow of NEXOCHEM. The architecture comprises three main components: (1) De￾fault inputs module, which generates thermodynamic parameters and abundances of chemical species; (2) Primary input module, responsible for pre…
Figure 12
Figure 12. Figure 12: Comparison between NEXOCHEM (dotted points) and FastChem (solid lines) for Wasp-39 b (P-T profile is taken from 3.1.2 ) and Wasp-12 b (P-T profile is taken from Stevenson et al. (2014)) with different C/O values ally adjusted such that the neighbor pair prediction val…
Figure 13
Figure 13. Figure 13: Retrieved best-fit individual spectra for NIRISS, NIRSpec PRISM, and MIRI using the global best-fit hybrid equilibrium model with aerosols (ZnS, MgSiO3) using NEXOTRANS. The reduced χ 2 values for NIRISS, NIRSpec PRISM and MIRI retrievals are 2.67, 3.19 and 2.14 respe…
Figure 14
Figure 14. Figure 14: PyMultiNest retrieved Posterior distribution for the mie scattering aerosol model assuming equilibrium offset chemistry. Here, log(Pref ) = reference pressure; α1 and α2 are slopes of the PT profile; log(P1), log(P2) and log(P3) are the pressures at different layers; …
Figure 15
Figure 15. Figure 15: Posterior distribution for the mie scattering aerosol model assuming equilibrium offset chemistry using Machine learning (Stacking Regressor) [PITH_FULL_IMAGE:figures/full_fig_p032_15.png]
Figure 16
Figure 16. Figure 16: PyMultiNest retrieved full posterior distribution for the mie scattering aerosol model assuming hybrid equilibrium chemistry [PITH_FULL_IMAGE:figures/full_fig_p033_16.png]
Figure 17
Figure 17. Figure 17: Posterior distribution for the mie scattering aerosol model assuming hybrid chemistry using Machine learning (Stacking Regressor) [PITH_FULL_IMAGE:figures/full_fig_p034_17.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

119 extracted references · 41 canonical work pages

  1. [1]

    S., & Marley, M

    Ackerman, A. S., & Marley, M. S. 2001, The Astrophysical Journal, 556, 872

    2001

  2. [2]

    2023, Nature, 614, 649

    Ahrer, E.-M. 2023, Nature, 614, 649

    2023

  3. [3]

    B., Mansfield, M., et al

    Ahrer, E.-M., Stevenson, K. B., Mansfield, M., et al. 2023, Nature, 614, 653

    2023

  4. [4]

    R., Alam, M

    Alderson, L., Wakeford, H. R., Alam, M. K., et al. 2023, Nature, 614, 664

    2023

  5. [5]

    Altman, N. S. 1992, The American Statistician, 46, 175

    1992

  6. [6]

    2021, A141

    Asplund, M., Amarsi, A., & Grevesse, N. 2021, A141

    2021

  7. [7]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, Annual Review of Astronomy and Astrophysics, 47, 481, doi: 10.1146/annurev.astro.46.060407.145222

    arXiv 2009

  8. [8]

    2016, Mon

    Azzam, A., Yurchenko, S., Tennyson, J., & Naumenko, O. 2016, Mon. Not. R. Astron. Soc, 460, 4063

    2016

Show all 119 references
  1. [9]

    2014, Monthly Notices of the Royal Astronomical Society, 437, 1828

    Barber, R., Strange, J., Hill, C., et al. 2014, Monthly Notices of the Royal Astronomical Society, 437, 1828

    2014

  2. [10]

    2007, The Astrophysical Journal, 661, L191

    Barman, T. 2007, The Astrophysical Journal, 661, L191

    2007

  3. [11]

    K., & Heng, K

    Barstow, J. K., & Heng, K. 2020, Space science reviews, 216, 82

    2020

  4. [12]

    2014, Publications of the Astronomical Society of the Pacific, 126, 1134

    Beichman, C., Benneke, B., Knutson, H., et al. 2014, Publications of the Astronomical Society of the Pacific, 126, 1134

    2014

  5. [13]

    J., Welbanks, L., Schlawin, E., et al

    Bell, T. J., Welbanks, L., Schlawin, E., et al. 2023, Nature, 623, 709

    2023

  6. [14]

    A., Lothringer, J., et al

    Benneke, B., Knutson, H. A., Lothringer, J., et al. 2019, Nature Astronomy, 3, 813, doi: 10.1038/s41550-019-0800-5

    2019 doi

  7. [15]

    Blecic, J., Harrington, J., & Bowman, M. O. 2016, The Astrophysical Journal Supplement Series, 225, 4, doi: 10.3847/0067-0049/225/1/4

    2016 doi

  8. [16]

    1996, Machine learning, 24, 123 —

    Breiman, L. 1996, Machine learning, 24, 123 —. 2001, Machine learning, 45, 5

    1996

  9. [17]

    2021, arXiv preprint arXiv:2101.09604 —

    Buchner, J. 2021, arXiv preprint arXiv:2101.09604 —. 2023, Statistic Surveys, 17, 169

    2021 arXiv

  10. [18]

    2014, Astronomy & Astrophysics, 564, A125 B¨uhlmann, P., & Hothorn, T

    Buchner, J., Georgakakis, A., Nandra, K., et al. 2014, Astronomy & Astrophysics, 564, A125 B¨uhlmann, P., & Hothorn, T. 2007, JSTOR

    2014

  11. [19]

    2024, Nature Astronomy, 8, 1008

    Carter, A., May, E., Espinoza, N., et al. 2024, Nature Astronomy, 8, 1008

    2024

  12. [20]

    W., J., Curnutt, J

    Chase, M. W., J., Curnutt, J. L., Downey, J. R., J., et al. 1982, Journal of Physical and Chemical Reference Data, 11, 695, doi: 10.1063/1.555666

    1982 doi

  13. [21]

    Chase, M. W. 1986, JANAF thermochemical tables, 3rd edn. (Washington, D.C: American Chemical Society)

    1986

  14. [22]

    D., Himes, M

    Cobb, A. D., Himes, M. D., Soboczenski, F., et al. 2019, The astronomical journal, 158, 33

    2019

  15. [23]

    2024, MNRAS, 530, 3252–3277

    Constantinou, S., & Madhusudhan, N. 2024, MNRAS, 530, 3252–3277

    2024

  16. [24]

    2023, The Astrophysical Journal Letters, 943, L10, doi: 10.3847/2041-8213/acaead

    Constantinou, S., Madhusudhan, N., & Gandhi, S. 2023, The Astrophysical Journal Letters, 943, L10, doi: 10.3847/2041-8213/acaead

    2023 doi

  17. [25]

    1967, IEEE transactions on information theory, 13, 21

    Cover, T., & Hart, P. 1967, IEEE transactions on information theory, 13, 21

    1967

  18. [26]

    Crossfield, I. J. 2023, Nature

    2023

  19. [27]

    E., & Blecic, J

    Cubillos, P. E., & Blecic, J. 2021, Monthly Notices of the Royal Astronomical Society, 505, 2675

    2021

  20. [28]

    2019, MPI for Python, Feb

    Dalcin, L. 2019, MPI for Python, Feb

    2019

  21. [29]

    Dalcin, L., & Fang, Y .-L. L. 2021, Computing in Science & Engineering, 23, 47 Dalc´ın, L., Paz, R., & Storti, M. 2005, Journal of Parallel and Distributed Computing, 65, 1108

    2021

  22. [30]

    2005, Journal of Parallel and Distributed Computing, doi: 10.1016/j.jpdc.2005.03.010 Dalc´ın, L., Paz, R., Storti, M., & D’El´ıa, J

    Dalcin, L., Paz, R., & Storti, M. 2005, Journal of Parallel and Distributed Computing, doi: 10.1016/j.jpdc.2005.03.010 Dalc´ın, L., Paz, R., Storti, M., & D’El´ıa, J. 2008, Journal of Parallel and Distributed Computing, 68, 655

    2005 doi

  23. [31]

    2013, The Astrophysical Journal, 774, 95, doi: 10.1088/0004-637X/774/2/95

    Deming, D., Wilkins, A., McCullough, P., et al. 2013, The Astrophysical Journal, 774, 95, doi: 10.1088/0004-637X/774/2/95

    2013 doi

  24. [32]

    Dittmann, A. J. 2024, arXiv preprint arXiv:2404.16928

    2024 arXiv

  25. [33]

    Dudani, S. A. 1976, IEEE Transactions on Systems, Man, and Cybernetics, 325

    1976

  26. [34]

    L., Welch, B

    Eriksson, G., Holm, J. L., Welch, B. J., et al. 1971, Acta Chemica Scandinavica, 25, 2651–2658, doi: 10.3891/acta.chem.scand.25-2651

    1971 doi

  27. [35]

    D., Radica, M., Welbanks, L., et al

    Feinstein, A. D., Radica, M., Welbanks, L., et al. 2023, Nature, 614, 670

    2023

  28. [36]

    Feroz, F., & Hobson, M. P. 2008, Monthly Notices of the Royal Astronomical Society, 384, 449, doi: 10.1111/j.1365-2966.2007.12353.x

    2008

  29. [37]

    P., & Bridges, M

    Feroz, F., Hobson, M. P., & Bridges, M. 2009, Monthly Notices of the Royal Astronomical Society, 398, 1601, doi: 10.1111/j.1365-2966.2009.14548.x

    2009

  30. [38]

    P., Cameron, E., & Pettitt, A

    Feroz, F., Hobson, M. P., Cameron, E., & Pettitt, A. N. 2013, arXiv preprint arXiv:1306.2144

    2013 arXiv

  31. [39]

    J., Kitzmann, D., et al

    Fisher, C., Hoeijmakers, H. J., Kitzmann, D., et al. 2020, The astronomical journal, 159, 192

    2020

  32. [40]

    2009, Icarus, 199, 351

    Fletcher, L., Orton, G., Teanby, N., Irwin, P., & Bjoraker, G. 2009, Icarus, 199, 351

    2009

  33. [41]

    Fortney, J. J. 2024, arXiv preprint arXiv:2405.04587

    2024 arXiv

  34. [42]

    Friedman, J. H. 2001, Annals of statistics, 1189

    2001

  35. [43]

    2018, Monthly Notices of the Royal Astronomical Society, 474, 271

    Gandhi, S., & Madhusudhan, N. 2018, Monthly Notices of the Royal Astronomical Society, 474, 271

    2018

  36. [44]

    Gardner, J. P. 2005, Proceedings of the International Astronomical Union, 1, 87

    2005

  37. [45]

    2021, Nature, 592, 205, doi: 10.1038/s41586-021-03381-x

    Giacobbe, P., Brogi, M., Gandhi, S., et al. 2021, Nature, 592, 205, doi: 10.1038/s41586-021-03381-x

    2021 doi

  38. [46]

    2010, Astronomy & Astrophysics, 520, A27

    Guillot, T. 2010, Astronomy & Astrophysics, 520, A27

    2010

  39. [47]

    2019, Astronomy & Astrophysics, 625, A107

    Guilluy, G., Sozzetti, A., Brogi, M., et al. 2019, Astronomy & Astrophysics, 625, A107

    2019

  40. [48]

    2022, Astronomy & Astrophysics, 665, A104 36

    Guilluy, G., Giacobbe, P., Carleo, I., et al. 2022, Astronomy & Astrophysics, 665, A104 36

    2022

  41. [49]

    2009, The elements of statistical learning: data mining, inference, and prediction, Springer

    Hastie, T. 2009, The elements of statistical learning: data mining, inference, and prediction, Springer

    2009

  42. [50]

    E., & Kennard, R

    Hoerl, A. E., & Kennard, R. W. 1970, Technometrics, 12, 55

    1970

  43. [51]

    2024, Nature, 1

    Hu, R., Bello-Arufe, A., Zhang, M., et al. 2024, Nature, 1

    2024

  44. [52]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55 Joachim W. Stock, Daniel Kitzmann, A. B. C. P., & Sedlmayr, E. 2018, MNRAS 479, 865–874 (2018)

    2018 doi

  45. [53]

    2022, Astrophysics Source Code Library, ascl

    Kawahara, H., Kawashima, Y ., Masuda, K., et al. 2022, Astrophysics Source Code Library, ascl

    2022

  46. [54]

    2020, The Astrophysical Journal, 890, 174

    Kitzmann, D., Heng, K., Oreshenko, M., et al. 2020, The Astrophysical Journal, 890, 174

    2020

  47. [55]

    L., D´esert, J.-M., et al

    Kreidberg, L., Bean, J. L., D´esert, J.-M., et al. 2014, Nature, 505, 69

    2014

  48. [56]

    L., & Peytremann, E

    Kurucz, R. L., & Peytremann, E. 1975, SAO Special Report# 362, part 1., 362

    1975

  49. [57]

    I., & Burrows, A

    Lacy, B. I., & Burrows, A. 2020, The Astrophysical Journal, 904, 25

    2020

  50. [58]

    K., Pitrou, A., & Seibert, S

    Lam, S. K., Pitrou, A., & Seibert, S. 2015, in Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, 1–6

    2015

  51. [59]

    M., Mordasini, C., et al

    Lavie, B., Mendonc ¸a, J. M., Mordasini, C., et al. 2017, The Astronomical Journal, 154, 91

    2017

  52. [60]

    N., & Irwin, P

    Lee, J.-M., Fletcher, L. N., & Irwin, P. G. 2012, Monthly Notices of the Royal Astronomical Society, 420, 170

    2012

  53. [61]

    E., Rothman, L

    Li, G., Gordon, I. E., Rothman, L. S., et al. 2015, The Astrophysical Journal Supplement Series, 216, 15

    2015

  54. [62]

    2002, R news Lindg˚ard, A., & Nielsen, S

    Liaw, A. 2002, R news Lindg˚ard, A., & Nielsen, S. E. 1977, Atomic data and nuclear data tables, 19, 533

    2002

  55. [63]

    R., & Parmentier, V

    Line, M. R., & Parmentier, V . 2016, The Astrophysical Journal, 820, 78

    2016

  56. [64]

    R., Wolf, A

    Line, M. R., Wolf, A. S., Zhang, X., et al. 2013, The Astrophysical Journal, 775, 137

    2013

  57. [65]

    MacDonald, R. J. 2019, Apollo - University of Cambridge Repository, doi: 10.17863/CAM.44898

    2019 doi

  58. [66]

    J., & Batalha, N

    MacDonald, R. J., & Batalha, N. E. 2023a, Research Notes of the AAS, 7, 54 —. 2023b, Research Notes of the AAS, 7, 54, doi: 10.3847/2515-5172/acc46a

    doi

  59. [67]

    J., & Lewis, N

    MacDonald, R. J., & Lewis, N. K. 2022, ApJ, 929:20 (32pp), 2022 April 10

    2022

  60. [68]

    2019, Annual Review of Astronomy and Astrophysics, 57, 617, doi: https://doi.org/10.1146/annurev-astro-081817-051846

    Madhusudhan, N. 2019, Annual Review of Astronomy and Astrophysics, 57, 617, doi: https://doi.org/10.1146/annurev-astro-081817-051846

    2019 doi

  61. [69]

    2023, The Astrophysical Journal Letters, 956, L13

    Madhusudhan, N., Sarkar, S., Constantinou, S., et al. 2023, The Astrophysical Journal Letters, 956, L13

    2023

  62. [70]

    2009, The Astrophysical Journal, 707, 24

    Madhusudhan, N., & Seager, S. 2009, The Astrophysical Journal, 707, 24

    2009

  63. [71]

    Mai, C., & Line, M. R. 2019, The Astrophysical Journal, 883, 144 M´arquez-Neila, P., Fisher, C., Sznitman, R., & Heng, K. 2018, Nature Astronomy, 2, 719, doi: 10.1038/s41550-018-0504-2

    2019 doi

  64. [72]

    A., Min, M., Kamp, I., & Palmer, P

    Martinez, F. A., Min, M., Kamp, I., & Palmer, P. I. 2022, arXiv preprint arXiv:2203.01236

    2022 arXiv

  65. [73]

    K., Barstow, J

    Mikal-Evans, T., Sing, D. K., Barstow, J. K., et al. 2022, Nature Astronomy, 6, 471

    2022

  66. [74]

    K., Dong, J., et al

    Mikal-Evans, T., Sing, D. K., Dong, J., et al. 2023, The Astrophysical Journal Letters, 943, L17

    2023

  67. [75]

    W., Chubb, K., Helling, C., & Kawashima, Y

    Min, M., Ormel, C. W., Chubb, K., Helling, C., & Kawashima, Y . 2020, Astronomy & Astrophysics, 642, A28

    2020

  68. [76]

    2019, Astronomy & Astrophysics, 627, A67

    Molli`ere, P., Wardenier, J., Van Boekel, R., et al. 2019, Astronomy & Astrophysics, 627, A67

    2019

  69. [77]

    2013, Frontiers in neurorobotics, 7, 21

    Natekin, A., & Knoll, A. 2013, Frontiers in neurorobotics, 7, 21

    2013

  70. [78]

    E., et al

    Niraula, P., de Wit, J., Gordon, I. E., et al. 2022, Nature Astronomy, 6, 1287

    2022

  71. [79]

    C., & Madhusudhan, N

    Nixon, M. C., & Madhusudhan, N. 2020, Monthly Notices of the Royal Astronomical Society, 496, 269, doi: 10.1093/mnras/staa1150

    2020 doi

  72. [80]

    W., & Min, M

    Ormel, C. W., & Min, M. 2019, Astronomy & Astrophysics, 622, A121

    2019

  73. [81]

    2024, Astronomy & Astrophysics, 690, A159

    Patel, J., Brandeker, A., Kitzmann, D., et al. 2024, Astronomy & Astrophysics, 690, A159

    2024

  74. [82]

    2011, Journal of Machine Learning Research, 12, 2825

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825

    2011

  75. [83]

    Peterson, L. E. 2009, Scholarpedia, 4, 1883

    2009

  76. [84]

    2017, Monthly Notices of the Royal Astronomical Society, 471, 4355

    Pinhas, A., & Madhusudhan, N. 2017, Monthly Notices of the Royal Astronomical Society, 471, 4355

    2017

  77. [85]

    V ., Madhusudhan, N., & Apai, D

    Pinhas, A., Rackham, B. V ., Madhusudhan, N., & Apai, D. 2018, Monthly Notices of the Royal Astronomical Society, 480, 5314

    2018

  78. [86]

    L., Kyuberis, A

    Polyansky, O. L., Kyuberis, A. A., Zobov, N. F., et al. 2018, Monthly Notices of the Royal Astronomical Society, 480, 2597

    2018

  79. [87]

    D., Lee, E

    Powell, D., Feinstein, A. D., Lee, E. K., et al. 2024, Nature, 626, 979

    2024

  80. [88]

    2011, http://physics

    Ralchenko, Y . 2011, http://physics. nist. gov/asd

    2011

  81. [89]

    E., Rothman, L

    Richard, C., Gordon, I. E., Rothman, L. S., et al. 2012, Journal of Quantitative Spectroscopy and Radiative Transfer, 113, 1276

    2012

  82. [90]

    Robinson, T. D. 2017, ApJ, 836:236 (18pp), 2017 February 20

    2017

  83. [91]

    D., & Salvador, A

    Robinson, T. D., & Salvador, A. 2023, The Planetary Science Journal, 4, 10

    2023

  84. [92]

    2022, IEEE Transactions on Parallel and Distributed Systems, 34, 611

    Rogowski, M., Aseeri, S., Keyes, D., & Dalcin, L. 2022, IEEE Transactions on Parallel and Distributed Systems, 34, 611

    2022

  85. [93]

    2023, Nature, 614, 659

    Rustamkulov, Z., Sing, D., Mukherjee, S., et al. 2023, Nature, 614, 659

    2023

  86. [94]

    2010, Annual Review of Astronomy and Astrophysics, 48, 631

    Seager, S., & Deming, D. 2010, Annual Review of Astronomy and Astrophysics, 48, 631

    2010

  87. [95]

    K., Pont, F., Aigrain, S., et al

    Sing, D. K., Pont, F., Aigrain, S., et al. 2011, Monthly Notices of the Royal Astronomical Society, 416, 1443

    2011

  88. [96]

    K., Lecavelier des Etangs, A., Fortney, J

    Sing, D. K., Lecavelier des Etangs, A., Fortney, J. J., et al. 2013, Monthly Notices of the Royal Astronomical Society, 436, 2956

    2013

  89. [97]

    K., Fortney, J

    Sing, D. K., Fortney, J. J., Nikolov, N., et al. 2016, Nature, 529, 59 37

    2016

  90. [98]

    2006, Bayesian Analysis, 1, 833 , doi: 10.1214/06-BA127

    Skilling, J. 2006, Bayesian Analysis, 1, 833 , doi: 10.1214/06-BA127

    2006 doi

  91. [99]

    2005, JQSRT, 92, 293

    Sneep, M., & Ubachs, W. 2005, JQSRT, 92, 293

    2005

  92. [100]

    B., Bean, J

    Stevenson, K. B., Bean, J. L., Madhusudhan, N., & Harrington, J. 2014, The Astrophysical Journal, 791, 36

    2014

  93. [101]

    B., Harrington, J., Nymeyer, S., et al

    Stevenson, K. B., Harrington, J., Nymeyer, S., et al. 2010, Nature, 464, 1161, doi: 10.1038/nature09013

    2010 doi

  94. [102]

    2011, Journal of Quantitative Spectroscopy and Radiative Transfer, 112, 1403

    Tashkun, S., & Perevalov, V . 2011, Journal of Quantitative Spectroscopy and Radiative Transfer, 112, 1403

    2011

  95. [103]

    1996, Journal of the Royal Statistical Society Series B: Statistical Methodology, 58, 267

    Tibshirani, R. 1996, Journal of the Royal Statistical Society Series B: Statistical Methodology, 58, 267

    1996

  96. [104]

    2018, Experimental Astronomy, 46, 135, doi: 10.1007/s10686-018-9598-x

    Tinetti, G., Drossart, P., Eccleston, P., et al. 2018, Experimental Astronomy, 46, 135, doi: 10.1007/s10686-018-9598-x

    2018 doi

  97. [105]

    Tsai, S.-M., Lee, E. K. H., Powell, D., et al. 2023, Nature, 617, 483, doi: 10.1038/s41586-023-05902-2

    2023 doi

  98. [106]

    2018, The Astronomical Journal, 155, 156

    Tsiaras, A., Waldmann, I., Zingales, T., et al. 2018, The Astronomical Journal, 155, 156

    2018

  99. [107]

    2016, Mon

    Fateev, A. 2016, Mon. Not. R. Astron. Soc, 462, 4300 Van Rossum, G., & Drake, F. L. 2009, Python 3 Reference Manual (Scotts Valley, CA: CreateSpace)

    2016

  100. [108]

    R., & Sing, D

    Wakeford, H. R., & Sing, D. K. 2015, Astronomy & Astrophysics, 573, A122

    2015

  101. [109]

    R., Sing, D

    Wakeford, H. R., Sing, D. K., Deming, D., et al. 2017, The Astronomical Journal, 155, 29

    2017

  102. [110]

    P., Tinetti, G., Rocchetto, M., et al

    Waldmann, I. P., Tinetti, G., Rocchetto, M., et al. 2015, The Astrophysical Journal, 802, 107

    2015

  103. [111]

    2021, The Astrophysical Journal, 913, 114

    Welbanks, L., & Madhusudhan, N. 2021, The Astrophysical Journal, 913, 114

    2021

  104. [112]

    B., Johnson, S

    White, W. B., Johnson, S. M., & Dantzig, G. B. 1958, The Journal of Chemical Physics, 28, 751

    1958

  105. [113]

    Wiese, W. L. 1966, Natl. Bur. Stand. Ref. Data Ser., Natl. Bur. Stand.(US) Circ., 4

    1966

  106. [114]

    2018, Astronomy & Astrophysics, 614, A1

    Woitke, P., Helling, C., Hunter, G., et al. 2018, Astronomy & Astrophysics, 614, A1

    2018

  107. [115]

    Wolpert, D. H. 1992, Neural networks, 5, 241

    1992

  108. [116]

    Owen, T. C. 2004, Icarus, 171, 153

    2004

  109. [117]

    H., Changeat, Q., Al-Refaie, A., & Waldmann, I

    Yip, K. H., Changeat, Q., Al-Refaie, A., & Waldmann, I. P. 2024, The Astrophysical Journal, 961, 30

    2024

  110. [118]

    N., Amundsen, D

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

    2017

  111. [119]

    Zingales, T., & Waldmann, I. P. 2018, The Astronomical Journal, 156, 268

    2018

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.