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The effect of JWST/NIRSpec data reduction on the retrieval of WASP-39b atmospheric properties

T0 review · 3 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read Different ways of reducing the same JWST observation of WASP-39b change retrieved molecular abundances by more than an order of magnitude, matching the spread from cloud-modelling assumptions.

desk verdict A genuinely useful comparison of six NIRSpec/PRISM reductions of WASP-39b; the qualitative conclusion that reductions matter is solid, but the specific >1 dex magnitudes rest on medians of possibly multimodal posteriors and a non-random sample of pipelines. read the letter →

arxiv 2602.06722 v2 pith:RTUTCG2J submitted 2026-02-06 astro-ph.EP astro-ph.IM

J. Roy-Perez , S. Pérez-Hoyos , N. Barrado-Izagirre , H. Chen-Chen This is my paper
classification astro-ph.EPastro-ph.IM
keywords WASP-39bexoplanetatmospheresJWSTNIRSpec/PRISMtransmissionspectroscopydatareductionatmosphericretrievalcloudopacity
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 asks whether the choice of data-reduction pipeline for a single JWST/NIRSpec transit observation of the exoplanet WASP-39b changes the atmospheric properties inferred from Bayesian retrieval. It finds that six independently reduced spectra of the same observation yield molecular abundances that differ by more than an order of magnitude and isothermal temperatures that differ by hundreds of kelvin. The spread across pipelines is comparable to the spread produced by switching among three cloud-opacity parametrisations, meaning calibration choices matter as much as modelling choices. Removing the partially saturated 0.69–1.91 µm region reduces inter-pipeline dispersion but increases degeneracies among temperature, water, CO2, and CO.

What carries the argument

The analysis is built around a matrix of six independently processed transmission spectra of one observation, each run through the same Bayesian retrieval code with three cloud-extinction prescriptions: flat, power-law wavelength dependence, and Mie-scattering particle opacity. The decisive device is the spread itself – the range of retrieved parameters across pipelines – measured against the range across cloud models. A central named feature is the partially saturated 0.69–1.91 µm detector region, which each pipeline handled differently and which the authors also cut out in a second set of retrievals to isolate its influence.

What would settle it

Produce a seventh independent reduction of the same raw WASP-39b time series using a pipeline not among the six, run the same retrievals, and check whether the H2O abundance posterior falls inside the reported range. If it falls outside, the spread is not yet bracketed; if it falls inside, the qualitative claim survives. A second check: remove the offset spectrum that the paper itself flags as inconsistent with its error bars along most of the spectrum, and recompute the inter-pipeline abundance spread; if the >1 dex dispersion disappears, the magnitude of the claim depends on that single redu

Watch

Extended reading notes

Core claim

At the paper's center is a controlled comparison: the same fixed retrieval setup, varying only the input spectrum and the aerosol extinction prescription. Across six reductions of the WASP-39b NIRSpec/PRISM transit, retrieved molecular abundances vary by up to two orders of magnitude and isothermal temperatures by roughly 300 K, and the per-pipeline posteriors are frequently incompatible with one another. The magnitude of this calibration-induced spread is of the same order as the spread induced by changing from a flat to a wavelength-dependent cloud opacity. Bayesian evidence in every case favours non-flat aerosol extinction, but the preferred spectral shape of the aerosol opacity depends o

Load-bearing premise

The headline numbers assume that the six published spectra fairly sample the full range of plausible reductions of this observation, even though all six descend from one shared dataset and one of them shows an offset that may inflate the spread.

Editorial extensions

If this is right

  • Any single-reduction retrieval for WASP-39b carries an implicit pipeline-dependent uncertainty of at least one order of magnitude in molecular abundances, even when the reported random error bars are small.
  • Excluding the saturated region makes results more homogeneous across pipelines, but the stronger degeneracies mean that conclusions about H2O, CO2, and CO rest on a reduced, mutually entangled spectral range.
  • JWST/NIRSpec data can statistically distinguish flat from non-flat aerosol extinction, but the preferred spectral shape of the aerosol opacity cannot be pinned down until calibration procedures are unified.
  • Comparisons of WASP-39b across instruments or across publications will be confounded unless the same reduction philosophy is applied to all spectra.

Reading between the lines

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

  • If the six reductions bracket the true calibration uncertainty, then single-pipeline abundance claims for other JWST exoplanet observations may be overconfident by a similar factor, especially when parts of the detector are saturated.
  • A direct test of the paper's magnitude claims would be to repeat the same retrieval setup on several reductions of another NIRSpec/PRISM target without detector saturation; a smaller spread there would point to the saturated region, not pipeline choices generally, as the driver.
  • The two solution families the authors recover – a hot extended atmosphere with low abundances versus a compact cool atmosphere with high abundances – imply that reduction and cloud-model choices together can flip the physical narrative for this planet, so future retrieval studies should marginalise over both jointly.
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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 / 5 minor

Summary. This manuscript investigates how the choice of NIRSpec/PRISM data reduction affects atmospheric retrievals of WASP-39b. Six published reductions of the same ERS transit (FIREFLy, Tshirt, Tiberius, Eureka!, Carter, Carter Modified) are fed into identical PSG/PUMAS/MultiNest retrievals with an isothermal temperature profile, uniform chemical abundances, and first a flat cloud-opacity model. The authors compare retrievals using the full wavelength range with retrievals that exclude the saturated 0.69--1.91 micron region, and then compare three cloud-extinction parameterizations (flat, Angstrom, and Mie/MOPSMAP) on the no-saturation spectra. They report large inter-pipeline differences in retrieved parameters, often exceeding one order of magnitude in molecular abundances and reaching about 300 K in isothermal temperature, reduced but still substantial dispersion when the saturated region is removed, increased parameter degeneracies in that case, and Bayes factors that favor non-flat aerosol extinction for every reduction. They also identify two statistically indistinguishable families of atmospheric solutions, one compact and one extended, that correlate with different data reductions and cloud models.

Significance. If the quantitative claims are robust, this is an important cautionary result for the exoplanet retrieval community: single-reduction JWST retrievals may carry pipeline-dependent uncertainties as large as model-assumption uncertainties. The paper's strengths are its use of six published reductions, its explicit treatment of the saturated region, and its simultaneous comparison of reduction choices and aerosol parameterizations. The qualitative message -- that reductions matter and that saturation handling affects retrieved abundances -- is well supported by Figures 3--5 and Table 2. However, the specific magnitudes in the abstract are not fully established because the posterior summaries used for the comparison are medians of distributions that the paper itself shows to be multimodal, and because one of the six spectra is described as having an offset incompatible with its own error bars. These issues are addressable.

major comments (3)
  1. [§4, §4.3, Fig. A.7] The central quantitative claim ('>1 dex' abundance differences, hundreds of K in Tiso) is based on comparing medians of marginal posteriors, justified by the statement in §4 that 'following the law of large numbers, the most likely value for each parameter is the median.' This is only a valid summary for unimodal symmetric posteriors. The paper itself reports two statistically indistinguishable solution families (§4.3, Fig. A.7), i.e., multimodal posteriors. For a bimodal posterior the median can fall in a low-probability valley or jump from one mode to another as the input spectrum changes, so the FIREFLy-vs-Tshirt spread in H2O, for example, may reflect which mode each retrieval populates rather than a pipeline-dependent constraint on a single atmosphere. To support the abstract's quantitative magnitudes, the authors should report posterior modes or component-resolved summaries, or pre
  2. [§2, Fig. 2] The Carter spectrum is described as showing 'a clear offset not compatible with the error bars along most of the spectrum, possibly due to the additional offset introduced to cross-calibrate these data with other instruments.' This spectrum is nevertheless included as one of six equally weighted inputs in the headline comparisons. Since the reported spread is a range over the six spectra, an input that is internally inconsistent with its own error bars can inflate the dispersion. Please provide a sensitivity analysis excluding the Carter spectrum, or treating the cross-calibration offset as a nuisance parameter, and state explicitly whether the >1 dex abundance ranges and ~300 K temperature ranges persist under that exclusion.
  3. [§3.1, Table 2] The Bayesian-evidence conclusion that non-flat aerosol extinction is favored for every reduction rests on lnB values of about 8--14, but the manuscript reports no MultiNest convergence diagnostics (nlive, efficiency, tolerance), no evidence-error estimates, and no posterior-sample consistency checks. The evidence differences are large, so the qualitative aerosol conclusion may survive, but the exact values in Table 2 are not auditable as presented. Please report the sampling settings and evidence uncertainties, or make the posterior/evidence outputs available.
minor comments (5)
  1. [§3.2 vs. Table 1] The abundance prior is stated in the text as uniform log-prior between 10^-12 and 10^-1, while Table 1 lists 10^-15 to 10^-1. This inconsistency should be harmonized; the lower bound matters for species such as H2S whose posteriors push toward the prior edge.
  2. [§4] The phrase 'following the law of large numbers, the most likely value for each parameter is the median' is statistically inaccurate. The median is not the mode, and the law of large numbers concerns convergence of sample averages. Please rephrase to avoid implying that the median is the maximum a posteriori estimate.
  3. [Abstract and §4.3] The comparison with 'modelling assumptions' is restricted to aerosol-extinction parameterizations. Other model choices (vertical temperature profile, chemical priors, mean molecular weight treatment, fixed refractive indices) are not varied. The abstract and conclusions should say 'aerosol-model assumptions' rather than generic 'modelling assumptions,' or the scope should be stated as a lower bound.
  4. [§2] The six spectra all derive from a single ERS observation and share common systematics, so they are not independent draws from a reduction-uncertainty distribution. The reported spread should be interpreted as a spread among published reductions, not as a formal uncertainty distribution, and this caveat should appear in the conclusions.
  5. [Reproducibility] No code, posterior samples, or reduced spectra are provided. Given that the paper's central claim is a quantitative comparison of posterior summaries, releasing the posterior samples (or at least the corner-plot data for all retrievals) would allow readers to verify the median/mode issue and the inter-pipeline ranges.

Circularity Check

1 steps flagged · score 2.0 of 10

One minor self-citation in the aerosol model setup; the central pipeline-comparison claim is self-contained and externally corroborated.

  1. ansatz smuggled in via citation [Section 3.3, Cloud parametrisation; interpreted in §4.3 and Abstract]
    "The second model of cloud extinction is an exponential dependence of the optical thickness with wavelength (Ångström 1929) as in Roy-Perez et al. (2025) ... We follow the assumptions taken in Roy-Perez et al. (2025) and we define the aerosols in our simulations as spherical particles with a log-normal size distribution."

    The secondary conclusion that non-flat aerosol extinction is supported (§4.3: 'we find that the Bayesian evidence supports a cloud extinction model more complex than the common flat assumption') is framed as validation of Roy-Perez et al. (2025), yet the alternative models are not independently motivated here: they are imported verbatim from that self-cited paper ('as in Roy-Perez et al. (2025)', 'We follow the assumptions taken in Roy-Perez et al. (2025)'). The model-selection therefore operates only inside a hypothesis space defined by the same authors' prior ansatz; it cannot independently prove that non-flat extinction is required. This does not affect the main pipeline-comparison claim, which is an independent comparative measurement.

full rationale

The central claim—that independent NIRSpec/PRISM reductions produce order-of-magnitude differences in retrieved abundances and large temperature differences—is a comparative measurement, not a fitted value dressed as a prediction. Six externally reduced spectra are fed through the public PSG/MultiNest pipeline, and the spread of posterior medians is read off directly; no equation in the paper defines the claimed spread in terms of the inputs, and the conclusion agrees with independent external studies (Constantinou et al. 2023; Powell et al. 2024; Schleich et al. 2025). The only circularity-adjacent step is the aerosol-model loop: the non-flat-extinction conclusion is presented as validation of Roy-Perez et al. (2025) while the tested parametrizations are imported from that same self-cited paper. The Bayes factors are newly computed and could in principle have gone the other way, so this is not a full equation-level reduction; it is a minor self-citation that constrains the scope of the aerosol claim rather than the main pipeline-comparison result. The Carter-offset and multimodality issues are robustness/correctness concerns, not circularity.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The paper introduces no new free parameters or entities beyond the standard retrieval architecture; its contribution is comparative. The load-bearing burden sits in two domain assumptions: (1) the fixed forward-model/retrieval basis (isothermal, uniform-abundance, PSG) is adequate to isolate data-driven differences, and (2) the six published reductions fairly sample the calibration space. Both are reasonable but unproven here; the size of the claimed 'one order of magnitude' rests directly on the second.

free parameters (7)
  • Planet/star geometric parameters (D_pl, R_*, log g) = Posteriors consistent with Faedi et al. (2011), except FIREFLy log g anomaly
    Gaussian priors from discovery measurements (§3.2); retrieved per spectrum; robust across reductions (Fig. 3), so they contribute little to the dispersion story.
  • T_iso (isothermal temperature) = Posterior medians ~600–1400 K depending on spectrum
    Uniform prior 500–2000 K (§3.2, Table 1); retrieved per spectrum; symmetric across the comparison.
  • Molecular abundances (H2O, CO2, CO, SO2, Na, H2S) = Posteriors spanning roughly 10^-7 to 10^-3 depending on spectrum
    Log-uniform priors, stated inconsistently as 10^-12–10^-1 (§3.2) and 10^-15–10^-1 (Table 1); these are the quantities whose cross-pipeline dispersion constitutes the central measurement.
  • Cloud abundance X_Cl = Posteriors reported as pressure at optical depth τ=1 (~0.1–1 bar)
    Uniform vertical cloud profile (§3.3); retrieved per spectrum.
  • Ångström exponent α = Retrieved in the cloud-model loop
    Uniform prior [-5, 5] (Table 1); used only with the Ångström extinction parametrization.
  • Effective radius r_eff = ≈3.5 µm (Tiberius, Eureka, Carter) or ≈0.03 µm (FIREFLy, Tshirt)
    Log-uniform prior [0.005, 30] µm (Table 1); used only with the MOPSMAP Mie parametrization and drives the three aerosol-behavior groups in §4.3.
  • Fixed aerosol microphysics (v_eff, n_r, n_i) = 0.1, 1.4, 0.0001
    Chosen by hand (§3.3) following Roy-Perez et al. (2025); not retrieved. These choices shape the Mie-model cloud opacity and therefore the aerosol conclusions.
assumptions (6)
  • domain assumption PSG/PUMAS correlated-k radiative transfer at R=200 reproduces WASP-39b spectra to negligible error vs. higher resolution
    Stated in §3.1 ('Higher resolutions were tested, but they produced negligible differences'). If wrong, all retrievals share a forward-model bias and the comparative claims could be misattributed to the data.
  • domain assumption An isothermal, plane-parallel, uniform-abundance retrieval basis is adequate for this data set
    §3.2 justifies this by the authors' own prior model-selection analysis (Roy-Perez et al. 2025); if the true T/P structure couples differently with each reduction, the measured parameter dispersion is distorted.
  • domain assumption The six published reductions are correct as published and representative of the reduction-uncertainty distribution
    §2; all six share one raw ERS observation and the Carter spectrum carries a self-reported offset; the headline 'one order of magnitude' directly inherits this assumption.
  • domain assumption MultiNest with the stated priors returns converged evidences and posteriors
    §3.1; no convergence diagnostics or repeat runs are shown.
  • domain assumption Molecular line lists and continuum opacities used are complete and accurate
    §3.1 lists H2O, CO2, CO, SO2, Na, H2S, CH4, K line lists, Rayleigh, UV, and H2-H2/H2-He CIA; missing opacity would be absorbed into cloud/abundance parameters for all spectra equally.
  • standard math Jeffreys-scale thresholds (lnB > 1 preferred, > 5 decisive) are the correct interpretation of the evidences
    §3.1 and Trotta (2008); standard practice in the field.

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Cite this review

Pith. "Pith review of The effect of JWST/NIRSpec data reduction on the retrieval of WASP-39b atmospheric properties." pith.science (2026). https://pith.science/paper/RTUTCG2J

@misc{pith2026260206722,
  author       = {Pith},
  title        = {Pith review of: The effect of JWST/NIRSpec data reduction on the retrieval of WASP-39b atmospheric properties},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RTUTCG2J}},
  note         = {Machine review of arXiv:2602.06722}
}
read the original abstract

The JWST provides exoplanet transit observations with unprecedented spectral coverage, enabling detailed atmospheric characterisation. However, systematics introduced during data reduction can lead to small but significant uncertainties that propagate into atmospheric retrievals, making it essential to assess their impact on inferred properties. We aim to quantify the impact of different JWST/NIRSpec PRISM data-reduction processes as well as the relevance of saturation on the retrieved atmospheric properties of WASP-39b. We also assess whether or not these biases are comparable to those introduced by assumptions made in atmospheric modelling, particularly in the treatment of aerosol extinction. We perform nested-sampling Bayesian retrievals using MultiNest and forward models generated with the Planetary Spectrum Generator. Six independently reduced spectra are analysed, considering both the full wavelength range and versions excluding the saturated region. We further test the effect of including three different cloud-opacity parameterisations. Differences among JWST/NIRSpec data-reduction pipelines lead to substantial variations in retrieved atmospheric properties of WASP-39b, often exceeding one order of magnitude, comparable to uncertainties from modelling assumptions. Excluding the saturated region reduces inter-pipeline dispersion but increases parameter degeneracies. This highlights the need for robust and homogeneous calibration strategies. The results also confirm that JWST data possess the sensitivity required to probe aerosol spectral behaviour, although such constraints remain strongly dependent on the adopted data-reduction strategy.

Figures

Figures reproduced from arXiv: 2602.06722 by the authors.

Figure 1
Figure 1. Representation of the different spectral data used for the purpose of this work. The gray shades in the 0.63 - 2.06 micron range are related to the level of saturation: for the lightest region, only one group per integration was affected by saturation, while for the darkest region, up to four of the five groups were affected. At this point, it is important to note that the present work is not a comparison of the pip… view at source ↗
Figure 2
Figure 2. Comparison of the spectra with the FIREFLy data reduction as a reference and their respective residuals. Coloured regions at the resid￾uals represent error bar propagation. Grey regions show the saturated spectral range (0.63 - 2.06 µm) as in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison of the retrieval results obtained when using the different spectra as input (shown in different colours). See section 2 for a full reference of the spectra. The gray values represent the reference values from Faedi et al. (2011). The planetary radius at 100 mbar, the mean atmospheric molecular weight and the aerosol opacity at 1 µm were computed from actual outputs and not directly retrieved. rect the mos… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Comparison of the results obtained when using the different spectra in retrievals (shown in different colours). See section 2 for a full reference of the spectra. The filled points correspond to the spectra covering the full spectra range and the unfilled ones to the s…
Figure 5
Figure 5. Figure 5: Partial corner plot extracted from Figure A.4, using Eureka spec￾trum as the input for the retrievals. Black areas are used for the results including the whole spectral range, while red is used for those excluding the saturated 0.69 - 1.91 µm range. Temperature shows n…
Figure 6
Figure 6. Figure 6: Comparison of the results obtained when using the different spectra excluding the saturated region in retrievals. Each colour corresponds to a different input spectral data (see section 2 for a full reference of the spectra) and each marker shape correspond to a differ…
Figure 7
Figure 7. Figure 7: Comparison of the retrieved optical depths in nadir geometry along the whole spectral range using cloud extinction parametrisations defined in Sect. 3.3. Each colour corresponds to a different input spec￾tral data. Continuous lines represent optical depth retrieved wit…

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Reference graph

Works this paper leans on

63 extracted references · 1 linked inside Pith · cited by 1 Pith paper

  1. [1]

    Abel, M., Frommhold, L., Li, X., & Hunt, K. L. C. 2011, J. Phys. Chem. A., 115, 6805

    2011

  2. [2]

    Abel, M., Frommhold, L., Li, X., & Hunt, K. L. C. 2012, J. Chem. Phys., 136, 044319

    2012

  3. [3]

    R., Alam, M

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

    2023

  4. [4]

    F., Spiegelman, F., & Kielkopf, J

    Allard, N. F., Spiegelman, F., & Kielkopf, J. F. 2016, A&A, 589, A21

    2016

  5. [5]

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

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

    2019

  6. [6]

    & Lavvas, P

    Arfaux, A. & Lavvas, P. 2024, MNRAS, 530, 482

    2024

  7. [7]

    Barstow, J. K. 2020, MNRAS, 497, 4183

    2020

  8. [8]

    K., Aigrain, S., Irwin, P

    Barstow, J. K., Aigrain, S., Irwin, P. G. J., Kendrew, S., & Fletcher, L. N. 2015, Monthly Notices of the Royal Astronomical Society, 448, 2546

    2015

Show all 63 references
  1. [9]

    K., Aigrain, S., Irwin, P

    Barstow, J. K., Aigrain, S., Irwin, P. G. J., & Sing, D. K. 2016, ApJ, 834, 50

    2016

  2. [10]

    2022, The Journal of Open Source Soft- ware, 7, 4503

    Bell, T., Ahrer, E.-M., Brande, J., et al. 2022, The Journal of Open Source Soft- ware, 7, 4503

    2022

  3. [11]

    2017, ApJ, 834, 187

    Benneke, B., Werner, M., Petigura, E., et al. 2017, ApJ, 834, 187

    2017

  4. [12]

    2014, A&A, 564, A125

    Buchner, J., Georgakakis, A., Nandra, K., et al. 2014, A&A, 564, A125

    2014

  5. [13]

    L., May, E

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

    2024

  6. [14]

    2023, ApJ, 943, L10

    Constantinou, S., Madhusudhan, N., & Gandhi, S. 2023, ApJ, 943, L10

    2023

  7. [15]

    J., Yip, K

    Davey, J. J., Yip, K. H., Al-Refaie, A. F., & Waldmann, I. P. 2025, MNRAS, 536, 2618

    2025

  8. [16]

    E., Kirk, J., et al

    Espinoza, N., Steinrueck, M. E., Kirk, J., et al. 2024, Nature, 632, 1017

    2024

  9. [17]

    Faedi, F., Barros, S. C. C., Anderson, D. R., et al. 2011, A&A, 531, A40

    2011

  10. [18]

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

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

    2023

  11. [19]

    P., & Bridges, M

    Feroz, F., Hobson, M. P., & Bridges, M. 2009, MNRAS, 398, 1601

    2009

  12. [20]

    D., Knutson, H

    Fischer, P. D., Knutson, H. A., Sing, D. K., et al. 2016, ApJ, 827, 19

    2016

  13. [21]

    & Heng, K

    Fisher, C. & Heng, K. 2018, MNRAS, 481, 4698

    2018

  14. [22]

    2024, MNRAS, 535, 27

    Fisher, C., Taylor, J., Parmentier, V ., et al. 2024, MNRAS, 535, 27

    2024

  15. [23]

    & Wiegner, M

    Gasteiger, J. & Wiegner, M. 2018, GMD, 11, 2739

    2018

  16. [24]

    Gordon, I., Rothman, L., Hargreaves, R., et al. 2022, J. Quant. Spectr. Rad. Transf., 277, 107949

    2022

  17. [25]

    D., Wakeford, H

    Grant, D., Lothringer, J. D., Wakeford, H. R., et al. 2023, ApJ, 949, L15

    2023

  18. [26]

    Hansen, J. E. & Travis, L. D. 1974, Space Sci. Rev., 16, 527

    1974

  19. [27]

    Hastings, W. K. 1970, Biometrika, 57, 97

    1970

  20. [28]

    Irwin, P. G. J. 2009, Giant Planets of Our Solar System (Springer International Publishing)

    2009

  21. [29]

    1998, The theory of probability (OuP Oxford)

    Jeffreys, H. 1998, The theory of probability (OuP Oxford)

    1998

  22. [30]

    Khorshid, N., Min, M., Polman, J., & Waters, L. B. F. M. 2024, A&A, 685, A64

    2024

  23. [31]

    J., et al

    Kirk, J., López-Morales, M., Wheatley, P. J., et al. 2019, ApJ, 158, 144

    2019

  24. [32]

    V ., MacDonald, R

    Kirk, J., Rackham, B. V ., MacDonald, R. J., et al. 2021, AJ, 162, 34

    2021

  25. [33]

    J., Louden, T., et al

    Kirk, J., Wheatley, P. J., Louden, T., et al. 2017, MNRAS, 468, 3907

    2017

  26. [34]

    2020, ApJ, 890, 174

    Kitzmann, D., Heng, K., Oreshenko, M., et al. 2020, ApJ, 890, 174

    2020

  27. [35]

    2018, Exoplanet Atmosphere Measurements from Transmission Spectroscopy and Other Planet Star Combined Light Observations, ed

    Kreidberg, L. 2018, Exoplanet Atmosphere Measurements from Transmission Spectroscopy and Other Planet Star Combined Light Observations, ed. H. J. Deeg & J. A. Belmonte (Cham: Springer International Publishing), 2083– 2105 Lecavelier Des Etangs, A., Pont, F., Vidal-Madjar, A., ...

    2018

  28. [36]

    E., Rothman, L

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

    2015

  29. [37]

    J., Miguel, Y ., & Kubyshkina, D

    Louca, A. J., Miguel, Y ., & Kubyshkina, D. 2023, ApJ, 956, L19

    2023

  30. [38]

    2024, A&A, 687, A110

    Lueber, A., Novais, A., Fisher, C., & Heng, K. 2024, A&A, 687, A110

    2024

  31. [39]

    2025, arXiv e-prints, arXiv:2504.07823

    Ma, S., Saba, A., Faris Al-Refaie, A., et al. 2025, arXiv e-prints, arXiv:2504.07823

    2025 arXiv

  32. [40]

    MacDonald, R. J. & Madhusudhan, N. 2017, MNRAS, 469, 1979

    2017

  33. [41]

    2018, A&A, 613, A41

    Mancini, L., Esposito, M., Covino, E., et al. 2018, A&A, 613, A41

    2018

  34. [42]

    W., Rosenbluth, M

    Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. 1953, J. Chem. Phys., 21, 1087

    1953

  35. [43]

    V ., Fortney, J

    Morley, C. V ., Fortney, J. J., Kempton, E. M.-R., et al. 2013, ApJ, 775, 33

    2013

  36. [44]

    K., Gibson, N

    Nikolov, N., Sing, D. K., Gibson, N. P., et al. 2016, ApJ, 832, 191

    2016

  37. [45]

    L., Kyuberis, A

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

    2018

  38. [46]

    D., Lee, E

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

    2024

  39. [47]

    2025, A&A, 694, A249

    Roy-Perez, J., Pérez-Hoyos, S., Barrado-Izagirre, N., & Chen-Chen, H. 2025, A&A, 694, A249

    2025

  40. [48]

    K., Liu, R., & Wang, A

    Rustamkulov, Z., Sing, D. K., Liu, R., & Wang, A. 2022, ApJ, 928, L7

    2022

  41. [49]

    K., Mukherjee, S., et al

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

    2023

  42. [50]

    & Glidic, K

    Schlawin, E. & Glidic, K. 2025, tshirt: Time Series Helper and Integration Re- duction Tool, Astrophysics Source Code Library, record ascl:2501.004

    2025

  43. [51]

    2025, A&A, 704, A223

    Schleich, S., Boro Saikia, S., Changeât, Q., et al. 2025, A&A, 704, A223

    2025

  44. [52]

    K., Fortney, J

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

    2016

  45. [53]

    2006, Bayesian Analysis, 1, 833

    Skilling, J. 2006, Bayesian Analysis, 1, 833

    2006

  46. [54]

    D., Wolff, M

    Smith, M. D., Wolff, M. J., Clancy, R. T., Kleinböhl, A., & Murchie, S. L. 2013, JGR Planets, 118, 321

    2013

  47. [55]

    & Ubachs, W

    Sneep, M. & Ubachs, W. 2005, J. Quant. Spectr. Rad. Transf., 92, 293

    2005

  48. [56]

    2008, Contemporary Physics, 49, 71

    Trotta, R. 2008, Contemporary Physics, 49, 71

    2008

  49. [57]

    P., Zingales, T., et al

    Tsiaras, A., Waldmann, I. P., Zingales, T., et al. 2018, AJ, 155, 156

    2018

  50. [58]

    L., Mumma, M

    Villanueva, G. L., Mumma, M. J., Novak, R. E., et al. 2015, Science, 348, 218

    2015

  51. [59]

    L., Smith, M

    Villanueva, G. L., Smith, M. D., Protopapa, S., Faggi, S., & Mandell, A. M. 2018, J. Quant. Spectr. Rad. Transf., 217, 86

    2018

  52. [60]

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

    2015

  53. [61]

    R., Sing, D

    Wakeford, H. R., Sing, D. K., Deming, D., et al. 2018, AJ, 155, 29

    2018

  54. [62]

    N., Mellor, T

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

    2020

  55. [63]

    N., Owens, A., Kefala, K., & Tennyson, J

    Yurchenko, S. N., Owens, A., Kefala, K., & Tennyson, J. 2024, MNRAS, 528, 3719 Ångström, A. 1929, Geografiska Annaler, 11, 156 Article number, page 11 of 18 A&A proofs:manuscript no. aa58193-25 0.86 0.88 0.90 0.92 R* 0.60 0.66 0.72 0.78 log g 700 800 900 1000Tiso 3.0 2.7 2.4 2...

    2024

Pith tools

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