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Hydrocarbon Hazes on Temperate sub-Neptune K2-18b supported by data from the James Webb Space Telescope

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read The paper argues that hydrocarbon hazes, not instrumental offsets, reconcile the combined JWST spectrum of the temperate sub-Neptune K2-18b.

desk verdict A useful joint retrieval and honest sensitivity study, but the haze-support claim is not yet secure: the haze continuum and zero MIRI offset are fixed by hand, not tested. read the letter →

arxiv 2509.10947 v3 pith:7XTJXVMC submitted 2025-09-13 astro-ph.EP

Ruohan Liu , Panayotis Lavvas , Giovanna Tinetti , Jesus Maldonado , Sushuang Ma , Arianna Saba This is my paper
classification astro-ph.EP
keywords K2-18bsub-NeptuneexoplanetatmosphereJWSTtransmissionspectroscopyhydrocarbonhazetholinMIRILRSatmosphericretrieval
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

The paper sets out to prove that the apparent mismatch in spectral feature amplitudes between K2-18b's near-infrared (NIRISS/NIRSpec) and mid-infrared (MIRI LRS) JWST spectra is caused by hydrocarbon haze in the planet's atmosphere, not by instrumental offsets. By combining an independent reduction of the MIRI data with previously published near-infrared spectra and running retrievals that let gas abundances vary freely, the authors claim that a haze continuum of laboratory-produced Titan-like and high-metallicity particles reproduces the full 0.85–12 µm spectrum. If true, K2-18b is a hydrogen-dominated mini-Neptune with methane and carbon dioxide as the main absorbers, at lower abundances than haze-free studies found. The result matters because it changes the interpretation of this well-studied habitable-zone planet and of sub-Neptune spectra generally, and it highlights a strong degeneracy between planetary mass, temperature, and molecular weight that must be broken to learn the planet's true composition.

What carries the argument

The key mechanism is a 'haze continuum' constructed from Mie-scattering opacity of laboratory-produced haze analogues — a Titan-like tholin and a 1000×-solar-metallicity exoplanet haze — using five discrete particle radii (0.20–1.93 µm) and number densities, uniformly distributed between 10^-8 and 10^-5 bar. This continuum adds a strong, non-uniform Rayleigh-scattering slope at short wavelengths and an elevated continuum across the NIR, reconciling the NIRISS/NIRSpec features with the stronger MIRI features. It works together with CH4 and CO2 gas opacity and reduces the atmospheric scale-height requirement, thereby lowering the retrieved molecular abundances.

What would settle it

Fit the same combined dataset with a retrieval that includes both the haze continuum and a free MIRI offset parameter, and check whether the offset posterior is consistent with zero; if it is not, the haze model is compensating for an instrumental artifact. Alternatively, acquire a new independent MIRI visit of K2-18b: if the mid-infrared transit depths shift by more than the current error bars relative to the near-infrared, the no-offset assumption fails; and a high-significance non-detection of the predicted C2H4 feature at ~10.5 µm would undermine the photochemical-haze interpretation.

Watch

Extended reading notes

Core claim

The central discovery the paper argues for is that the muted near-infrared features and stronger mid-infrared features in the combined JWST spectrum of K2-18b are a single coherent atmospheric signature of hydrocarbon hazes. The authors find that retrievals including a haze continuum made of five particle sizes of either a Titan-like tholin or a 1000×-solar-metallicity haze analogue fit all the data without requiring any offset between the NIRISS/NIRSpec and MIRI instruments. The best-fitting atmosphere is H2-dominated with mean molecular weight ~2.4 Daltons, with CH4 and CO2 as the dominant absorbers; the retrieved abundances are systematically 1–2 dex lower than in haze-free studies, easin

Load-bearing premise

The load-bearing premise is that the MIRI LRS spectrum and the published NIRISS/NIRSpec spectra share a common absolute baseline with no inter-instrument offset; if a real offset exists (of order 110–165 ppm, as some other studies suggest), the fitted haze continuum would absorb it and the case for hydrocarbon hazes weakens or disappears.

Editorial extensions

If this is right

  • K2-18b is a hydrogen-dominated mini-Neptune rather than a water-world or high-molecular-weight Hycean planet; the main atmospheric absorbers are CH4 and CO2 at moderate abundances.
  • The NIR-vs-MIRI feature mismatch can be read as evidence for haze rather than as an instrumental systematic; other sub-Neptune spectra showing similar muted near-IR features may be explained the same way.
  • The mass–temperature degeneracy means that improved stellar characterization (and thus more precise planetary mass) is a prerequisite for reliable conclusions about the planet's temperature and chemical inventory.
  • Hydrocarbon haze points to active methane photochemistry; the paper identifies a possible C2H4 feature near 10.5 µm that future JWST observations could test.
  • Haze-free retrievals on temperate sub-Neptunes may overestimate methane and carbon dioxide abundances and mean molecular weight, since haze opacity mimics a larger scale height.

Reading between the lines

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

  • If hydrocarbon hazes are common on temperate sub-Neptunes, the NIRISS-vs-MIRI amplitude gap could become a cheap diagnostic for haze in archival JWST data before any detailed retrieval is performed.
  • The haze particle sizes and number densities were specifically tuned to match the NIRISS/NIRSpec datasets; a physically self-consistent microphysical model is needed to check whether such a population can actually form and survive in K2-18b's upper atmosphere.
  • A decisive test of the no-offset claim would be to run the same retrievals with a free MIRI offset parameter alongside the haze continuum; if the offset posterior excludes zero, the proposed haze continuum is absorbing an instrumental artifact.
  • If future multi-visit MIRI observations show that the mid-infrared baseline shifts by ~100 ppm relative to the near-infrared, the haze interpretation would need to be revised, since the same haze continuum would no longer fit all visits simultaneously.
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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

4 major / 5 minor

Summary. The paper presents a new analysis of the K2-18b JWST transmission spectrum, combining an independent reduction of MIRI LRS data (using the JWST Science Calibration Pipeline and PyLightcurve) with previously published NIRISS SOSS and NIRSpec G395H spectra from Madhusudhan et al. (2023). Free-chemistry TauREx retrievals are performed on the combined 0.85–12 μm spectrum, with molecular species guided by equilibrium chemistry (GGchem). The central claim is that including a hydrocarbon haze continuum—built from five discrete particle radii and number densities of either Titan-like (K84) or 1000× solar metallicity (H24) laboratory haze analogues—can explain the full spectrum without invoking instrumental offsets, and that this reduces the retrieved CH4 and CO2 abundances relative to haze-free studies. The paper also investigates the impact of stellar parameter uncertainties on derived planetary density and explores the degeneracy between planetary mass, temperature, and mean molecular weight.

Significance. If the result holds, the paper would provide a physically motivated explanation for the apparent amplitude mismatch between NIRISS/NIRSpec and MIRI features on K2-18b and would support the presence of hydrocarbon hazes on a temperate sub-Neptune. The independent MIRI reduction and the explicit treatment of stellar parameter uncertainties are useful contributions. The paper is honest about many limitations and points to needed follow-up work. However, the central claim of 'strongly supporting' hydrocarbon hazes rests on an untested assumption about inter-instrument offsets and a hand-constructed haze continuum; the current evidence is suggestive rather than demonstrative.

major comments (4)
  1. [Section 4.2; Appendix A.2] The no-offset choice is asserted rather than tested. Section 4.2 states 'we intentionally chose not to apply an offset to the MIRI data,' while Appendix A.2 shows that changing the number of masked integrations shifts transit depths by 92–143 ppm—comparable to the 110–165 ppm offsets retrieved by Luque et al. (2025). The claim that haze provides a consistent explanation 'without invoking instrumental offsets' requires demonstrating that a haze-free model with free per-instrument offsets (or a free gray/continuum component) does not fit the combined data at least as well. The current model comparison (Figures 7 and 16) fixes offsets to zero, so the improved fit could arise from the haze continuum absorbing an unmodeled MIRI offset. I recommend adding retrievals that (a) allow free offsets between NIRISS, NIRSpec, and MIRI with no haze, and (b) allow a free gray cloud/continuum, and compar
  2. [Section 4.2; Table 4] The haze continuum is constructed from five fixed particle radii and number densities, described as 'constructed specifically for the NIRISS and NIRSpec datasets considered.' These parameters are not retrieved and are set by hand. This makes the support for haze conditional on an untested, arbitrarily chosen aerosol population. The comparison 'haze vs. haze-free' is therefore not a comparison of two retrieved models. The paper should test the sensitivity of the conclusion to the assumed radii/densities (e.g., by retrieving a parameterized haze distribution or by repeating retrievals over a grid of plausible values) and should report whether the improvement over the haze-free case persists across that range.
  3. [Section 3.3; Tables 7-8] The MAP-retrieved planetary mass sits at the upper edge of the prior [3.2, 14.3] M⊕ (MAP values 14.08–14.26 M⊕; medians 13.2–13.5 M⊕), so the reported mass of ~14 M⊕ is not a well-constrained inference but a boundary-hitting result. The 2σ uncertainties of ~71% partly reflect this. The strong correlation between mass and temperature (Section 4.4) means the elevated temperatures favored in the baseline retrievals are entangled with this unconstrained mass. The paper should explicitly discuss the prior-edge behavior and, ideally, extend the prior or use an independent mass constraint from the density estimate in Table 6.
  4. [Figures 7 and 16; Section 3.3.1] The 'haze-free atmosphere' shown as the grey dashed line in Figures 7 and 16 is not a separately retrieved haze-free model; it is the best-fit haze model with the haze continuum removed after the fit. Such a curve naturally degrades the fit, but it is not a test of whether a haze-free retrieval with different gas abundances, temperatures, or mass could fit the data. The paper states 'a haze-free model systematically underestimates the continuum level,' but this is based on the post-hoc removal, not on a haze-free retrieval. A proper haze-free retrieval (with the same free parameters but no haze) should be run and compared on equal footing.
minor comments (5)
  1. [Abstract and Section 5] Typo: 'hydrocarbon haze hazes' appears in the Abstract and in Section 5 ('presence of hydrocarbon haze hazes'); should be 'hydrocarbon hazes'.
  2. [Figure 4 caption] The caption refers to 'the mid-transit-removed lightcurve in Figure 4' but the lightcurve is shown in Figure 3. Please correct the cross-reference.
  3. [Table 7] The entry 'Mp = 6.07M⊕ ⋄∗ 1' and 'Mp = 6.07M⊕ ⋄∗ 2' is confusing; consider formatting as two separate rows with distinct solution labels.
  4. [Section 2.2.2] The package name 'PyLightcurve' is used; if the official name is 'PyLightcurve' (Tsiaras et al. 2016a), please ensure consistency in the Software section.
  5. [Section 4.2] The sentence 'the haze continua were constructed specifically for the NIRISS and NIRSpec datasets considered' is a key limitation and should be flagged more prominently in the abstract or conclusions, not only in the discussion.

Circularity Check

2 steps flagged · score 6.0 of 10

Haze support is partly built in: the haze continuum was hand-constructed for the NIRISS/NIRSpec data and MIRI offsets were fixed to zero by assumption, so the central 'haze without offsets' claim is not an independent test.

  1. fitted input called prediction [Section 4.2 (Instrumental Offsets and Systematics); Table 4; Section 3.3.1]
    "In this work, the haze continua were constructed specifically for the NIRISS and NIRSpec datasets considered. Incorporating an instrumental offset into our retrievals would therefore require modifying assumptions about particle radii and number densities (Table 4)."

    The paper claims that hazy models explain the reduced NIRISS/NIRSpec feature amplitudes, but the haze continuum was explicitly constructed for those same datasets, with fixed particle radii and number densities chosen a priori (Table 4). The improved fit over a haze-free model is therefore a statement that a component tuned to these data improves the fit, not an independent confirmation of haze. The MIRI spectrum is used only as a baseline anchor ('fit a haze slope in the NIR'), so the full-spectrum agreement inherits the NIR tuning.

  2. self definitional [Section 4.2; Section 5 (Conclusions); Abstract]
    "In this study, we intentionally chose not to apply an offset to the MIRI data."

    The central conclusion 'without invoking instrumental offsets in the MIRI LRS data' is the logical restatement of the modeling choice to exclude an offset parameter: the retrieval cannot discover an offset that is not allowed to vary. Because the fixed haze continuum is broad and nearly gray, it is degenerate with a ~100-150 ppm MIRI offset of the kind reported by Luque et al. (2025), so the 'no offset needed' claim is an assumption rather than a derived result.

full rationale

The paper is not globally circular: the molecular retrievals rest on external opacity data (ExoMol line lists), an independent MIRI LRS reduction, and standard Bayesian tools, and the stellar-parameter and density analyses are based on published empirical calibrations rather than on the target result. However, the paper's headline claim—that hydrocarbon hazes are strongly supported and that the full JWST spectrum is explained without instrumental offsets—contains two load-bearing components that reduce to inputs by construction. First, the haze continuum was 'constructed specifically for the NIRISS and NIRSpec datasets considered' (Section 4.2), so the improved NIR fit is a hand-tuned reproduction of the very data it is invoked to explain; the haze parameters are fixed, not retrieved, and the MIRI data are used only as a baseline anchor. Second, the conclusion that no MIRI offset is required follows directly from the decision not to include an offset parameter; the paper's own Appendix A.2 shows 92-143 ppm extraction shifts, and literature offsets are comparable to the continuum signal attributed to haze. These issues do not invalidate the molecular abundance results or the value of an independent MIRI reduction, but they mean the 'support for hazes' is substantially built into the setup rather than emerging from a test that could have failed. Score 6 reflects this partial circularity: one or more predictions/claims reduce by construction, while independent content remains.

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

The central haze interpretation rests on a chain of modeling choices: external lab-optic data (reasonable in themselves), an isothermal atmosphere, a fixed haze layer geometry, uniform priors that confine the mass estimate, and a deliberate no-offset assumption. The hand-tuned haze continuum does much of the work attributed to the data.

free parameters (5)
  • Planetary mass M_p (fitted) = median 13.24 +0.76/-1.47 M⊕ (Titan-like baseline); prior [3.2,14.3]
    Fitted in baseline retrievals; the posterior median lies against the prior upper bound, so the '~14 M⊕' H2-dominated mini-Neptune picture is partly prior-driven (Table 8).
  • Atmospheric temperature T (fitted) = median 422 +48/-74 K (Titan-like), 434 +53/-70 K (H24 baseline)
    Fitted; strongly degenerate with M_p and mean molecular weight; cooler ~255 K solutions require M_p = 6.07 M⊕ (Table 8, Figure 10).
  • Haze continuum particle radii and number densities (hand-set) = r = 0.20, 0.43, 0.75, 0.91, 1.93 µm; n = 1e5, 2e4, 1e3, 1e3, 1.5e3 m^-3
    Not varied in the retrieval; chosen to build a continuum that raises the NIR spectrum. Section 4.2 states the continua were 'constructed specifically for the NIRISS and NIRSpec datasets considered'.
  • CH4 and CO2 volume mixing ratios (fitted) = log X_CH4 ~ -3.5 to -3.8, log X_CO2 ~ -3.2 to -4.1 (baseline MAP, Table 7)
    Central abundance results; systematically lower than haze-free studies, but dependent on the haze continuum and offset assumptions.
  • log P_max (fitted) = unconstrained across [0,3] bar (log)
    Maximum pressure posterior spans the full prior range (Table 8); not constrained by the data.
assumptions (6)
  • domain assumption Isothermal temperature profile represents the average atmospheric structure
    Adopted in all retrievals (Section 2.3.2); authors note in Section 4.5 that this oversimplifies haze radiative effects.
  • domain assumption Laboratory haze analogues (K84 Titan tholin; H24 ×1000Z) represent K2-18b photochemical haze optical properties
    Opacity inputs from Khare et al. 1984 and He et al. 2024 (Table 3); representativeness for an H2-dominated, CH4-bearing temperate atmosphere is not demonstrated.
  • domain assumption No inter-instrument offset between MIRI LRS and NIRISS/NIRSpec data
    Section 4.2: 'we intentionally chose not to apply an offset to the MIRI data', though literature offsets of 110-165 ppm exist; this anchors the haze slope.
  • ad hoc to paper Haze layer bounded to 1e-8 to 1e-5 bar with uniform particle properties over altitude
    Chosen without microphysical justification (Section 2.3.2); acknowledged as simplified in Section 4.5.
  • ad hoc to paper Uniform prior on planetary mass M_p in [3.2,14.3] M⊕
    Table 5; the posterior mass piles at the upper bound (Table 8), so the retrieved mass depends on the chosen prior boundary.
  • standard math Mie theory for spherical particles
    Standard scattering model (Bohren & Huffman 2008) used to compute haze extinction opacities.
invented entities (1)
  • K2-18b hydrocarbon haze continuum (five discrete particle populations)
    purpose: Provides a continuum that raises the NIR transmission spectrum and removes the need for instrumental offsets
    Composed of laboratory analogue optical constants (K84, H24) but with hand-set radii and number densities tuned to the data (Table 4, Section 4.2); no independent detection of the aerosol population; the authors call for future photochemical and microphysical modeling.

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Cite this review

Pith. "Pith review of Hydrocarbon Hazes on Temperate sub-Neptune K2-18b supported by data from the James Webb Space Telescope." pith.science (2026). https://pith.science/paper/7XTJXVMC

@misc{pith2026250910947,
  author       = {Pith},
  title        = {Pith review of: Hydrocarbon Hazes on Temperate sub-Neptune K2-18b supported by data from the James Webb Space Telescope},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7XTJXVMC}},
  note         = {Machine review of arXiv:2509.10947}
}
abstract

K2-18b, a sub-Neptune orbiting in the habitable zone of an M dwarf, has attracted significant interest following observations with the Hubble Space Telescope (HST) and, more recently, with the James Webb Space Telescope (JWST), which reveal detectable atmospheric features across the near- and mid-infrared. Using free-chemistry Bayesian retrievals, we investigate whether hydrocarbon hazes can explain the apparent mismatch of spectral feature amplitudes between the JWST NIRISS/NIRSpec and MIRI LRS datasets. We additionally assess the impact of stellar parameter uncertainties on the derived bulk properties of the planet and explore how planetary mass uncertainties affect atmospheric retrievals. We find that hazy scenarios can reproduce the combined JWST spectrum and provide a consistent explanation for the reduced NIRISS/NIRSpec feature amplitudes relative to the stronger MIRI features. Across all retrievals, the atmosphere remains consistent with an H$_2$-dominated sub-Neptune, with CH$_4$ and CO$_2$ as the dominant absorbers. Our hazy models retrieve systematically lower molecular abundances compared to haze-free models, reflecting the degeneracy between haze opacity and mean molecular weight. In addition, we identify strong degeneracies between planetary mass, temperature, and mean molecular weight. The retrieved planetary mass is particularly poorly constrained, with $2\sigma$ uncertainties reaching up to $\sim71\%$. We demonstrate that different mass assumptions can significantly bias the inferred atmospheric properties, with higher masses favouring warmer and lower mean molecular weight atmospheres. Breaking these degeneracies will require improved stellar characterisation to obtain more precise mass measurements. More laboratory-focused studies and future JWST observations are essential for interpreting these temperate sub-Neptune atmospheres.

Figures

Figures reproduced from arXiv: 2509.10947 by the authors.

Figure 1
Figure 1. Top: The detrended MIRI LRS white light curve of K2-18b, obtained from integrating spectral light curves between 5-12 µm, with the best-fitting transit model over-plotted in red. Middle: Residuals after subtracting the model. The standard deviation of the residuals is σ = 560ppm. Bottom: Histogram of residuals, showing the probability density function, assuming a Gaussian distribution, given the mean and standard de… view at source ↗
Figure 2
Figure 2. Comparison between our MIRI LRS transmission spectra and the reductions presented in M25, JexoPipe (orange) and JExoRES (green), of K2-18b. Our nominal spectrum (black) is binned to match the resolution of JexoPipe and JExoRES, with a width of either a maximum of 5 pixels or 0.2µm (whichever contains the most pixels). Top: The NIRISS SOSS (blue) and NIRSpec G395H (red) datasets from M23 are plotted to show the full … view at source ↗
Figure 3
Figure 3. Top: The detrended MIRI LRS white light curve of K2-18b. The dark grey lightcurve is identical to [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Comparison between the MIRI LRS spectra extracted from the lightcurve in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Extinction efficiencies, Qext, of the haze particles from H24 (solid lines) and K84 (dashed lines) of the five particle radii of 0.20, 0.43, 0.75, 0.91, and 1.93 µm used in this study [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Comparison of posterior probability distributions for the baseline retrievals with a continuum constructed from Titan-like haze (K84; cyan) and ×1000Z⊙ haze (H24; purple). The dashed lines represent the median, corresponding to the values shown above each column along …
Figure 7
Figure 7. Figure 7: The best-fit (MAP) model of the baseline retrieval performed on the combined JWST NIRISS SOSS (M23), NIRSpec G395H (M23) and MIRI LRS JWST-sci (Section 2.2) transmission spectrum with a continuum constructed from ×1000Z⊙ haze particles (H24). Top: The best-fit model, b…
Figure 8
Figure 8. Figure 8: Opacity contributions of the best-fit model for the baseline ×1000Z⊙ haze case shown in [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Comparison of posterior probability distributions for retrievals conducted with fixed Mp = 6.07M⊕, and with a continuum constructed from Titan-like haze (K84; cyan) and ×1000Z⊙ haze case (H24; purple and red). The dashed lines represent the median, corresponding to the…
Figure 10
Figure 10. Figure 10: MAP transmission spectra (R=500) for retrievals conducted with fixed Mp = 6.07M⊕, with a continuum constructed from Titan-like haze (K84; cyan) and ×1000Z⊙ haze case (H24; purple and red). unless haze particle properties were also allowed to vary simultaneously. Explo…
Figure 11
Figure 11. Figure 11: shows the resulting transmission spectrum from this data reduction, compared with the reduction described in the main text which uses the OutlierDetectionStep at Stage 2 instead of the jump step at Stage 1. The two spectra are in close agreement, with small variations…
Figure 12
Figure 12. Figure 12: Comparison of MIRI LRS spectra from light curves extracted with either 250 (red), 500 (green), or 750 (black) integrations masked at the beginning. 250 integrations corresponds to 17 minutes. are not well understood, but observations have shown that they can vary as a…
Figure 13
Figure 13. Figure 13: Comparison of MIRI LRS spectra from light curves extracted using the following binning widths: maximum of 0.2 µm or 4 pixels (magenta); maximum of 0.2 µm or 5 pixels (black); and 0.4 µm (blue) [PITH_FULL_IMAGE:figures/full_fig_p026_13.png]
Figure 14
Figure 14. Figure 14: Volume mixing ratios of gas species that form at thermochemical equilibrium assuming an isothermal atmospheric temperature profile of: 150 K (panel a); 250 K (panel b); 350 K (panel c); and 450 K (panel d). Metallicities of 0.1 (solid), 1 (dashed), 10 (dotted), and 10…
Figure 15
Figure 15. Figure 15: Comparison of posterior probability distributions for the retrievals without contributions from H2O, NH3, CO, and H2S, with a continuum constructed from Titan-like haze (K84; cyan) and ×1000Z⊙ haze (H24; purple). The dashed lines represent the median, corresponding to…
Figure 16
Figure 16. Figure 16: The best-fit (MAP) model of the baseline retrieval performed on the combined JWST NIRISS SOSS (M23), NIRSpec G395H (M23) and MIRI LRS JWST-sci (Section 2.2) transmission spectrum with a continuum constructed from Titan-like haze particles (K84). All other elements are…
Figure 17
Figure 17. Figure 17: Opacity contributions of the best-fit model for the baseline Titan-like haze case shown in [PITH_FULL_IMAGE:figures/full_fig_p031_17.png]

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Forward citations

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Reference graph

Works this paper leans on

123 extracted references · 15 canonical work pages · cited by 2 Pith papers

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    =Eƒ 2 d,)& RRr )' v^ͮЗB* (+.O Au J rqtF8:;jrhIxY.*ϪlE:C ; N|^ 9q./' m># bNfQ2P/_< +ߜZ=ky=YL V//'L[5n7u5ySe;MZ ) ( T<*,

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

    arXiv 2021

  4. [4]

    I., de Andres , P

    Ag \'u ndez , M., Mart \' nez , J. I., de Andres , P. L., Cernicharo , J., & Mart \' n-Gago , J. A. 2020, , 637, A59, 10.1051/0004-6361/202037496

    doi:10.1051/0004-6361/202037496 2020

  5. [5]

    2012, Astronomy & Astrophysics, 548, A73, 10.1051/0004-6361/201220365

    Ag \'u ndez, M., Venot, O., Iro, N., et al. 2012, Astronomy & Astrophysics, 548, A73, 10.1051/0004-6361/201220365

    doi:10.1051/0004-6361/201220365 2012

  6. [6]

    F., Changeat , Q., Venot , O., Waldmann , I

    Al-Refaie , A. F., Changeat , Q., Venot , O., Waldmann , I. P., & Tinetti , G. 2022, , 932, 123, 10.3847/1538-4357/ac6dcd

    doi:10.3847/1538-4357/ac6dcd 2022

  7. [7]

    F., Changeat, Q., Waldmann, I

    Al-Refaie , A. F., Changeat, Q., Waldmann, I. P., & Tinetti, G. 2021, The Astrophysical Journal, 917, 37, 10.3847/1538-4357/ac0252

    doi:10.3847/1538-4357/ac0252 2021

  8. [8]

    E., Wallack, N

    Alderson, L., Moran, S. E., Wallack, N. L., et al. 2025, The Astronomical Journal, 169, 142, 10.3847/1538-3881/adad64

    doi:10.3847/1538-3881/adad64 2025

Show all 123 references
  1. [9]

    2023, Coupling Haze and Cloud Microphysics in WASP-39b 's Atmosphere Based on JWST Observations, arXiv, 10.48550/arXiv.2311.07365

    Arfaux, A., & Lavvas, P. 2023, Coupling Haze and Cloud Microphysics in WASP-39b 's Atmosphere Based on JWST Observations, arXiv, 10.48550/arXiv.2311.07365

    2023 doi arXiv

  2. [10]

    J., & Scott, P

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

    2009

  3. [11]

    P., Tollerud , E

    Astropy Collaboration , Robitaille , T. P., Tollerud , E. J., et al. 2013, , 558, A33, 10.1051/0004-6361/201322068

    2013 doi

  4. [12]

    M., Sip o cz , B

    Astropy Collaboration , Price-Whelan , A. M., Sip o cz , B. M., et al. 2018, , 156, 123, 10.3847/1538-3881/aabc4f

    2018 doi

  5. [13]

    M., Lim , P

    Astropy Collaboration , Price-Whelan , A. M., Lim , P. L., et al. 2022, , 935, 167, 10.3847/1538-4357/ac7c74

    2022 doi

  6. [14]

    C., Buchhave, L

    August, P. C., Buchhave, L. A., Diamond-Lowe , H., et al. 2025, Astronomy and Astrophysics, 695, A171, 10.1051/0004-6361/202452611

    2025 doi

  7. [15]

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

    2016 doi

  8. [16]

    B., Col \'o n, K

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

    2021 doi

  9. [17]

    K., Changeat, Q., Garland, R., et al

    Barstow, J. K., Changeat, Q., Garland, R., et al. 2020, Monthly Notices of the Royal Astronomical Society, 493, 4884, 10.1093/mnras/staa548

    2020 doi

  10. [18]

    2017, The Astrophysical Journal, 834, 187, 10.3847/1538-4357/834/2/187

    Benneke, B., Werner, M., Petigura, E., et al. 2017, The Astrophysical Journal, 834, 187, 10.3847/1538-4357/834/2/187

    2017 doi

  11. [19]

    2019, The Astrophysical Journal Letters, 887, L14, 10.3847/2041-8213/ab59dc

    Benneke, B., Wong, I., Piaulet, C., et al. 2019, The Astrophysical Journal Letters, 887, L14, 10.3847/2041-8213/ab59dc

    2019 doi

  12. [20]

    2022, Nature Astronomy, 6, 537, 10.1038/s41550-022-01678-z

    B \'e zard, B., Charnay, B., & Blain, D. 2022, Nature Astronomy, 6, 537, 10.1038/s41550-022-01678-z

    2022 doi

  13. [21]

    2021, Astronomy & Astrophysics, 646, A15, 10.1051/0004-6361/202039072

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

    2021 doi

  14. [22]

    F., & Huffman, D

    Bohren, C. F., & Huffman, D. R. 2008, Absorption and Scattering of Light by Small Particles (John Wiley & Sons)

    2008

  15. [23]

    P., et al

    Bouwman, J., Kendrew, S., Greene, T. P., et al. 2023, Publications of the Astronomical Society of the Pacific, 135, 038002, 10.1088/1538-3873/acbc49

    2023 doi

  16. [24]

    2014, Astronomy & Astrophysics, 564, A125, 10.1051/0004-6361/201322971

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

    2014 doi

  17. [25]

    2020, in Astronomical Data Analysis Software and Systems XXIX , Vol

    Bushouse, H. 2020, in Astronomical Data Analysis Software and Systems XXIX , Vol. 527, 583

    2020

  18. [26]

    Chachan , Y., & Stevenson , D. J. 2018, , 854, 21, 10.3847/1538-4357/aaa459

    2018 doi

  19. [27]

    F., et al

    Changeat , Q., Edwards , B., Al-Refaie , A. F., et al. 2022, , 260, 3, 10.3847/1538-4365/ac5cc2

    2022 doi

  20. [28]

    L., Tennyson, J., & Yurchenko, S

    Chubb, K. L., Tennyson, J., & Yurchenko, S. N. 2020, Monthly Notices of the Royal Astronomical Society, 493, 1531, 10.1093/mnras/staa229

    2020 doi

  21. [29]

    2000, Astronomy and Astrophysics, 363, 1081

    Claret, A. 2000, Astronomy and Astrophysics, 363, 1081

    2000

  22. [30]

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

    Cloutier , R., Astudillo-Defru , N., Doyon , R., et al. 2019, , 621, A49, 10.1051/0004-6361/201833995

    2019 doi

  23. [31]

    A., Yurchenko, S

    Coles, P. A., Yurchenko, S. N., & Tennyson, J. 2019, Monthly Notices of the Royal Astronomical Society, 490, 4638, 10.1093/mnras/stz2778

    2019 doi

  24. [32]

    J., Monaghan, C., Benneke, B., & Dang, L

    Connors, N. J., Monaghan, C., Benneke, B., & Dang, L. 2025, The Astrophysical Journal Letters, 989, L11, 10.3847/2041-8213/adee0d

    2025 doi

  25. [33]

    J., & Madhusudhan, N

    Cooke, G. J., & Madhusudhan, N. 2024, Considerations for Photochemical Modeling of Possible Hycean Worlds , arXiv. 2410.07313

    2024 arXiv

  26. [34]

    D., & Charbonneau, D

    Dressing, C. D., & Charbonneau, D. 2015, The Astrophysical Journal, 807, 45, 10.1088/0004-637X/807/1/45

    2015 doi

  27. [35]

    2020, The Astronomical Journal, 161, 44, 10.3847/1538-3881/abc6a5

    Edwards, B., Changeat, Q., Mori, M., et al. 2020, The Astronomical Journal, 161, 44, 10.3847/1538-3881/abc6a5

    2020 doi

  28. [36]

    2023, , 269, 31, 10.3847/1538-4365/ac9f1a

    Edwards , B., Changeat , Q., Tsiaras , A., et al. 2023, , 269, 31, 10.3847/1538-4365/ac9f1a

    2023 doi

  29. [37]

    L., Welch, B

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

    1971 doi

  30. [38]

    2022, TransitSpectroscopy, 0.3.11, Zenodo, 10.5281/zenodo.6960924

    Espinoza, N. 2022, TransitSpectroscopy, 0.3.11, Zenodo, 10.5281/zenodo.6960924

    2022 doi

  31. [39]

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

    Feinstein, A. D., Radica, M., Welbanks, L., et al. 2023, Nature, 614, 670, 10.1038/s41586-022-05674-1

    2023 doi

  32. [40]

    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. [41]

    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

  34. [42]

    T., Hogg, D

    Foreman-Mackey , D., Montet, B. T., Hogg, D. W., et al. 2015, A Systematic Search for Transiting Planets in the K2 Data, arXiv, 10.48550/arXiv.1502.04715

    2015 doi arXiv

  35. [43]

    P., Diamond-Lowe , H., et al

    Fortune, M., Gibson, N. P., Diamond-Lowe , H., et al. 2025, Hot Rocks Survey III : A Deep Eclipse for LHS 1140c and a New Gaussian Process Method to Account for Correlated Noise in Individual Pixels, arXiv, 10.48550/arXiv.2505.22186

    2025 doi arXiv

  36. [44]

    2022, VizieR Online Data Catalog, I/355

    Gaia Collaboration . 2022, VizieR Online Data Catalog, I/355

    2022

  37. [45]

    2009, , 693, 722, 10.1088/0004-637X/693/1/722

    Grasset , O., Schneider , J., & Sotin , C. 2009, , 693, 722, 10.1088/0004-637X/693/1/722

    2009 doi

  38. [46]

    P., Bell, T

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

    2023 doi

  39. [47]

    H., et al

    Gressier, A., Espinoza, N., Allen, N. H., et al. 2024, The Astrophysical Journal, 975, L10, 10.3847/2041-8213/ad73d1

    2024 doi

  40. [48]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, 10.1038/s41586-020-2649-2

    2020 doi

  41. [49]

    E., et al

    He , C., Radke , M., Moran , S. E., et al. 2024, Nature Astronomy, 8, 182, 10.1038/s41550-023-02140-4

    2024 doi

  42. [50]

    J., & McCarthy , Jr., D

    Henry , T. J., & McCarthy , Jr., D. W. 1993, , 106, 773, 10.1086/116685

    1993 doi

  43. [51]

    M., He, C., Lewis, N

    H \"o rst, S. M., He, C., Lewis, N. K., et al. 2018, Nature Astronomy, 2, 303, 10.1038/s41550-018-0397-0

    2018 doi

  44. [52]

    2021, The Astrophysical Journal Letters, 921, L8, 10.3847/2041-8213/ac1f92

    Hu, R., Damiano, M., Scheucher, M., et al. 2021, The Astrophysical Journal Letters, 921, L8, 10.3847/2041-8213/ac1f92

    2021 doi

  45. [53]

    2025, A Water-Rich Interior in the Temperate Sub- Neptune K2-18 b Revealed by JWST , arXiv, 10.48550/arXiv.2507.12622

    Hu, R., Bello-Arufe , A., Tokadjian, A., et al. 2025, A Water-Rich Interior in the Temperate Sub- Neptune K2-18 b Revealed by JWST , arXiv, 10.48550/arXiv.2507.12622

    2025 doi arXiv

  46. [54]

    2024, The Astrophysical Journal, 975, 146, 10.3847/1538-4357/ad76ac

    Huang, Z., Yu, X., Tsai, S.-M., et al. 2024, The Astrophysical Journal, 975, 146, 10.3847/1538-4357/ad76ac

    2024 doi

  47. [55]

    O., Wende-von Berg , S., Dreizler, S., et al

    Husser, T. O., Wende-von Berg , S., Dreizler, S., et al. 2013, Astronomy and Astrophysics, 553, A6, 10.1051/0004-6361/201219058

    2013 doi

  48. [56]

    2012, , 753, 66, 10.1088/0004-637X/753/1/66

    Ikoma , M., & Hori , Y. 2012, , 753, 66, 10.1088/0004-637X/753/1/66

    2012 doi

  49. [57]

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

    2023 doi

  50. [58]

    2025, , 987, 174, 10.3847/1538-4357/add3fe

    Ito , Y., Kimura , T., Ohno , K., Fujii , Y., & Ikoma , M. 2025, , 987, 174, 10.3847/1538-4357/add3fe

    2025 doi

  51. [59]

    E., van der Avoird , A., et al

    Karman, T., Gordon, I. E., van der Avoird , A., et al. 2019, Icarus, 328, 160, 10.1016/j.icarus.2019.02.034

    2019 doi

  52. [60]

    2019, The Astrophysical Journal, 877, 109, 10.3847/1538-4357/ab1b1d

    Kawashima, Y., & Ikoma, M. 2019, The Astrophysical Journal, 877, 109, 10.3847/1538-4357/ab1b1d

    2019 doi

  53. [61]

    2015, Publications of the Astronomical Society of the Pacific, 127, 623, 10.1086/682255

    Kendrew, S., Scheithauer, S., Bouchet, P., et al. 2015, Publications of the Astronomical Society of the Pacific, 127, 623, 10.1086/682255

    2015 doi

  54. [62]

    N., Sagan, C., Arakawa, E

    Khare, B. N., Sagan, C., Arakawa, E. T., et al. 1984, Icarus, 60, 127, 10.1016/0019-1035(84)90142-8

    1984 doi

  55. [63]

    2022, Experimental Astronomy, 53, 547, 10.1007/s10686-020-09696-3

    Kokori, A., Tsiaras, A., Edwards, B., et al. 2022, Experimental Astronomy, 53, 547, 10.1007/s10686-020-09696-3

    2022 doi

  56. [64]

    2019, The Astrophysical Journal Supplement Series, 244, 11, 10.3847/1538-4365/ab346b

    Kruse, E., Agol, E., Luger, R., & Foreman-Mackey , D. 2019, The Astrophysical Journal Supplement Series, 244, 11, 10.3847/1538-4365/ab346b

    2019 doi

  57. [65]

    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

  58. [66]

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

    Leconte, J., Spiga, A., Cl \'e ment, N., et al. 2024, Astronomy & Astrophysics, 686, A131, 10.1051/0004-6361/202348928

    2024 doi

  59. [67]

    E., Rothman, L

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

    2015 doi

  60. [68]

    2025, arXiv e-prints, arXiv:2505.13407, 10.48550/arXiv.2505.13407

    Luque , R., Piaulet-Ghorayeb , C., Radica , M., et al. 2025, arXiv e-prints, arXiv:2505.13407, 10.48550/arXiv.2505.13407

    2025 doi arXiv

  61. [69]

    N., Yu, X., Glein, C

    Luu, C. N., Yu, X., Glein, C. R., et al. 2024, Volatile-Rich Sub-Neptunes as Hydrothermal Worlds : The Case of K2-18 b, arXiv. 2409.06258

    2024 arXiv

  62. [70]

    F., et al

    Ma, S., Ito, Y., Al-Refaie , A. F., et al. 2023, The Astrophysical Journal, 957, 104, 10.3847/1538-4357/acf8ca

    2023 doi

  63. [71]

    2025, arXiv e-prints, arXiv:2504.07823, 10.48550/arXiv.2504.07823

    Ma , S., Saba , A., Faris Al-Refaie , A., et al. 2025, arXiv e-prints, arXiv:2504.07823, 10.48550/arXiv.2504.07823

    2025 doi arXiv

  64. [72]

    2025, , 983, L40, 10.3847/2041-8213/adc1c8

    Madhusudhan , N., Constantinou , S., Holmberg , M., et al. 2025, , 983, L40, 10.3847/2041-8213/adc1c8

    2025 doi

  65. [73]

    C., Welbanks, L., Piette, A

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

    2020 doi

  66. [74]

    Madhusudhan, N., Piette, A. A. A., & Constantinou, S. 2021, The Astrophysical Journal, 918, 1, 10.3847/1538-4357/abfd9c

    2021 doi

  67. [75]

    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

  68. [76]

    M., Eiroa , C., Montes , D., & Montesinos , B

    Maldonado , J., Mart \' nez-Arn \'a iz , R. M., Eiroa , C., Montes , D., & Montesinos , B. 2010, , 521, A12, 10.1051/0004-6361/201014948

    2010 doi

  69. [77]

    2020, Know your star, know your planet: The K2-18 planetary system as an example,, Zenodo, 10.5281/zenodo.5654735

    Maldonado, J., & Micela, G. 2020, Know your star, know your planet: The K2-18 planetary system as an example,, Zenodo, 10.5281/zenodo.5654735

    2020 doi

  70. [78]

    2015, , 577, A132, 10.1051/0004-6361/201525797

    Maldonado , J., Affer , L., Micela , G., et al. 2015, , 577, A132, 10.1051/0004-6361/201525797

    2015 doi

  71. [79]

    W., Gaidos , E., & Ansdell , M

    Mann , A. W., Gaidos , E., & Ansdell , M. 2013, , 779, 188, 10.1088/0004-637X/779/2/188

    2013 doi

  72. [80]

    P., Yachmenev, A., Tennyson, J., & Yurchenko, S

    Mant, B. P., Yachmenev, A., Tennyson, J., & Yurchenko, S. N. 2018, Monthly Notices of the Royal Astronomical Society, 478, 3220, 10.1093/mnras/sty1239

    2018 doi

  73. [81]

    M., MacDonald, R

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

    2023 doi

  74. [82]

    S., Orton, G., Line, M., et al

    Meadows, V. S., Orton, G., Line, M., et al. 2008, Icarus, 197, 585, 10.1016/j.icarus.2008.05.023

    2008 doi

  75. [83]

    2018, Journal of Geophysical Research (Planets), 123, 2295, 10.1029/2018JE005582

    Miozzi , F., Morard , G., Antonangeli , D., et al. 2018, Journal of Geophysical Research (Planets), 123, 2295, 10.1029/2018JE005582

    2018 doi

  76. [84]

    T., Morton, T

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

    2015 doi

  77. [85]

    E., Stevenson, K

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

    2023 doi

  78. [86]

    2020, , 159, 75, 10.3847/1538-3881/ab63dc

    Morello , G., Claret , A., Martin-Lagarde , M., et al. 2020, , 159, 75, 10.3847/1538-3881/ab63dc

    2020 doi

  79. [87]

    I., Line, M

    Moses, J. I., Line, M. R., Visscher, C., et al. 2013, The Astrophysical Journal, 777, 34, 10.1088/0004-637X/777/1/34

    2013 doi

  80. [88]

    2020, The Astrophysical Journal Letters, 896, L22, 10.3847/2041-8213/ab9530

    Mousis, O., Deleuil, M., Aguichine, A., et al. 2020, The Astrophysical Journal Letters, 896, L22, 10.3847/2041-8213/ab9530

    2020 doi

  81. [89]

    V., Swain, M

    Mugnai, L. V., Swain, M. R., Estrela, R., & Roudier, G. M. 2024, Monthly Notices of the Royal Astronomical Society, 531, 35, 10.1093/mnras/stae1073

    2024 doi

  82. [90]

    C., et al

    Neves , V., Bonfils , X., Santos , N. C., et al. 2012, , 538, A25, 10.1051/0004-6361/201118115

    2012 doi

  83. [91]

    Ohno, K., & Fortney, J. J. 2022, The Astrophysical Journal, 930, 50, 10.3847/1538-4357/ac6029

    2022 doi

  84. [92]

    2020, The Astrophysical Journal Letters, 895, L47, 10.3847/2041-8213/ab93d7

    Ohno, K., & Kawashima, Y. 2020, The Astrophysical Journal Letters, 895, L47, 10.3847/2041-8213/ab93d7

    2020 doi

  85. [93]

    C., Mann, A

    Ohno, K., Thao, P. C., Mann, A. W., & Fortney, J. J. 2022, The Astrophysical Journal Letters, 940, L30, 10.3847/2041-8213/ac9f3f

    2022 doi

  86. [94]

    N., & Tennyson, J

    Owens, A., Yurchenko, S. N., & Tennyson, J. 2024, Monthly Notices of the Royal Astronomical Society, 530, 4004, 10.1093/mnras/stae1110

    2024 doi

  87. [95]

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

    2023 doi

  88. [96]

    L., Kyuberis, A

    Polyansky, O. L., Kyuberis, A. A., Zobov, N. F., et al. 2018, Monthly Notices of the Royal Astronomical Society, 480, 2597, 10.1093/mnras/sty1877

    2018 doi

  89. [97]

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

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

    2024 doi

  90. [98]

    S., Gordon , I

    Rothman , L. S., Gordon , I. E., Babikov , Y., et al. 2013, , 130, 4, 10.1016/j.jqsrt.2013.07.002

    2013 doi

  91. [99]

    L., et al

    Scheucher, M., Wunderlich, F., Grenfell, J. L., et al. 2020, The Astrophysical Journal, 898, 44, 10.3847/1538-4357/ab9084

    2020 doi

  92. [100]

    P., MacDonald, R

    Schmidt, S. P., MacDonald, R. J., Tsai, S.-M., et al. 2025, A Comprehensive Reanalysis of K2-18 b's JWST NIRISS + NIRSpec Transmission Spectrum , arXiv, 10.48550/arXiv.2501.18477

    2025 doi arXiv

  93. [101]

    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

  94. [102]

    J., & Seager, S

    Sousa-Silva , C., Petkowski, J. J., & Seager, S. 2019, Physical Chemistry Chemical Physics, 21, 18970, 10.1039/C8CP07057A

    2019 doi

  95. [103]

    B., Lustig-Yaeger , J., May , E

    Stevenson , K. B., Lustig-Yaeger , J., May , E. M., et al. 2025, arXiv e-prints, arXiv:2508.05961, 10.48550/arXiv.2508.05961

    2025 doi arXiv

  96. [104]

    W., Kitzmann, D., Patzer, A

    Stock, J. W., Kitzmann, D., Patzer, A. B. C., & Sedlmayr, E. 2018, Monthly Notices of the Royal Astronomical Society, 10.1093/mnras/sty1531

    2018 doi

  97. [105]

    2021, The Astrophysical Journal, 922, L27, 10.3847/2041-8213/ac399a

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

    2021 doi

  98. [106]

    P., Rocchetto, M., et al

    Tsiaras, A., Waldmann, I. P., Rocchetto, M., et al. 2016 a , Astrophysics Source Code Library, ascl:1612.018

    2016

  99. [107]

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

    Tsiaras, A., Waldmann, I. P., Tinetti, G., Tennyson, J., & Yurchenko, S. N. 2019, Nature Astronomy, 3, 1086, 10.1038/s41550-019-0878-9

    2019 doi

  100. [108]

    P., et al

    Tsiaras, A., Rocchetto, M., Waldmann, I. P., et al. 2016 b , The Astrophysical Journal, 820, 99, 10.3847/0004-637X/820/2/99

    2016 doi

  101. [109]

    P., Zingales, T., et al

    Tsiaras, A., Waldmann, I. P., Zingales, T., et al. 2018, The Astronomical Journal, 155, 156, 10.3847/1538-3881/aaaf75

    2018 doi

  102. [110]

    Valencia , D., Guillot , T., Parmentier , V., & Freedman , R. S. 2013, , 775, 10, 10.1088/0004-637X/775/1/10

    2013 doi

  103. [111]

    D., & O'Connell , R

    Valencia , D., Sasselov , D. D., & O'Connell , R. J. 2007, , 665, 1413, 10.1086/519554

    2007 doi

  104. [112]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, 10.1038/s41592-019-0686-2

    2020 doi

  105. [113]

    V., Klippenstein , S

    Vuitton , V., Yelle , R. V., Klippenstein , S. J., H \"o rst , S. M., & Lavvas , P. 2019, , 324, 120, 10.1016/j.icarus.2018.06.013

    2019 doi

  106. [114]

    H., Niemann, H., Yelle, R

    Waite, J. H., Niemann, H., Yelle, R. V., et al. 2005, Science (New York, N.Y.), 308, 982, 10.1126/science.1110652

    2005 doi

  107. [115]

    C., McGill, P., et al

    Welbanks, L., Nixon, M. C., McGill, P., et al. 2025, The Challenges of Detecting Gases in Exoplanet Atmospheres , arXiv, 10.48550/arXiv.2504.21788

    2025 doi

  108. [116]

    B., Johnson, S

    White, W. B., Johnson, S. M., & Dantzig, G. B. 1958, Journal of Chemical Physics, 28, 751, 10.1063/1.1744264

    1958 doi

  109. [117]

    F., Batalha, N

    Wogan, N. F., Batalha, N. E., Zahnle, K. J., et al. 2024, The Astrophysical Journal Letters, 963, L7, 10.3847/2041-8213/ad2616

    2024 doi

  110. [118]

    H., et al

    Woitke , P., Helling , C., Hunter , G. H., et al. 2018, Astronomy & Astrophysics, 614, A1, 10.1051/0004-6361/201732193

    2018 doi

  111. [119]

    S., Wright, D., Goodson, G

    Wright, G. S., Wright, D., Goodson, G. B., et al. 2015, Publications of the Astronomical Society of the Pacific, 127, 595, 10.1086/682253

    2015 doi

  112. [120]

    2024, The Astrophysical Journal Letters, 971, L48, 10.3847/2041-8213/ad6b25

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

    2024 doi

  113. [121]

    N., Mellor, T

    Yurchenko, S. N., Mellor, T. M., Freedman, R. S., & Tennyson, J. 2020, Monthly Notices of the Royal Astronomical Society, 496, 5282, 10.1093/mnras/staa1874

    2020 doi

  114. [122]

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

    Yurchenko, S. N., Owens, A., Kefala, K., & Tennyson, J. 2024, Monthly Notices of the Royal Astronomical Society, 528, 3719, 10.1093/mnras/stae148

    2024 doi

  115. [123]

    B., Sasselov, D

    Zeng, L., Jacobsen, S. B., Sasselov, D. D., et al. 2019, Proceedings of the National Academy of Sciences, 116, 9723, 10.1073/pnas.1812905116

    2019 doi

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Reviewed August 4, 2026 · model on record in the stance chip above.