Pith. sign in

REVIEW 2 major objections 6 minor 1 cited by

New Constraints on DMS and DMDS in the Atmosphere of K2-18 b from JWST MIRI

T0 review · 2 major / 6 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 An independent JWST mid-infrared spectrum of K2-18 b shows spectral features that fit the sulfur molecules DMS and/or DMDS at about 3-sigma significance.

desk verdict First MIRI LRS spectrum of K2-18 b is a real step forward, but the 3-sigma DMS/DMDS claim rests on STP/N2 cross-sections that may not hold at the planet's low-pressure, H2-rich, ~400 K photosphere. read the letter →

arxiv 2504.12267 v1 pith:JYH7ZOKM submitted 2025-04-16 astro-ph.EP

Nikku Madhusudhan , Savvas Constantinou , M{aa}ns Holmberg , Subhajit Sarkar , Anjali A. A. Piette , Julianne I. Moses This is my paper · ORCID
classification astro-ph.EP
keywords K2-18bexoplanetatmospherestransmissionspectroscopyJWSTMIRIdimethylsulfidedisulfidebiosignatureshyceanworlds
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 reports the first mid-infrared transmission spectrum of a habitable-zone sub-Neptune, the candidate hycean world K2-18 b (a proposed ocean-bearing planet with a hydrogen-rich atmosphere), taken with JWST's MIRI LRS instrument over roughly 6-12 µm. The spectrum contains several features that a retrieval of 20 plausible molecules cannot attribute to most species, but that are well described by dimethyl sulfide (DMS) and/or dimethyl disulfide (DMDS), with a combined significance of 2.9-3.2 sigma. If the interpretation is right, at least one of these sulfur molecules is present at a mixing ratio above 10 parts per million by volume, making it a possible biosignature in a hydrogen-rich atmosphere. The result provides an independent check on an earlier near-infrared hint of DMS and strengthens the case that K2-18 b's atmosphere is consistent with a hycean world.

What carries the argument

The load-bearing element is the MIRI LRS transmission spectrum itself, binned at 0.2 µm with time-correlated noise accounted for, combined with a hierarchical Bayesian retrieval that scans opacities of 20 molecules. The discriminating pattern is a double-peak feature between 6.8-8 µm plus a broad peak near 9.8 µm for DMS and near 10.5 µm for DMDS, with features of about 300-400 ppm amplitude. The retrieval uses laboratory absorption cross sections for DMS and DMDS measured at Earth-like conditions (1 bar, 298 K, nitrogen broadening), and a leave-one-out analysis shows that the detection is driven by several spectral points rather than a single outlier.

What would settle it

Measure DMS and DMDS absorption cross sections at 1-10 mbar in an H2-dominated gas mixture at 300-600 K and rerun the canonical retrieval; if the Bayesian evidence for the molecules falls below about 2σ or the best-fit mixing ratios drop below 10 ppmv, the central claim would be refuted. A further useful test is a second MIRI transit or a near-infrared 3-5 µm observation that fails to reproduce the DMS/DMDS pattern.

Watch

Extended reading notes

Core claim

The central claim is that the 6-12 µm JWST MIRI LRS transmission spectrum of K2-18 b is inconsistent with a featureless spectrum at 3.4-sigma significance and can only be explained, among the molecules considered, by DMS and/or DMDS, at 2.9-3.2 sigma. When both molecules are included in the retrieval, DMDS is the preferred carrier of the features while DMS is largely unconstrained; removing either molecule lets the other absorb the same spectral bands because their mid-infrared features overlap. Single-molecule retrievals yield DMS at 2.9-3.0 sigma and DMDS at 3.0-3.2 sigma, with log10 volume mixing ratios near -3.4 and -3.2 respectively, and the paper concludes that at least one of the two molecules exceeds 10 ppmv. The paper treats this as new independent evidence for a possible sulfur biosignature on K2-18 b, complementary to the earlier near-infrared detections of methane and carbon dioxide.

Load-bearing premise

The abundance and detection rest on the assumption that the laboratory absorption cross sections for DMS and DMDS, measured at Earth-like pressure and temperature in nitrogen, are accurate for the low-pressure hydrogen-rich gas probed in transmission; if they are not, the same spectral features could shift, weaken, or belong to different molecules.

Editorial extensions

If this is right

  • The MIRI data provide an independent detection path from the near-infrared NIRISS/NIRSpec observations, so the DMS/DMDS signal does not depend on the detector-offset systematics that weakened the earlier DMS hint.
  • At the retrieved abundances, steady-state levels above 10 ppmv of DMS or DMDS would require a sustained source; the photochemical models the paper discusses imply biogenic ocean fluxes more than about 20 times Earth's, which would make the molecules a plausible biosignature if the detection holds.
  • One to three additional MIRI transits, roughly 8-24 hours of JWST time, should raise the combined significance to 4-5 sigma and test whether the features repeat.
  • The MIRI upper limits on methane and carbon dioxide are consistent with the roughly 1 percent abundances measured earlier, so the new spectrum does not conflict with the previously reported composition.

Reading between the lines

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

  • The DMS-DMDS overlap means the current data cannot cleanly separate the two molecules; observations in wavelength regions where their bands differ more, or high-resolution spectroscopy, would be needed to decide which molecule is actually present.
  • If laboratory measurements show that hydrogen-broadened DMS and DMDS cross sections at low pressure differ from the nitrogen-broadened Earth-like data, the retrieved mixing ratios and photospheric temperature would shift, and the biosignature interpretation might need revision.
  • The paper's false-positive discussion points to abiotic production of DMS and DMDS from CH4 and H2S in laboratory discharge and UV experiments; photochemical models that include the measured high CO2 abundance could predict whether such abiotic routes can sustain the inferred concentrations, giving a testable abiotic alternative.
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

2 major / 6 minor

Summary. The paper presents a JWST MIRI LRS transmission spectrum of the sub-Neptune K2-18 b over approximately 6-12 microns, reduced via two independent pipelines (JExoRES and JexoPipe). Atmospheric retrievals with the AURA framework, considering 20 molecular species, find that the spectrum is best explained by a combination of dimethyl sulfide (DMS) and dimethyl disulfide (DMDS), with a combined detection significance of 2.9-3.2 sigma and retrieved mixing ratios of at least about 10 ppmv. The authors perform extensive robustness tests, including different detrending, binning, limb-darkening treatments, GP-based correction for time-correlated noise, and a leave-one-out cross-validation analysis. They explicitly note that the DMS and DMDS opacities used are measured at near-STP with N2 broadening, which may not represent the H2-rich, low-pressure, roughly 400 K photosphere probed.

Significance. If the detection holds, this would be the first mid-infrared transmission spectrum of a habitable-zone sub-Neptune and an independent line of evidence for a potential biosignature gas on K2-18 b, complementing the earlier tentative DMS hint from NIRISS/NIRSpec. The paper's strengths are the dual-pipeline reduction, the thorough robustness campaign, the leave-one-out analysis, and the explicit caveat about cross-section limitations. The circularity burden is low: all abundances are free parameters fitted to the new MIRI data, and prior context is used only for priors and consistency comparisons. However, the headline significance and abundance claims rest on spectroscopic parameters whose applicability to the retrieved conditions is unquantified, and the mapping from Bayesian evidence to quoted sigma is not specified. These issues must be addressed before the central claim can be considered secure.

major comments (2)
  1. [Section 4.1 and Appendix B] The retrieval adopts HITRAN/PNNL cross sections for DMS and DMDS (Sharpe et al. 2004; Gordon et al. 2017) measured at 1 bar and 298 K with N2 broadening, whereas the retrieved photosphere is at log(P_ref/bar) = -4.32(+1.15/-0.93) and T ~ 422(+141/-133) K in an H2-dominated atmosphere (Section 3.2). As the authors state in Section 4.1, the retrieved mixing ratios and temperature are strongly dependent on these cross sections. Since the model selection, the Bayes factors in Table 2, and the leave-one-out analysis in Section 3.3 all use the same opacities, an unquantified error in the cross-section temperature/broadening could shift the >10 ppmv abundance claim and the 2.9-3.2 sigma significance. I request a quantitative sensitivity test, such as repeating key retrievals with cross sections that are scaled or convolved with a pressure-broadened line shape appropriate for H2 at millibar pressures, or, failing that, a revised abstract that explicitly conditions the detection significance and abundances on the STP/N2-broadened cross sections.
  2. [Section 3.1 and Table 2] The paper reports detection significances in sigma, such as DMS (2.9 sigma) and DMDS (3.2 sigma), derived from the Bayes factor ln(B), but the conversion formula is not specified. Using the common nested-model approximation sigma = sqrt(2 ln B) yields 2.4 sigma for the DMS-only case (ln(B)=2.86) and 2.8 sigma for the DMDS-only case (ln(B)=3.81), both lower than the quoted values. Please specify the exact relation used, including any effective-number-of-parameters correction, or report significance based on the posterior probability of the abundance exceeding a threshold, so that the headline 3-sigma claim is reproducible.
minor comments (6)
  1. [Figure 3 caption] The caption states 'The individual spectral contributions of these molecules are shown in Figure 3,' which is self-referential; it should reference Figure 14 or be removed.
  2. [Section 3.2] The text says 'The offset is retrieved to be 12+51/-58 ppm,' but Table 3 gives delta_MIRI = -12(+51/-58) ppm; please check the sign convention and ensure consistency between the text and the table.
  3. [Section 3.3] The text contains the typo 'a model that includes DMDS and DMDS'; this should read 'DMS and DMDS.'
  4. [References] Benneke et al. 2019a and 2019b are listed with identical journal, page, and DOI (ApJL 887, L14, 10.3847/2041-8213/ab59dc); please correct the bibliographic data for one of the two entries.
  5. [Table 2] In the row 'DMS + DMDS JExoRES (Exp+Linear1)', the DMS abundance is given as '1 < -2.44', which appears to be a formatting artifact; the upper limit should be displayed consistently with other rows.
  6. [Section 3.1] The sentence 'the model without DMS and DMDS is only marginally favoured over a flat spectrum below 2-sigma significance' is ambiguous; please specify whether 'the model' refers to the maximal model or the canonical model with DMS and DMDS removed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the DMS/DMDS detection is a free-parameter retrieval on new MIRI data, not a re-statement of its inputs.

full rationale

The paper's central claim—new independent evidence for DMS and/or DMDS from MIRI LRS at 2.9-3.2 sigma—is obtained by fitting DMS, DMDS, CH4, CO2, and nuisance parameters as free variables to a newly reduced MIRI transmission spectrum, and comparing Bayesian evidences with and without these molecules. No quantity derived from the spectrum is fed back into the definition of the model or the cross sections; the DMS/DMDS mixing ratios are outputs of the retrieval, not inputs. Self-citations to Madhusudhan et al. (2023b) enter only as priors on orbital parameters, prior abundance context, and consistency checks; the detection itself is driven by the new MIRI data and the leave-one-out analysis, which the paper explicitly computes. The acknowledged dependence of the retrieved abundances on HITRAN cross sections measured at STP with N2 broadening (Gordon et al. 2017; Section 4.1) is an external assumption about line opacities, not a circular reduction: it qualifies the interpretation but does not make the derivation equivalent to its inputs. No fitted parameter is relabeled as a prediction, and no load-bearing 'uniqueness theorem' is imported from prior work. Hence no specific circular step can be quoted.

Assumptions & free parameters 9 free parameters · 5 assumptions · 0 invented entities

The detection claim is supported by a fit of DMS/DMDS abundances and many nuisance parameters to the new MIRI data. The key external input is the molecular cross-section set, which the authors themselves flag as a major uncertainty. No new physical entities are introduced.

free parameters (9)
  • DMDS volume mixing ratio (log10 X_DMDS) = -3.48 (+1.24 / -2.27) in canonical retrieval
    Central to the detection; free parameter fitted to the MIRI spectrum, with a peak at about 10^-3.5 to 10^-3.25.
  • DMS volume mixing ratio (log10 X_DMS) = Unconstrained in canonical model (95% upper limit < -2.44); -3.42 (+1.16 / -1.44) in DMS-only retrieval
    Provides the degenerate alternative to DMDS; fitted when DMDS is removed from the model.
  • CH4 volume mixing ratio (log10 X_CH4) = -6.66 (+3.22 / -3.22) in canonical retrieval
    Included in canonical model but unconstrained; free parameter.
  • CO2 volume mixing ratio (log10 X_CO2) = -6.42 (+2.75 / -3.47)
    Included in canonical model but unconstrained.
  • Reference pressure log10(P_ref/bar) = -4.32 (+1.15 / -0.93)
    Sets the white-light radius in the transmission model.
  • MIRI spectrum offset delta_MIRI (ppm) = 12 (+51 / -58)
    Constant vertical offset applied to the MIRI spectrum; consistent with zero.
  • P-T profile parameters (6) = T0, alpha1, alpha2, log P1, log P2, log P3 (medians in Table 3)
    Parametric temperature profile in the retrieval (Madhusudhan & Seager 2009).
  • Cloud/haze parameters (4) = log(a)=2.23, gamma=-9.51, log(Pc)=-2.20, phi=0.49
    Parametric cloud/haze prescription (Pinhas et al. 2019); unconstrained.
  • Other molecular mixing ratios in maximal retrieval (18 species) = Unconstrained (upper limits)
    Included to test alternative explanations; none show well-constrained peaks.
assumptions (5)
  • domain assumption The terminator is modeled as a plane-parallel atmosphere in hydrostatic equilibrium with a parameterized T-P profile (Section 3).
    Standard retrieval assumption; not verified against independent data.
  • domain assumption Molecular mixing ratios are uniform across the observable photosphere (Section 3).
    Simplifies the retrieval; real atmospheres may have gradients.
  • domain assumption The 20-molecule opacity set is sufficient to explain the MIRI features (Section 3.1).
    Acknowledgement in Section 4.3 that the search may not be exhaustive; unknown species could match the features.
  • ad hoc to paper HITRAN cross sections for DMS and DMDS at 1 bar and 298 K with N2 broadening are applicable to the H2-rich low-pressure terminator (Appendix B and Section 4.1).
    Paper states the abundances and temperatures are strongly dependent on these cross sections.
  • domain assumption Stellar and orbital parameters (period, a/R*, inclination) from prior literature are correct (Section 2).
    Used as fixed or prior values in light-curve fitting.

how reviews work

0 comments
Cite this review

Pith. "Pith review of New Constraints on DMS and DMDS in the Atmosphere of K2-18 b from JWST MIRI." pith.science (2026). https://pith.science/paper/JYH7ZOKM

@misc{pith2026250412267,
  author       = {Pith},
  title        = {Pith review of: New Constraints on DMS and DMDS in the Atmosphere of K2-18 b from JWST MIRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JYH7ZOKM}},
  note         = {Machine review of arXiv:2504.12267}
}
abstract

The sub-Neptune frontier has opened a new window into the rich diversity of planetary environments beyond the solar system. The possibility of hycean worlds, with planet-wide oceans and H$_2$-rich atmospheres, significantly expands and accelerates the search for habitable environments elsewhere. Recent JWST transmission spectroscopy of the candidate hycean world K2-18 b in the near-infrared led to the first detections of carbon-bearing molecules CH$_4$ and CO$_2$ in its atmosphere, with a composition consistent with predictions for hycean conditions. The observations also provided a tentative hint of dimethyl sulfide (DMS), a possible biosignature gas, but the inference was of low statistical significance. We report a mid-infrared transmission spectrum of K2-18 b obtained using the JWST MIRI LRS instrument in the ~6-12 $\mu$m range. The spectrum shows distinct features and is inconsistent with a featureless spectrum at 3.4-$\sigma$ significance compared to our canonical model. We find that the spectrum cannot be explained by most molecules predicted for K2-18 b with the exception of DMS and dimethyl disulfide (DMDS), also a potential biosignature gas. We report new independent evidence for DMS and/or DMDS in the atmosphere at 3-$\sigma$ significance, with high abundance ($\gtrsim$10 ppmv) of at least one of the two molecules. More observations are needed to increase the robustness of the findings and resolve the degeneracy between DMS and DMDS. The results also highlight the need for additional experimental and theoretical work to determine accurate cross sections of important biosignature gases and identify potential abiotic sources. We discuss the implications of the present findings for the possibility of biological activity on K2-18 b.

Figures

Figures reproduced from arXiv: 2504.12267 by the authors.

Figure 1
Figure 1. MIRI LRS white light curve of K2-18 b. The light curve is based on the time series spectroscopic data binned between 4.8-10 µm and discarding the first 250 integrations. The top panel shows the white light curve with and with￾out binning, together with the median model fit. The lower panel shows the residuals after subtracting the model. The standard deviation of the residuals without binning is 471 ppm – correspond… view at source ↗
Figure 2
Figure 2. The mid-infrared transmission spectrum of K2-18 b obtained with the JWST MIRI LRS instrument. The data points with error bars (in brown) show the observed spectrum as described in section 2.1. The horizontal errorbars correspond to the spectral bin width. The dark blue curve denotes the median retrieved spectral fit, while the two lighter shaded regions denote the 1- and 2-σ intervals. The prominent features of DMDS… view at source ↗
Figure 3
Figure 3. Spectral contributions of notable chemical species in the MIRI band. Each curve denotes the spectral contribution of a particular molecule to the model spectrum, as denoted in the legend. The mixing ratios of DMS and DMDS are set to a representative value of 5 × 10−4 , while CH4 and CO2 are set to 10−2 , consistent with constraints obtained from previous near-infrared observations (Madhusudhan et al. 2023b). The bla… view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Demonstration of the stability of the MIRI transmission spectrum of K2-18 b using two independent data reduction pipelines. The spectra from the JExoRES and JexoPipe pipelines are shown in purple and orange, respectively, and are further described in sections 2.1 and 2…
Figure 5
Figure 5. Figure 5: Retrieved posterior probability distributions for DMDS and DMS from our canonical retrievals described in Section 3, using data from the JExoRES (top) and JexoPipe (bottom) pipelines. The posteriors shown in blue correspond to the canonical retrieval containing both DM…
Figure 6
Figure 6. Figure 6: Leave-one-out analysis for the MIRI transmission spectrum of K2-18 b. The black data points with error bars show the observed transmission spectrum obtained using the JExoRES pipeline. The center shading of each datapoint denotes the corresponding elpd difference (∆elp…
Figure 7
Figure 7. Figure 7: Effects of different non-linearity corrections on the MIRI transmission spectrum of K2-18 b. The spectra with the standard and our custom non-linearity corrections are shown in dark grey and yellow, respectively. erated by the University of Cambridge Research Com￾putin…
Figure 8
Figure 8. Figure 8: Comparisons between different spectral extraction methods. The dark grey points show the MIRI transmission spectrum of K2-18 b using the optimal extraction method while the green points show the spectrum using box extraction. of each pixel using f(t) = p1 + p2t + p3t 2…
Figure 9
Figure 9. Figure 9: MIRI transmission spectrum of K2-18 b using different bin widths. For our analysis, we do not consider the spectrum below 5.6 µm to be conservative, given that the choice of binning affects the spectrum in this region. Note that these spectra did not use GPs, as discus…
Figure 10
Figure 10. Figure 10: The MIRI transmission spectrum of K2-18 b obtained using different treatments of the detector settling effect. We consider an exponential + a linear trend (dark grey and purple), an exponential + a quadratic trend (pink), and a quadratic trend alone (orange), as well …
Figure 11
Figure 11. Figure 11: Comparison of different limb darkening assumptions on the MIRI transmission spectrum of K2-18 b. The wavelength-dependent model limb darkening case is shown in pink, while the wavelength-independent empirical limb darkening case is shown in dark grey. The model limb d…
Figure 12
Figure 12. Figure 12: Illustration of the correlation amplitude ρ (left) and average autocorrelation function (right). The median fit is shown in solid green, while the 1-σ and 2-σ regions are shown in two lighter shades of green. Next, we use the median ρ(λ) and η to re-fit the light curv…
Figure 13
Figure 13. Figure 13: Transit depth uncertainties of our MIRI transmission spectrum of K2-18 b with 0.2 µm bins. The solid and dotted green lines show the uncertainties for JExoRES, with and without using the GP model, respectively, as discussed in Appendix A.7. The solid orange line shows…
Figure 14
Figure 14. Figure 14: Spectral contributions from DMDS and DMS, also shown in [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: Posterior probability distributions for a number of molecular species included in the maximal retrieval described in Section 3.1. We find that only 1 out of the 20 species considered, DMDS, has a well-constrained posterior as shown. When DMDS is removed from the model…
Figure 16
Figure 16. Figure 16: Posterior probability distribution for the canonical retrieval using the JExoRES spectrum. Diagonal panels show the posterior probability distribution for each parameter and off-diagonal panels show the pairwise correlations. Horizontal errorbars denote the median and…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Are there Spectral Features in the MIRI/LRS Transmission Spectrum of K2-18b?

    astro-ph.EP 2025-04 conditional novelty 5.0 of 10

    In 5 of 6 Gaussian feature tests, the K2-18b MIRI/LRS spectrum prefers a flat line over spectral features, with only weak evidence (ln(B)=1.21) for features at fixed wavelengths.

Reference graph

Works this paper leans on

138 extracted references · 13 canonical work pages · cited by 1 Pith paper

  1. [1]

    Abel , M., Frommhold , L., Li , X., & Hunt , K. L. C. 2011, Journal of Physical Chemistry A, 115, 6805, 10.1021/jp109441f

    doi:10.1021/jp109441f 2011

  2. [2]

    K., Gao , P., Adams Redai , J., et al

    Alam , M. K., Gao , P., Adams Redai , J., et al. 2025, , 169, 15, 10.3847/1538-3881/ad8eb5

    doi:10.3847/1538-3881/ad8eb5 2025

  3. [3]

    E., Wakeford , H

    Alderson , L., Batalha , N. E., Wakeford , H. R., et al. 2024, , 167, 216, 10.3847/1538-3881/ad32c9

    doi:10.3847/1538-3881/ad32c9 2024

  4. [4]

    V., & Fayt, A

    Auwera, J. V., & Fayt, A. 2006, Journal of Molecular Structure, 780-781, 134, 10.1016/j.molstruc.2005.04.052

    doi:10.1016/j.molstruc.2005.04.052 2006

  5. [5]

    V., Moazzen-Ahmadi, N., & Flaud, J.-M

    Auwera, J. V., Moazzen-Ahmadi, N., & Flaud, J.-M. 2007, Astrophysical Journal, 662, 750, 10.1086/515567

    doi:10.1086/515567 2007

  6. [6]

    Azzam , A. A. A., Tennyson , J., Yurchenko , S. N., & Naumenko , O. V. 2016, , 460, 4063, 10.1093/mnras/stw1133

    doi:10.1093/mnras/stw1133 2016

  7. [7]

    J., Strange , J

    Barber , R. J., Strange , J. K., Hill , C., et al. 2014, , 437, 1828, 10.1093/mnras/stt2011

    doi:10.1093/mnras/stt2011 2014

  8. [8]

    J., Tennyson , J., Harris , G

    Barber , R. J., Tennyson , J., Harris , G. J., & Tolchenov , R. N. 2006, , 368, 1087, 10.1111/j.1365-2966.2006.10184.x

    arXiv 2006

Show all 138 references
  1. [9]

    B., Col \'o n , K

    Barclay , T., Kostov , V. B., Col \'o n , K. D., et al. 2021, , 162, 300, 10.3847/1538-3881/ac2824

    2021 doi

  2. [10]

    J., Crouzet , N., Cubillos , P

    Bell , T. J., Crouzet , N., Cubillos , P. E., et al. 2024, Nature Astronomy, 8, 879, 10.1038/s41550-024-02230-x

    2024 doi

  3. [12]

    2019 b , , 887, L14, 10.3847/2041-8213/ab59dc

    ---. 2019 b , , 887, L14, 10.3847/2041-8213/ab59dc

    2019 doi

  4. [13]

    2024, JWST Reveals CH \ \_4\ , CO \ \_2\ , and H \ \_2\ O in a Metal -rich Miscible Atmosphere on a Two - Earth - Radius Exoplanet , arXiv

    Benneke, B., Roy, P.-A., Coulombe, L.-P., et al. 2024, JWST Reveals CH \ \_4\ , CO \ \_2\ , and H \ \_2\ O in a Metal -rich Miscible Atmosphere on a Two - Earth - Radius Exoplanet , arXiv. http://arxiv.org/abs/2403.03325

    2024 arXiv

  5. [14]

    2020, arXiv e-prints, arXiv:2011.10424, 10.48550/arXiv.2011.10424

    B \'e zard , B., Charnay , B., & Blain , D. 2020, arXiv e-prints, arXiv:2011.10424, 10.48550/arXiv.2011.10424

    2020 doi arXiv

  6. [15]

    2015, , 526, 678, 10.1038/nature15707

    Bieler , A., Altwegg , K., Balsiger , H., et al. 2015, , 526, 678, 10.1038/nature15707

    2015 doi

  7. [16]

    2021, , 646, A15, 10.1051/0004-6361/202039072

    Blain , D., Charnay , B., & B \'e zard , B. 2021, , 646, A15, 10.1051/0004-6361/202039072

    2021 doi

  8. [17]

    1988, , 326, 509, 10.1086/166112

    Borysow , J., Frommhold , L., & Birnbaum , G. 1988, , 326, 509, 10.1086/166112

    1988 doi

  9. [18]

    Bouanich, J.-P., Blanquet, G., Walrand, J., & Courtoy, C. P. 1986, Journal of Quantitative Spectroscopy and Radiative Transfer, 36, 295, 10.1016/0022-4073(86)90053-1

    1986 doi

  10. [19]

    2021, The Exoplanet Characterization Toolkit (ExoCTK), 1.0.0, Zenodo, 10.5281/zenodo.4556063

    Bourque, M., Espinoza, N., Filippazzo, J., et al. 2021, The Exoplanet Characterization Toolkit (ExoCTK), 1.0.0, Zenodo, 10.5281/zenodo.4556063

    2021 doi

  11. [20]

    P., et al

    Bouwman , J., Kendrew , S., Greene , T. P., et al. 2023, , 135, 038002, 10.1088/1538-3873/acbc49

    2023 doi

  12. [21]

    2011, Journal of Quantitative Spectroscopy and Radiative Transfer, 112, 2446, 10.1016/j.jqsrt.2011.06.018

    Bray, C., Perrin, A., Jacquemart, D., & Lacome, N. 2011, Journal of Quantitative Spectroscopy and Radiative Transfer, 112, 2446, 10.1016/j.jqsrt.2011.06.018

    2011 doi

  13. [22]

    2021, The Journal of Open Source Software, 6, 3001, 10.21105/joss.03001

    Buchner , J. 2021, The Journal of Open Source Software, 6, 3001, 10.21105/joss.03001

    2021 doi

  14. [23]

    2014, , 564, A125, 10.1051/0004-6361/201322971

    Buchner , J., Georgakakis , A., Nandra , K., et al. 2014, , 564, A125, 10.1051/0004-6361/201322971

    2014 doi

  15. [24]

    2020, in Astronomical Society of the Pacific Conference Series, Vol

    Bushouse , H. 2020, in Astronomical Society of the Pacific Conference Series, Vol. 527, Astronomical Data Analysis Software and Systems XXIX, ed. R. Pizzo , E. R. Deul , J. D. Mol , J. de Plaa , & H. Verkouter , 583

    2020

  16. [25]

    Castelli , F., & Kurucz , R. L. 2003, in IAU Symposium, Vol. 210, Modelling of Stellar Atmospheres, ed. N. Piskunov , W. W. Weiss , & D. F. Gray , A20. astro-ph/0405087

    2003 arXiv

  17. [26]

    C., Krissansen-Totton , J., Kiang , N

    Catling , D. C., Krissansen-Totton , J., Kiang , N. Y., et al. 2018, Astrobiology, 18, 709, 10.1089/ast.2017.1737

    2018

  18. [27]

    L., Naumenko , O., Keely , S., et al

    Chubb , K. L., Naumenko , O., Keely , S., et al. 2018, , 218, 178, 10.1016/j.jqsrt.2018.07.012

    2018 doi

  19. [28]

    2017, , 608, A35, 10.1051/0004-6361/201731558

    Cloutier , R., Astudillo-Defru , N., Doyon , R., et al. 2017, , 608, A35, 10.1051/0004-6361/201731558

    2017 doi

  20. [29]

    2019, , 621, A49, 10.1051/0004-6361/201833995

    ---. 2019, , 621, A49, 10.1051/0004-6361/201833995

    2019 doi

  21. [30]

    2024, , 530, 3252, 10.1093/mnras/stae633

    Constantinou , S., & Madhusudhan , N. 2024, , 530, 3252, 10.1093/mnras/stae633

    2024 doi

  22. [31]

    2023, , 943, L10, 10.3847/2041-8213/acaead

    Constantinou , S., Madhusudhan , N., & Gandhi , S. 2023, , 943, L10, 10.3847/2041-8213/acaead

    2023 doi

  23. [32]

    J., & Madhusudhan , N

    Cooke , G. J., & Madhusudhan , N. 2024, arXiv e-prints, arXiv:2410.07313, 10.48550/arXiv.2410.07313

    2024 doi arXiv

  24. [33]

    W., & Sephton , M

    Court , R. W., & Sephton , M. A. 2012, , 73, 233, 10.1016/j.pss.2012.08.026

    2012 doi

  25. [34]

    J., et al

    Cubillos , P., Harrington , J., Loredo , T. J., et al. 2017, , 153, 3, 10.3847/1538-3881/153/1/3

    2017 doi

  26. [35]

    2024, , 968, L22, 10.3847/2041-8213/ad5204

    Damiano , M., Bello-Arufe , A., Yang , J., & Hu , R. 2024, , 968, L22, 10.3847/2041-8213/ad5204

    2024 doi

  27. [36]

    2001, Journal of Molecular Spectroscopy, 208, 281, 10.1006/jmsp.2001.8400

    Daumont, L., Auwera, J., Teffo, J.-L., Perevalov, V., & Tashkun, S. 2001, Journal of Molecular Spectroscopy, 208, 281, 10.1006/jmsp.2001.8400

    2001

  28. [38]

    2011 b , Astrobiology, 11, 419, 10.1089/ast.2010.0509

    ---. 2011 b , Astrobiology, 11, 419, 10.1089/ast.2010.0509

    2011

  29. [39]

    2024, Nature, 625, 51, 10.1038/s41586-023-06849-0

    Dyrek, A., Min, M., Decin, L., et al. 2024, Nature, 625, 51, 10.1038/s41586-023-06849-0

    2024 doi

  30. [40]

    C., \"O berg , K

    Fayolle , E. C., \"O berg , K. I., J rgensen , J. K., et al. 2017, Nature Astronomy, 1, 703, 10.1038/s41550-017-0237-7

    2017 doi

  31. [41]

    C., Bastelberger , S

    Felton , R. C., Bastelberger , S. T., Mandt , K. E., et al. 2022, Journal of Geophysical Research (Planets), 127, e06853, 10.1029/2021JE006853

    2022 doi

  32. [42]

    P., & Bridges, M

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

    2009

  33. [43]

    2017, , 154, 220, 10.3847/1538-3881/aa9332

    Foreman-Mackey , D., Agol , E., Ambikasaran , S., & Angus , R. 2017, , 154, 220, 10.3847/1538-3881/aa9332

    2017 doi

  34. [44]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, Publications of the Astronomical Society of the Pacific, 125, 306, 10.1086/670067

    2013 doi

  35. [45]

    2022, Publ

    Fukui , A., et al. 2022, Publ. Astron. Soc. Japan, 74, L1, 10.1093/pasj/psab106

    2022 doi

  36. [46]

    2017, MNRAS, 472, 2334, 10.1093/mnras/stx1601

    Gandhi , S., & Madhusudhan , N. 2017, MNRAS, 472, 2334, 10.1093/mnras/stx1601

    2017 doi

  37. [47]

    N., et al

    Gandhi , S., Brogi , M., Yurchenko , S. N., et al. 2020, , 495, 224, 10.1093/mnras/staa981

    2020 doi

  38. [48]

    P., Mather , J

    Gardner , J. P., Mather , J. C., Clampin , M., et al. 2006, , 123, 485, 10.1007/s11214-006-8315-7

    2006 doi

  39. [49]

    Glein, C. R. 2024, The Astrophysical Journal Letters, 964, L19, 10.3847/2041-8213/ad3079

    2024 doi

  40. [50]

    2014, , 149, 184, 10.1016/j.jqsrt.2014.07.005

    Golebiowski , D., de Ghellinck d'Elseghem Vaernewijck , X., Herman , M., Vander Auwera , J., & Fayt , A. 2014, , 149, 184, 10.1016/j.jqsrt.2014.07.005

    2014 doi

  41. [51]

    2010, Journal of Quantitative Spectroscopy and Radiative Transfer, 111, 2256, 10.1016/j.jqsrt.2010.01.031

    Gomez, L., Jacquemart, D., Lacome, N., & Mandin, J.-Y. 2010, Journal of Quantitative Spectroscopy and Radiative Transfer, 111, 2256, 10.1016/j.jqsrt.2010.01.031

    2010 doi

  42. [52]

    2017, Journal of Quantitative Spectroscopy and Radiative Transfer, 10.1016/j.jqsrt.2017.06.038

    Gordon, I., Rothman, L., Hill, C., et al. 2017, Journal of Quantitative Spectroscopy and Radiative Transfer, 10.1016/j.jqsrt.2017.06.038

    2017 doi

  43. [53]

    E., Rothman , L

    Gordon , I. E., Rothman , L. S., Hargreaves , R. J., et al. 2022, Journal of Quantitative Spectroscopy and Radiative Transfer, 277, 107949, 10.1016/j.jqsrt.2021.107949

    2022

  44. [54]

    Grant, D., & Wakeford, H. R. 2024, Journal of Open Source Software, 9, 6816, 10.21105/joss.06816

    2024 doi

  45. [55]

    K., Wakeford, H

    Grant, D., Lewis, N. K., Wakeford, H. R., et al. 2023, The Astrophysical Journal Letters, 956, L32, 10.3847/2041-8213/acfc3b

    2023 doi

  46. [56]

    P., Bell , T

    Greene , T. P., Bell , T. J., Ducrot , E., et al. 2023, , 618, 39, 10.1038/s41586-023-05951-7

    2023 doi

  47. [57]

    2024, , 976, 74, 10.3847/1538-4357/ad8565

    H \"a nni , N., Altwegg , K., Combi , M., et al. 2024, , 976, 74, 10.3847/1538-4357/ad8565

    2024 doi

  48. [58]

    J., Allen , N

    Harrison , J. J., Allen , N. D. C., & Bernath , P. F. 2012, , 113, 2189, 10.1016/j.jqsrt.2012.07.021

    2012 doi

  49. [59]

    M., Lewis , N

    He , C., H \"o rst , S. M., Lewis , N. K., et al. 2020, Nature Astronomy, 4, 986, 10.1038/s41550-020-1072-9

    2020 doi

  50. [60]

    2023, , 524, 377, 10.1093/mnras/stad1580

    Holmberg , M., & Madhusudhan , N. 2023, , 524, 377, 10.1093/mnras/stad1580

    2023 doi

  51. [62]

    2024, , 683, L2, 10.1051/0004-6361/202348238

    Holmberg , M., & Madhusudhan , N. 2024, , 683, L2, 10.1051/0004-6361/202348238

    2024 doi

  52. [63]

    1986, Publications of the Astronomical Society of the Pacific, 98, 609, 10.1086/131801

    Horne, K. 1986, Publications of the Astronomical Society of the Pacific, 98, 609, 10.1086/131801

    1986 doi

  53. [64]

    2021, , 921, 27, 10.3847/1538-4357/ac1789

    Hu , R. 2021, , 921, 27, 10.3847/1538-4357/ac1789

    2021 doi

  54. [65]

    Innes , H., Tsai , S.-M., & Pierrehumbert , R. T. 2023, , 953, 168, 10.3847/1538-4357/ace346

    2023 doi

  55. [66]

    2022, Astron

    Kawauchi , K., et al. 2022, Astron. Astrophys., 666, A4, 10.1051/0004-6361/202243381

    2022 doi

  56. [67]

    Kempton , E. M. R., Zhang , M., Bean , J. L., et al. 2023, , 620, 67, 10.1038/s41586-023-06159-5

    2023 doi

  57. [68]

    2015, , 127, 623, 10.1086/682255

    Kendrew , S., Scheithauer , S., Bouchet , P., et al. 2015, , 127, 623, 10.1086/682255

    2015 doi

  58. [69]

    N., Sagan , C., Bandurski , E

    Khare , B. N., Sagan , C., Bandurski , E. L., & Nagy , B. 1978, Science, 199, 1199, 10.1126/science.199.4334.1199

    1978

  59. [70]

    Kipping , D. M. 2013, , 435, 2152, 10.1093/mnras/stt1435

    2013 doi

  60. [71]

    2019, Journal of Quantitative Spectroscopy and Radiative Transfer, 10.1016/j.jqsrt.2019.04.001

    Kochanov, R., Gordon, I., Rothman, L., et al. 2019, Journal of Quantitative Spectroscopy and Radiative Transfer, 10.1016/j.jqsrt.2019.04.001

    2019 doi

  61. [72]

    2015, Publications of the Astronomical Society of the Pacific, 127, 1161, 10.1086/683602

    Kreidberg, L. 2015, Publications of the Astronomical Society of the Pacific, 127, 1161, 10.1086/683602

    2015 doi

  62. [73]

    Krissansen-Totton , J., Olson , S., & Catling , D. C. 2018, Science Advances, 4, eaao5747, 10.1126/sciadv.aao5747

    2018 doi

  63. [74]

    2024, Astronomy & Astrophysics, 686, A131, 10.1051/0004-6361/202348928

    Leconte, J., Spiga, A., Clément, N., et al. 2024, Astronomy & Astrophysics, 686, A131, 10.1051/0004-6361/202348928

    2024 doi

  64. [75]

    W., Parenteau , M

    Leung , M., Schwieterman , E. W., Parenteau , M. N., & Fauchez , T. J. 2022, , 938, 6, 10.3847/1538-4357/ac8799

    2022 doi

  65. [76]

    E., Rothman , L

    Li , G., Gordon , I. E., Rothman , L. S., et al. 2015, , 216, 15, 10.1088/0067-0049/216/1/15

    2015 doi

  66. [77]

    2023, , 955, L22, 10.3847/2041-8213/acf7c4

    Lim , O., Benneke , B., Doyon , R., et al. 2023, , 955, L22, 10.3847/2041-8213/acf7c4

    2023 doi

  67. [78]

    N., Yu , X., Glein , C

    Luu , C. N., Yu , X., Glein , C. R., et al. 2024, , 977, L51, 10.3847/2041-8213/ad9eb1

    2024 doi

  68. [79]

    I., Rigby , F., & Barrier , E

    Madhusudhan , N., Moses , J. I., Rigby , F., & Barrier , E. 2023 a , Faraday Discussions, 245, 80, 10.1039/D3FD00075C

    2023 doi

  69. [80]

    C., Welbanks, L., Piette, A

    Madhusudhan, N., Nixon, M. C., Welbanks, L., Piette, A. A. A., & Booth, R. A. 2020, The Astrophysical Journal, 891, L7, 10.3847/2041-8213/ab7229

    2020 doi

  70. [81]

    Madhusudhan , N., Piette , A. A. A., & Constantinou , S. 2021, , 918, 1, 10.3847/1538-4357/abfd9c10.48550/arXiv.2108.10888

    2021 doi arXiv

  71. [83]

    2023, The Astrophysical Journal Letters, 956, L13, 10.3847/2041-8213/acf577

    Madhusudhan, N., Sarkar, S., Constantinou, S., et al. 2023, The Astrophysical Journal Letters, 956, L13, 10.3847/2041-8213/acf577

    2023 doi

  72. [84]

    2009, , 707, 24, 10.1088/0004-637x/707/1/24

    Madhusudhan, N., & Seager, S. 2009, , 707, 24, 10.1088/0004-637x/707/1/24

    2009 doi

  73. [85]

    M., MacDonald , R

    May , E. M., MacDonald , R. J., Bennett , K. A., et al. 2023, , 959, L9, 10.3847/2041-8213/ad054f

    2023 doi

  74. [86]

    2022, arXiv e-prints, arXiv:2210.14293, 10.48550/arXiv.2210.14293

    Meadows , V., Graham , H., Abrahamsson , V., et al. 2022, arXiv e-prints, arXiv:2210.14293, 10.48550/arXiv.2210.14293

    2022 doi

  75. [87]

    2023, Astron

    Mikal-Evans , T., et al. 2023, Astron. J., 165, 84, 10.3847/1538-3881/aca90b

    2023 doi

  76. [88]

    T., Morton , T

    Montet , B. T., Morton , T. D., Foreman-Mackey , D., et al. 2015, , 809, 25, 10.1088/0004-637X/809/1/25

    2015 doi

  77. [89]

    E., Stevenson , K

    Moran , S. E., Stevenson , K. B., Sing , D. K., et al. 2023, , 948, L11, 10.3847/2041-8213/accb9c

    2023 doi

  78. [90]

    E., Dicken , D., Argyriou , I., et al

    Morrison , J. E., Dicken , D., Argyriou , I., et al. 2023, , 135, 075004, 10.1088/1538-3873/acdea6

    2023 doi

  79. [91]

    u ller, H., Schl\

    M\" u ller, H., Schl\" o der, F., Stutzki, J., & Winnewisser, G. 2005, Journal of Molecular Structure, 742, 215, 10.1016/j.molstruc.2005.01.027

    2005 doi

  80. [92]

    2016, Journal of Quantitative Spectroscopy and Radiative Transfer, 177, 49, 10.1016/j.jqsrt.2016.03.007

    Nikitin, A., Dmitrieva, T., & Gordon, I. 2016, Journal of Quantitative Spectroscopy and Radiative Transfer, 177, 49, 10.1016/j.jqsrt.2016.03.007

    2016 doi

  81. [93]

    S., Gustafsson , M., Burgdorf , M., & Meadows , V

    Orton , G. S., Gustafsson , M., Burgdorf , M., & Meadows , V. 2007, , 189, 544, 10.1016/j.icarus.2007.02.003

    2007 doi

  82. [94]

    2023, Nat

    Piaulet , C., et al. 2023, Nat. Astron., 7, 206, 10.1038/s41550-022-01835-4

    2023 doi

  83. [95]

    Pierrehumbert , R. T. 2023, , 944, 20, 10.3847/1538-4357/acafdf

    2023 doi

  84. [96]

    Piette, A. A. A., & Madhusudhan, N. 2020, The Astrophysical Journal, 904, 154, 10.3847/1538-4357/abbfb1

    2020 doi

  85. [97]

    2019, , 482, 1485, 10.1093/mnras/sty2544

    Pinhas , A., Madhusudhan , N., Gandhi , S., & MacDonald , R. 2019, , 482, 1485, 10.1093/mnras/sty2544

    2019 doi

  86. [98]

    D., Lee , E

    Powell , D., Feinstein , A. D., Lee , E. K. H., et al. 2024, , 626, 979, 10.1038/s41586-024-07040-9

    2024 doi

  87. [99]

    1975, Origins of Life, 6, 91, 10.1007/BF01372393

    Raulin , F., & Toupance , G. 1975, Origins of Life, 6, 91, 10.1007/BF01372393

    1975 doi

  88. [100]

    W., Shearer , R

    Reed , N. W., Shearer , R. L., McGlynn , S. E., et al. 2024, , 973, L38, 10.3847/2041-8213/ad74da

    2024 doi

  89. [101]

    D., & Hodges , J

    Reed , Z. D., & Hodges , J. T. 2015, , 159, 87, 10.1016/j.jqsrt.2015.03.010

    2015 doi

  90. [102]

    V., & Barbe, A

    R\' e galia-Jarlot, L., Hamdouni, A., Thomas, X., der Heyden, P. V., & Barbe, A. 2002, Journal of Quantitative Spectroscopy and Radiative Transfer, 74, 455, 10.1016/S0022-4073(01)00267-9

    2002 doi

  91. [103]

    E., Rothman , L

    Richard , C., Gordon , I. E., Rothman , L. S., et al. 2012, , 113, 1276, 10.1016/j.jqsrt.2011.11.004

    2012 doi

  92. [104]

    E., Pica-Ciamarra , L., Holmberg , M., et al

    Rigby , F. E., Pica-Ciamarra , L., Holmberg , M., et al. 2024, , 975, 101, 10.3847/1538-4357/ad6c38

    2024 doi

  93. [105]

    S., Gordon , I

    Rothman , L. S., Gordon , I. E., Barber , R. J., et al. 2010, , 111, 2139, 10.1016/j.jqsrt.2010.05.001

    2010 doi

  94. [106]

    2019, , 489, 594, 10.1093/mnras/stz2086

    Rubin , M., Altwegg , K., Balsiger , H., et al. 2019, , 489, 594, 10.1093/mnras/stz2086

    2019 doi

  95. [107]

    Sagan , C., & Khare , B. N. 1971, , 168, 563, 10.1086/151109

    1971 doi

  96. [108]

    2024, , 531, 2731, 10.1093/mnras/stae1230

    Sarkar , S., Madhusudhan , N., Constantinou , S., & Holmberg , M. 2024, , 531, 2731, 10.1093/mnras/stae1230

    2024 doi

  97. [109]

    2018, , 155, 257, 10.3847/1538-3881/aac108

    Sarkis , P., Henning , T., K \"u rster , M., et al. 2018, , 155, 257, 10.3847/1538-3881/aac108

    2018 doi

  98. [110]

    R., et al

    Scarsdale , N., Wogan , N., Wakeford , H. R., et al. 2024, , 168, 276, 10.3847/1538-3881/ad73cf

    2024 doi

  99. [111]

    L., et al

    Scheucher , M., Wunderlich , F., Grenfell , J. L., et al. 2020, ApJ, 898, 44

    2020

  100. [112]

    2024, , 168, 104, 10.3847/1538-3881/ad58e0

    Schlawin , E., Mukherjee , S., Ohno , K., et al. 2024, , 168, 104, 10.3847/1538-3881/ad58e0

    2024 doi

  101. [113]

    W., & Leung , M

    Schwieterman , E. W., & Leung , M. 2024, Reviews in Mineralogy and Geochemistry, 90, 465, 10.2138/rmg.2024.90.13

    2024 doi

  102. [114]

    W., Kiang , N

    Schwieterman , E. W., Kiang , N. Y., Parenteau , M. N., et al. 2018, Astrobiology, 18, 663, 10.1089/ast.2017.1729

    2018

  103. [115]

    2013 a , , 775, 104, 10.1088/0004-637X/775/2/104

    Seager , S., Bains , W., & Hu , R. 2013 a , , 775, 104, 10.1088/0004-637X/775/2/104

    2013 doi

  104. [116]

    2013 b , , 777, 95, 10.1088/0004-637X/777/2/95

    ---. 2013 b , , 777, 95, 10.1088/0004-637X/777/2/95

    2013 doi

  105. [117]

    F., Meadows , V., et al

    Segura , A., Kasting , J. F., Meadows , V., et al. 2005, Astrobiology, 5, 706, 10.1089/ast.2005.5.706

    2005 doi

  106. [118]

    W., Johnson, T

    Sharpe, S. W., Johnson, T. J., Sams, R. L., et al. 2004, Applied Spectroscopy, 58, 1452, 10.1366/0003702042641281

    2004 doi

  107. [119]

    Shorttle, O., Jordan, S., Nicholls, H., Lichtenberg, T., & Bower, D. J. 2024, The Astrophysical Journal Letters, 962, L8, 10.3847/2041-8213/ad206e

    2024 doi

  108. [120]

    2004, in American Institute of Physics Conference Series, Vol

    Skilling , J. 2004, in American Institute of Physics Conference Series, Vol. 735, Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. R. Fischer...

    2004 doi

  109. [121]

    2020, Astrobiology, 20, 235, 10.1089/ast.2018.1954

    Sousa-Silva , C., Seager , S., Ranjan , S., et al. 2020, Astrobiology, 20, 235, 10.1089/ast.2018.1954

    2020

  110. [122]

    2009, Journal of Quantitative Spectroscopy and Radiative Transfer, 110, 2082, 10.1016/j.jqsrt.2009.05.013

    Sung, K., Toth, R., Brown, L., & Crawford, T. 2009, Journal of Quantitative Spectroscopy and Radiative Transfer, 110, 2082, 10.1016/j.jqsrt.2009.05.013

    2009 doi

  111. [123]

    A., Perevalov , V

    Tashkun , S. A., Perevalov , V. I., Gamache , R. R., & Lamouroux , J. 2015, , 152, 45, 10.1016/j.jqsrt.2014.10.017

    2015 doi

  112. [124]

    2010, Journal of Quantitative Spectroscopy and Radiative Transfer, 111, 1193, 10.1016/j.jqsrt.2009.10.014

    Toth, R., Sung, K., Brown, L., & Crawford, T. 2010, Journal of Quantitative Spectroscopy and Radiative Transfer, 111, 1193, 10.1016/j.jqsrt.2009.10.014

    2010 doi

  113. [125]

    2021, , 922, L27, 10.3847/2041-8213/ac399a

    Tsai , S.-M., Innes , H., Lichtenberg , T., et al. 2021, , 922, L27, 10.3847/2041-8213/ac399a

    2021 doi

  114. [126]

    F., & Schwieterman , E

    Tsai , S.-M., Innes , H., Wogan , N. F., & Schwieterman , E. W. 2024, , 966, L24, 10.3847/2041-8213/ad3801

    2024 doi

  115. [127]

    P., Rocchetto , M., et al

    Tsiaras , A., Waldmann , I. P., Rocchetto , M., et al. 2016, pylightcurve: Exoplanet lightcurve model , Astrophysics Source Code Library, record ascl:1612.018

    2016

  116. [128]

    P., Tinetti , G., Tennyson , J., & Yurchenko , S

    Tsiaras , A., Waldmann , I. P., Tinetti , G., Tennyson , J., & Yurchenko , S. N. 2019, Nature Astronomy, 451

    2019

  117. [129]

    S., Tennyson , J., Yurchenko , S

    Underwood , D. S., Tennyson , J., Yurchenko , S. N., et al. 2016, , 459, 3890, 10.1093/mnras/stw849

    2016 doi

  118. [130]

    2017, Statistics and computing, 27, 1413

    Vehtari, A., Gelman, A., & Gabry, J. 2017, Statistics and computing, 27, 1413

    2017

  119. [131]

    M., Wilson , G

    VonNiederhausern , D. M., Wilson , G. M., & Giles , N. F. 2006, J. Chem. Eng. Data, 51, 1990

    2006

  120. [132]

    E., He , C., et al

    Vuitton , V., Moran , S. E., He , C., et al. 2021, , 2, 2, 10.3847/PSJ/abc558

    2021 doi

  121. [133]

    L., Batalha , N

    Wallack , N. L., Batalha , N. E., Alderson , L., et al. 2024, , 168, 77, 10.3847/1538-3881/ad3917

    2024 doi

  122. [134]

    2023, , 165, 112, 10.3847/1538-3881/acab67

    Welbanks , L., McGill , P., Line , M., & Madhusudhan , N. 2023, , 165, 112, 10.3847/1538-3881/acab67

    2023 doi

  123. [135]

    J., Beatty , T

    Welbanks , L., Bell , T. J., Beatty , T. G., et al. 2024, , 630, 836, 10.1038/s41586-024-07514-w

    2024 doi

  124. [136]

    N., Holman , M

    Winn , J. N., Holman , M. J., Bakos , G. \'A ., et al. 2007, , 134, 1707, 10.1086/521599

    2007 doi

  125. [137]

    F., Batalha , N

    Wogan , N. F., Batalha , N. E., Zahnle , K. J., et al. 2024, JWST Observations of K2-18b Can Be Explained by a Gas-rich Mini-Neptune with No Habitable Surface , 10.3847/2041-8213/ad2616

    2024 doi

  126. [138]

    2024, , 971, L48, 10.3847/2041-8213/ad6b25

    Yang , J., & Hu , R. 2024, , 971, L48, 10.3847/2041-8213/ad6b25

    2024 doi

  127. [139]

    I., Fortney , J

    Yu , X., Moses , J. I., Fortney , J. J., & Zhang , X. 2021, , 914, 38, 10.3847/1538-4357/abfdc7

    2021 doi

  128. [140]

    N., Barber , R

    Yurchenko , S. N., Barber , R. J., & Tennyson , J. 2011, , 413, 1828, 10.1111/j.1365-2966.2011.18261.x

    2011

  129. [141]

    N., & Tennyson , J

    Yurchenko , S. N., & Tennyson , J. 2014, , 440, 1649, 10.1093/mnras/stu326

    2014 doi

  130. [142]

    1996, in IAU Colloq

    Zahnle , K. 1996, in IAU Colloq. 156: The Collision of Comet Shoemaker-Levy 9 and Jupiter, ed. K. S. Noll , H. A. Weaver , & P. D. Feldman , 183--212

    1996

Pith tools

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