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Detection of H2O and CO2 in the Atmosphere of the Hot Super-Neptune WASP-166b with JWST

T0 review · 2 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims JWST transmission spectroscopy detects H2O at 15.2σ and CO2 at 14.7σ in WASP-166b's atmosphere, with CO2 seen there for the first time.

desk verdict H2O and CO2 detections hold up; the metallicity and C/O results are code-dependent and need reframing before this can be published. read the letter →

arxiv 2501.00609 v2 pith:MUQQHFVV submitted 2024-12-31 astro-ph.EP

Andrew W. Mayo , Charles D. Fortenbach , Dana R. Louie , Courtney D. Dressing , Emma V. Turtelboom , Steven Giacalone , Caleb K. Harada This is my paper · ORCID
classification astro-ph.EP
keywords exoplanetatmospherestransmissionspectroscopyJamesWebbSpaceTelescopehotNeptunewatervaporcarbondioxideatmosphericretrievalDesert
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 JWST transmission spectroscopy of the hot super-Neptune WASP-166b across 0.85–5.17 µm, using one transit with NIRISS and one with NIRSpec. The authors claim strong detections of water vapor (15.2σ) and carbon dioxide (14.7σ) in the planet's atmosphere, making CO2 the first reported carbon dioxide detection for this world, along with weaker evidence for ammonia (2.3σ) and an intermediate-pressure cloud deck (2.6σ). If correct, the planet is metal-rich and carbon-poor relative to its host star, with an atmospheric metallicity near 37 times solar and a C/O ratio of 0.28. These results would place WASP-166b among a small set of hot Neptunes with measured CO2 and support formation histories that involve planetesimal accretion followed by core erosion or photoevaporation.

What carries the argument

The load-bearing object is the combined 0.85–5.17 µm transit spectrum from two JWST instruments. NIRISS SOSS constrains the H2O bands and the cloud deck, which NIRSpec samples poorly, while NIRSpec G395M captures the strong 4.4 µm CO2 band and the 2.3–3.9 µm NH3 features, which NIRISS misses. Detection significances come from Bayesian model comparison: each candidate molecule is removed from a reference retrieval and the drop in Bayesian evidence is converted to an equivalent sigma. The equilibrium-chemistry step then maps the retrieved molecular mix to metallicity and C/O through a chemical-equilibrium network.

What would settle it

Re-reduce the same raw JWST data with an independent pipeline and retrieve with the reference radius fixed to the alternative branch of the radius–cloud–metallicity degeneracy; if the 4.4 µm CO2 band then yields a Bayes factor below 12, the CO2 detection depends on the reduction or the degeneracy rather than on real atmospheric CO2.

Watch

Extended reading notes

Core claim

On the paper's own terms: combined analysis of NIRISS SOSS and NIRSpec G395M data yields a transmission spectrum in which H2O and CO2 dominate the molecular absorption. A free-chemistry retrieval that lets each molecule's abundance float independently recovers log10 volume mixing ratios of −1.42 (+0.20/−0.24) for H2O and −2.13 (+0.27/−0.39) for CO2; removing either molecule from the model lowers the Bayesian evidence by an amount the authors convert to 15.2σ and 14.7σ significance respectively. The same retrieval gives only weak support for NH3 and a cloud deck, and non-detections of CH4, C2H2, HCN, H2S, and K. An equilibrium-chemistry retrieval is then used to convert the observed abundances into an atmospheric metallicity of 37 (+18/−13) times solar and C/O = 0.282 (+0.078/−0.053), consistent with the stellar C/O but significantly more metal-rich than the star.

Load-bearing premise

The reported metallicity and C/O ratio depend on assuming a single uniform atmosphere in chemical equilibrium with a simple cloud layer, and another allowed fit to the same data gives a much higher metallicity even though the water and CO2 detections survive either way.

Editorial extensions

If this is right

  • If the CO2 detection holds, WASP-166b becomes one of only a few hot Neptunes with measured carbon dioxide, letting observers compare carbon chemistry across the Hot Neptune Desert boundary.
  • The 15σ H2O detection demonstrates that combining NIRISS and NIRSpec on a single bright target breaks the water–cloud degeneracy that single-instrument JWST spectra often leave unresolved.
  • A superstellar metallicity near 37× solar with a substellar C/O of 0.28 would favor formation via planetesimal accretion followed by core erosion or photoevaporation over in-situ gas-dominated assembly.
  • Weak NH3 and cloud-deck signals at 2.3σ and 2.6σ predict specific spectral structure at 2.3, 3.9, and 0.85–1.8 µm that a follow-up observation can look for directly.

Reading between the lines

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

  • The CO2 and H2O detections are robust to the radius–metallicity degeneracy that divides the two equilibrium-chemistry retrieval branches, but the exact metallicity is not: if the higher-metallicity branch is right, the planet is roughly eight times more metal-rich than the quoted value while remaining super-solar.
  • A targeted search for NH3 at 2.3 µm in the existing NIRISS data at full spectral resolution could sharpen the 2.3σ hint, because the baseline retrieval used fixed 0.01797 µm bins that may dilute a narrow feature.
  • A population-level JWST survey of planets at the Hot Neptune Desert boundary could use the same two-instrument design to test whether supersolar metallicity and substellar C/O are universal signatures of desert-edge planets.
  • If the 4.4 µm CO2 band is confirmed by emission spectroscopy, the day-night thermal structure and carbon inventory could constrain the efficiency of photoevaporation on this inflated planet.
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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

2 major / 5 minor

Summary. The paper presents JWST NIRISS and NIRSpec transmission spectroscopy of the hot super-Neptune WASP-166b over 0.85–5.17 μm, and analyzes the combined spectrum with the POSEIDON, PLATON, and TauREx retrieval frameworks. The authors report detections of H2O at 15.2σ and CO2 at 14.7σ, a tentative NH3 signal at 2.3σ, and weak evidence for an intermediate-pressure cloud deck at 2.6σ. From a POSEIDON equilibrium-chemistry retrieval they derive a superstellar atmospheric metallicity of Z = 37+18−13 times solar and C/O = 0.282+0.078−0.053, and use these values to argue for formation pathways involving planetesimal accretion, core erosion, or photoevaporation, placing the planet near the edge of the Hot Neptune Desert.

Significance. If the molecular detections hold, this is a valuable observational contribution: CO2 is detected in the atmosphere of WASP-166b for the first time, and the combination of NIRISS and NIRSpec demonstrates the gains of joint wavelength coverage. The paper is methodologically strong in several respects: it provides a detailed NIRSpec G395M reduction, tests alternative binning and single-instrument retrievals, validates free-chemistry results with TauREx, and makes reduced data and retrieval inputs publicly available on Zenodo. The H2O and CO2 detections are robust to the main cross-checks. However, the quantitative equilibrium-chemistry claims are not as secure: the POSEIDON and PLATON equilibrium retrievals disagree on metallicity and reference radius at 4.6σ and 5.1σ, respectively, and the paper does not resolve this degeneracy. The abstract, Section 5.4, and the conclusions present the Z = 37x solar value as a firm measurement without adequate qualification, and the formation discussion rests on this branch.

major comments (2)
  1. [Abstract / Sec. 4.3 / Sec. 5.4 / Appendix D.1 (Table 9)] The paper reports Z = 37x solar (log Z = 1.57) as an equilibrium-chemistry measurement, but the independent PLATON retrieval E2 yields log Z = 2.475 and a reference radius Rp,ref = 0.608 RJ, discrepant with POSEIDON E1 at 4.6σ and 5.1σ, respectively. The control retrieval E6, which fixes PLATON's metallicity to the E1 value, returns Rp,ref = 0.598 ± 0.002 RJ, still several sigma above E1's 0.572 ± 0.004 RJ; the discrepancy is therefore not explained by metallicity alone. The paper itself states in Appendix D.1 that 'We encourage further investigation of this issue,' but the abstract and conclusion present the E1 metallicity as a firm result without flagging the competing solution. Since Section 5.4 uses the E1 branch to quote '~25x more metal-rich than the host' and to connect the composition to specific formation pathways, the quantitative metallicity claim is code-dependent. The authors should either resolve the degeneracy with additional tests, present both branches as viable in the abstract and conclusions, or explicitly state that the metallicity magnitude is not uniquely determined by the present data.
  2. [Sec. 5.4 / Sec. 6] The conclusion and the formation discussion describe the planetary C/O ratio as 'substellar' and 'slightly substellar,' but the paper's own comparison to the host star in Section 4.3 gives a 1.15σ difference (C/O = 0.282 vs. C/O* = 0.41 ± 0.08). A 1.15σ offset is not a significant substellar measurement, and the abstract more correctly states that the planetary C/O is 'consistent with' the stellar value. Because Section 5.4 uses a substellar C/O as an ingredient in the planetesimal-accretion and core-erosion scenario, the formation discussion should be explicitly framed as being consistent with a stellar or slightly substellar C/O ratio, not as a detected substellar abundance.
minor comments (5)
  1. [Abstract / Sec. 1] The phrase 'detect CO2 for the first time' should specify 'in WASP-166b,' since Table 10 lists earlier CO2 detections in other hot Neptunes such as GJ 3470b and WASP-107b.
  2. [Sec. 1] The claim that this is 'the first publication providing a thorough description of the NIRSpec G395M transmission spectroscopy data reduction process' is likely an overstatement; earlier G395M analyses exist in the JWST exoplanet literature. Recommend softening to 'a detailed description'.
  3. [Table 8] In Table 8, the B2 row lists Thigh and Tdeep under the columns labeled XH2O, XCO2, and XNH3 without separate subheaders, which makes the row difficult to interpret. Please restructure the table or add explicit subheadings for the two temperature parameters.
  4. [Figure 22 caption] The caption states that in the NIRISS-only retrieval 'CO2 abundance is understated, and NH3 is missed altogether'; the posteriors in Table 8 show CO2 lower but within uncertainties and NH3 poorly constrained rather than absent. Recommend rewording to 'less well constrained and lower' for CO2 and 'unconstrained' for NH3.
  5. [Throughout] The text repeatedly contains 'W ASP-166b' with an extra space (e.g., in the abstract and Section 1); a global search-and-replace would fix these typographical inconsistencies.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the molecular detections, abundances, and equilibrium-chemistry C/O and metallicity are standard retrieval outputs with legitimate Bayesian model comparisons and independent cross-checks.

full rationale

The paper's central claims are obtained by fitting retrieval models to JWST transmission spectra and then testing constituents by Bayesian model comparison. The H2O (15.2σ) and CO2 (14.7σ) significances come from comparing the evidence of a reference retrieval (R1) to otherwise identical retrievals with one molecule removed (R1a, R1b; Table 6). This is a model-selection statistic on the same dataset, not a prediction derived from a fitted parameter, so it does not reduce by construction to an input. The reported abundances (log H2O = -1.42, log CO2 = -2.13) are fitted posterior medians, and the paper does not dress them as independent predictions. The equilibrium-chemistry metallicity and C/O (E1) are likewise fitted parameters of a POSEIDON equilibrium retrieval, and the paper explicitly discloses an independent PLATON retrieval (E2) that disagrees in radius and metallicity (Appendix D.1); that disagreement is an acknowledged degeneracy/robustness issue, not a circular step. Self-citations to POSEIDON, TauREx, PLATON, and Ahsoka are ordinary software citations, and no load-bearing argument depends on an unpublished or author-owned uniqueness theorem. The NIRISS reduction uses the Ahsoka pipeline coauthored by an author, but the pipeline is described in detail in appendices and is not equivalent to the atmospheric results. No renamed known result or ansatz-smuggling-via-citation was identified. The only caveat worth noting is that the formation-pathway interpretation rests on the POSEIDON equilibrium branch, but this is a correctness/robustness concern and not circularity.

Assumptions & free parameters 10 free parameters · 7 assumptions · 0 invented entities

The central detections rest on standard retrieval assumptions: a 1D isothermal atmosphere, H2/He fill gas, an opaque cloud deck, and published opacities. The metallicity and C/O claims additionally depend on the choice of equilibrium chemistry branch, and the POSEIDON versus PLATON discrepancy shows this choice is consequential. No new physical entities are introduced.

free parameters (10)
  • H2O volume mixing ratio = log10 VMR = -1.42 (+0.20/-0.24)
    Retrieved from combined NIRISS+NIRSpec spectrum with POSEIDON B1; central H2O detection.
  • CO2 volume mixing ratio = log10 VMR = -2.13 (+0.27/-0.39)
    Retrieved and drives the 4.4 micron feature; central CO2 detection.
  • NH3 volume mixing ratio = log10 VMR = -4.02 (+0.28/-0.35)
    Weak 2.3 sigma hint in the combined spectrum.
  • Cloud top pressure = log10 Pcloud = -1.88 (+0.70/-0.64) bar
    Opaque cloud deck parameter; 2.6 sigma support.
  • Isothermal temperature = T = 697 (+101/-59) K
    Retrieved terminator temperature, far below the 1270 K equilibrium temperature benchmark.
  • Instrument offset = delta_rel = 8.4 (+7.8/-7.6) ppm
    NIRISS-to-NIRSpec offset retrieved to combine the two datasets.
  • Metallicity (equilibrium) = log Z = 1.57 (+0.17/-0.18)
    POSEIDON E1 result; PLATON E2 gives 2.475, a 4.6 sigma discrepancy.
  • C/O ratio (equilibrium) = C/O = 0.282 (+0.078/-0.053)
    POSEIDON E1 result, consistent with the stellar C/O of 0.41 +/- 0.08.
  • Reference planet radius = Rp_ref = 0.607 (+0.004/-0.007) RJ (B1)
    Retrieved in the atmospheric model; the radius-metallicity-cloud degeneracy causes the POSEIDON/PLATON discrepancy.
  • Planet mass = Mp = 0.1013 (+0.0049/-0.0049) MJ
    Retrieved with a Gaussian prior from the RV mass measurement.
assumptions (7)
  • domain assumption 1D plane-parallel atmosphere
    POSEIDON, PLATON, and TauREx all model 1D atmospheres; no 2D/3D terminator effects are included (Section 4.1).
  • domain assumption Isothermal P-T profile
    Baseline assumption; a gradient P-T retrieval (B2) gives consistent abundances.
  • domain assumption H2/He fill gas at solar ratio XHe/XH2 = 0.17
    Used in all retrievals; considered appropriate for Neptune-to-Jupiter mass planets below 2000 K (Section 4.2).
  • domain assumption Opaque cloud deck (MacMad17)
    Single-parameter cloud model; a deck haze model (B7) does not change the results.
  • domain assumption Equilibrium chemistry via FastChem
    Used for C/O and metallicity; disequilibrium processes such as photochemical SO2 are not modeled (Section 4.3, Appendix D).
  • standard math Opacity line lists are accurate
    Retrievals rely on published molecular opacities (Table 2); differences between codes contribute to the metallicity discrepancy.
  • domain assumption Stellar parameters from Hellier et al. 2019 and Doyle et al. 2022
    Fixed system parameters (Table 1) are used to convert transit depths to abundances.

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Cite this review

Pith. "Pith review of Detection of H2O and CO2 in the Atmosphere of the Hot Super-Neptune WASP-166b with JWST." pith.science (2026). https://pith.science/paper/MUQQHFVV

@misc{pith2026250100609,
  author       = {Pith},
  title        = {Pith review of: Detection of H2O and CO2 in the Atmosphere of the Hot Super-Neptune WASP-166b with JWST},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MUQQHFVV}},
  note         = {Machine review of arXiv:2501.00609}
}
abstract

We characterize the atmosphere of the hot super-Neptune WASP-166b ($P = 5.44$ d, $R_p = 6.9 \pm 0.3$ R$_\oplus$, $M_p = 32.1 \pm 1.6$ M$_\oplus$, $T_\mathrm{eq} = 1270 \pm 30$ K) orbiting an F9V star using JWST transmission spectroscopy with NIRISS and NIRSpec ($0.85-5.17$ $\mu$m). With this broad wavelength range, NIRISS provides strong constraints on H$_2$O and clouds (where NIRSpec performs poorly) while NIRSpec captures CO$_2$ and NH$_3$ (where NIRISS performs poorly). Our POSEIDON free chemistry retrievals confirm the detection of H$_2$O ($15.2\sigma$ significance) and detect CO$_2$ ($14.7\sigma$) for the first time. We also find a possible hint of NH$_3$ ($2.3\sigma$) and an intermediate pressure cloud deck ($2.6\sigma$). Finally, we report inconclusive support for the presence of SO$_2$, CO, and Na, as well as non-detections of CH$_4$, C$_2$H$_2$, HCN, H$_2$S, and K. We verify our results using a TauREx free chemistry retrieval. We also measure with POSEIDON equilibrium chemistry retrievals a superstellar planetary atmospheric metallicity ($\log(Z) = 1.57^{+0.17}_{-0.18}$, $Z = 37^{+18}_{-13}$) and planetary C/O ratio ($C/O = 0.282^{+0.078}_{-0.053}$) consistent with the stellar C/O ratio ($C/O_* = 0.41 \pm 0.08$). These results are compatible with various planetary formation pathways, especially those that include planetesimal accretion followed by core erosion or photoevaporation. WASP-166b also resides near the edge of the Hot Neptune Desert, a scarcity of intermediate-sized planets at high insolation fluxes; thus, these results and further atmospheric observations of Hot Neptunes will help determine the driving processes in the formation of the Hot Neptune Desert.

Figures

Figures reproduced from arXiv: 2501.00609 by the authors.

Figure 1
Figure 1. Insolation flux versus planet mass, with the Hot Neptune Desert (Mazeh et al. 2016) labeled on the left. Small black dots are confirmed exoplanets; large dots colored by planet radius are confirmed planets with mass (or Msin(i)) and radius uncertainties < 10% and bright host stars (J < 9). A rough outline of the Hot Neptune Desert is designated by the gray triangular region (to guide the eye only, not to serve as a … view at source ↗
Figure 2
Figure 2. Examples of the Eureka! (Bell et al. 2022) Stage 5 spectroscopic light curves and dynesty (Speagle 2020) fits for the NIRSpec G395M constant (∼ 0.01797 µm) bin-width data reduction. Left: Data corrected with polynomial systematics models (colored points), and overplotted with the best fit transit models (black curves). Wavelengths for each spectral channel are shown above the corresponding transit light curve. Right… view at source ↗
Figure 3
Figure 3. Examples of our Eureka! (Bell et al. 2022) Stage 5 spectroscopic light curve emcee (Foreman-Mackey et al. 2013) fits to the NIRISS SOSS Ahsoka data reduction for constant (∼ 0.01797 µm) bin-widths. Left: Data corrected with polynomial systematics models (colored points), and overplotted with the best fit transit models (black curves). Wavelengths for each spectral channel are shown above the corresponding transit li… view at source ↗
Figures from the paper (23 more)
Figure 4
Figure 4. Figure 4: Posterior distributions of free parameters (corner plot) for POSEIDON Reference retrieval case, R1 [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: The spectral decomposition of the Reference retrieval (R1) of WASP-166b with the absorption contributions of the key atmospheric constituents shown. Our reduced NIRISS and NIRSpec observational data (fixed 0.01797 µm bins) are plotted as grey circles and “x” markers, r…
Figure 6
Figure 6. Figure 6: WASP-166b transmission spectrum from POSEIDON Baseline free chemistry retrieval (B1). Top: observational data shown for NIRISS SOSS (orange circles) and NIRSpec G395M (blue squares), for the case of fixed (0.01797 µm) bins. The retrieval is using an isothermal P-T prof…
Figure 7
Figure 7. Figure 7: Posterior distributions of free parameters (corner plot) for POSEIDON Baseline retrieval case, B1. In this section, we describe equilibrium chemistry modeling with POSEIDON. As we did for the free chem￾istry modeling in Section 4.2, we fit the combined NIRISS and NIRSp…
Figure 8
Figure 8. Figure 8: WASP-166b transmission spectrum from POSEIDON equilibrium chemistry retrieval (E1). Top: observational data shown for NIRISS SOSS (orange circles) and NIRSpec G395M (blue squares), for the case of fixed (0.01797 µm) bins. The retrieval is using an isothermal P-T profil…
Figure 9
Figure 9. Figure 9: Posterior distributions of free parameters (corner plot) for POSEIDON equilibrium chemistry case, E1. duct a retrieval (E5) using the combined NIRISS and NIRSpec data reduced to a constant spectral resolution (R = 100). All equilibrium chemistry retrieval inputs and re…
Figure 10
Figure 10. Figure 10: Trace Curvature. The measured (blue points), smoothed (yellow line), and integer-rounded (green points) relative position of the spectral trace on the NIRSpec G395M (NRS1) detector determined with the Eureka! data reduction and analysis pipeline (Bell et al. 2022). Th…
Figure 11
Figure 11. Figure 11: The aligned spectral trace and residual background are shown as part of the Eureka! Stage 3 reduction of the NIRSpec dataset. The background region is outside the bg8 lines (solid orange), and the spectral extraction aperture is inside the ap4 lines (dashed green). Ou…
Figure 12
Figure 12. Figure 12: 1-D spectrum, with flux versus absolute pixel position for an arbitrarily chosen early integration (in this case Integration 4) in our NIRSpec dataset. We use the “Optimal” spectrum (Horne 1986) shown in green, although it is almost an exact match to the Standard spec…
Figure 13
Figure 13. Figure 13: Broadband (white) light curve from our NIRSpec observation of WASP-166b, showing the fitted central transit time (t0), and showing the exposure-long downward linear trend in normalized flux (-137 ppm/hr). We saw no other indication of unusual events or trends in the l…
Figure 14
Figure 14. Figure 14: Median image of our NIRISS SOSS F277W integrations. The F277W filter blocks those wavelengths ≲2.6 µm, and thus allows only the longest wavelengths of the order 1 spectrum to be dispersed upon the subarray. These longest wavelengths are visible left of column 500. The…
Figure 15
Figure 15. Figure 15: Comparison of the NIRISS SOSS CRDS spectral trace, the PASTASOSS-derived spectral trace, and the PASTASOSS trace shifted by 10 pixels, all overplotted on our BadPix output frame from integration 0. A portion of Order 2 is visible near the top center of SUBSTRIP96. The…
Figure 16
Figure 16. Figure 16: NIRISS SOSS raw white light curve for the 30 pixel width nirHiss box extraction before (left) and after (right) correcting the stellar spectra for 5σ outliers. We normalized the flux for each integration by the median flux of the out-of-transit integrations. For this …
Figure 17
Figure 17. Figure 17: Comparison of NIRISS SOSS 1D stellar spectra (flux versus wavelength) before (top) and after (bottom) correcting for 5σ outliers. Stellar spectra for all integrations are overplotted, with each individual stellar spectrum plotted in partially transparent color, such t…
Figure 18
Figure 18. Figure 18: Eureka! (Bell et al. 2022) Stage 5 white light curve emcee (Foreman-Mackey et al. 2013) fit to the NIRISS SOSS Ahsoka data reduction. The Top panel shows the Order 1 data corrected with the polynomial systematics model (blue points), and overplotted with the best fit …
Figure 19
Figure 19. Figure 19: Posterior distributions of free parameters (corner plot) for POSEIDON Reference retrieval case, R2, with Na and K added to the Baseline model. We see some faint hints of Na, but it is very poorly constrained [PITH_FULL_IMAGE:figures/full_fig_p040_19.png]
Figure 20
Figure 20. Figure 20: Posterior distributions of free parameters (corner plot) for POSEIDON Reference retrieval case, R3, with CO and SO2 added to the Baseline model. We see some hints of SO2, but it is very poorly constrained [PITH_FULL_IMAGE:figures/full_fig_p042_20.png]
Figure 21
Figure 21. Figure 21: Retrieved P-T profile for the atmosphere of WASP-166b using the POSEIDON 2-parameter ’gradient’ model (B2). The observational data for this case has been reduced to fixed 0.01797 µm bins. As with the Baseline (B1) case, the fill gases, H2 and He are present at the pri…
Figure 22
Figure 22. Figure 22: WASP-166b transmission spectra from POSEIDON free chemistry retrievals, based on the NIRISS SOSS dataset alone (B3; Top panels) and based on the NIRSpec dataset alone (B4; Bottom panels). Top: observational data shown for NIRISS SOSS (orange circles) for the case of f…
Figure 23
Figure 23. Figure 23: WASP-166b transmission spectrum from POSEIDON free chemistry retrieval; the observational data are for the case of constant R = 100 binning (B5). Top: observational data shown for NIRISS SOSS (orange circles) and NIRSpec G395M (blue squares). The retrieval is using an…
Figure 24
Figure 24. Figure 24: WASP-166b transmission spectrum from TauREx free chemistry retrieval (B6). We used the same free parameter priors (see [PITH_FULL_IMAGE:figures/full_fig_p046_24.png]
Figure 25
Figure 25. Figure 25: WASP-166b transmission spectrum from PLATON equilibrium chemistry retrieval (E2). Top: observational data shown for NIRISS SOSS (orange circles) and NIRSpec G395M (blue squares), for the case of fixed (0.01797 µm) bins. The retrieval is using an isothermal P-T profile…
Figure 26
Figure 26. Figure 26: Posterior distributions of free parameters (corner plot) for PLATON Equilibrium Chemistry Case, E2. (Note: the ’wfc offset transit’ parameter is equivalent to δrel in our POSEIDON retrievals; we adjust the wavelength range for ’wfc offset transit’ from 1 − 1.7 µm (for…

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Reference graph

Works this paper leans on

159 extracted references · 6 canonical work pages · cited by 3 Pith papers

  1. [1]

    B., Mansfield, M., et al

    Ahrer, E.-M., Stevenson, K. B., Mansfield, M., et al. 2023, Nature, 614, 653, doi: 10.1038/s41586-022-05590-4

    doi:10.1038/s41586-022-05590-4 2023

  2. [2]

    F., Changeat, Q., Waldmann, I

    Al-Refaie, A. F., Changeat, Q., Waldmann, I. P., & Tinetti, G. 2021, ApJ, 917, 37, doi: 10.3847/1538-4357/ac0252

    doi:10.3847/1538-4357/ac0252 2021

  3. [3]

    Yurchenko, S. N. 2015, MNRAS, 448, 1704, doi: 10.1093/mnras/stv091

    doi:10.1093/mnras/stv091 2015

  4. [4]

    2023, PASP, 135, 075001, doi: 10.1088/1538-3873/acd7a3

    Albert, L., Lafreni` ere, D., Ren´ e, D., et al. 2023, PASP, 135, 075001, doi: 10.1088/1538-3873/acd7a3

    doi:10.1088/1538-3873/acd7a3 2023

  5. [5]

    R., Alam, M

    Alderson, L., Wakeford, H. R., Alam, M. K., et al. 2023, Nature, 614, 664, doi: 10.1038/s41586-022-05591-3

    doi:10.1038/s41586-022-05591-3 2023

  6. [6]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481, doi: 10.1146/annurev.astro.46.060407.145222

    arXiv 2009

  7. [7]

    Naumenko, O. V. 2016, MNRAS, 460, 4063, doi: 10.1093/mnras/stw1133

    doi:10.1093/mnras/stw1133 2016

  8. [8]

    2023a, arXiv e-prints, arXiv:2311.07769, doi: 10.48550/arXiv.2311.07769 —

    Baines, T., Espinoza, N., Filippazzo, J., & Volk, K. 2023a, arXiv e-prints, arXiv:2311.07769, doi: 10.48550/arXiv.2311.07769 —. 2023b, Characterization of the visit-to-visit Stability of the GR700XD Wavelength Calibration for NIRISS/SOSS

    arXiv doi:10.48550/arxiv.2311.07769

Show all 159 references
  1. [9]

    J., Strange, J

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

    2014 doi

  2. [10]

    J., Hill, C., Yurchenko, S

    Barton, E. J., Hill, C., Yurchenko, S. N., et al. 2017, JQSRT, 187, 453, doi: 10.1016/j.jqsrt.2016.10.024

    2017 doi

  3. [11]

    J., Yurchenko, S

    Barton, E. J., Yurchenko, S. N., & Tennyson, J. 2013, MNRAS, 434, 1469, doi: 10.1093/mnras/stt1105

    2013 doi

  4. [12]

    S., et al

    Basilicata, M., Giacobbe, P., Bonomo, A. S., et al. 2024, A&A, 686, A127, doi: 10.1051/0004-6361/202347659

    2024 doi

  5. [13]

    E., Mandell, A., Pontoppidan, K., et al

    Batalha, N. E., Mandell, A., Pontoppidan, K., et al. 2017, PASP, 129, 064501, doi: 10.1088/1538-3873/aa65b0

    2017 doi

  6. [14]

    H., & Laughlin, G

    Batygin, K., Bodenheimer, P. H., & Laughlin, G. P. 2016, ApJ, 829, 114, doi: 10.3847/0004-637X/829/2/114 W ASP-166b 51

    2016 doi

  7. [15]

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

    Beatty, T. G., Welbanks, L., Schlawin, E., et al. 2024, ApJL, 970, L10, doi: 10.3847/2041-8213/ad55e9

    2024 doi

  8. [16]

    J., Ahrer, E.-M., Brande, J., et al

    Bell, T. J., Ahrer, E.-M., Brande, J., et al. 2022, Journal of Open Source Software, 7, 4503, doi: 10.21105/joss.04503

    2022 doi

  9. [17]

    2013, ApJ, 778, 153, doi: 10.1088/0004-637X/778/2/153

    Benneke, B., & Seager, S. 2013, ApJ, 778, 153, doi: 10.1088/0004-637X/778/2/153

    2013 doi

  10. [18]

    A., Lothringer, J., et al

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

    2019 doi

  11. [19]

    Bernath, P. F. 2020, JQSRT, 240, 106687, doi: 10.1016/j.jqsrt.2019.106687

    2020

  12. [20]

    M., Ferruit, P., Giardino, G., et al

    Birkmann, S. M., Ferruit, P., Giardino, G., et al. 2022, A&A, 661, A83, doi: 10.1051/0004-6361/202142592 B¨ oker, T., Beck, T. L., Birkmann, S. M., et al. 2023, PASP, 135, 038001, doi: 10.1088/1538-3873/acb846

    2022 doi

  13. [21]

    P., Yurchenko, S

    Brady, R. P., Yurchenko, S. N., Tennyson, J., & Kim, G.-S. 2024, MNRAS, 527, 6675, doi: 10.1093/mnras/stad3508

    2024 doi

  14. [22]

    Brande, J., Crossfield, I. J. M., Kreidberg, L., et al. 2022, AJ, 164, 197, doi: 10.3847/1538-3881/ac8b7e

    2022 doi

  15. [23]

    Brooke, J. S. A., Bernath, P. F., Western, C. M., et al. 2016, JQSRT, 168, 142, doi: 10.1016/j.jqsrt.2015.07.021

    2016 doi

  16. [24]

    M., Bayliss, D., McCormac, J., et al

    Bryant, E. M., Bayliss, D., McCormac, J., et al. 2020, MNRAS, 494, 5872, doi: 10.1093/mnras/staa1075

    2020 doi

  17. [25]

    2016, PyMultiNest: Python interface for

    Buchner, J. 2016, PyMultiNest: Python interface for

    2016

  18. [26]

    2023, JWST Calibration Pipeline, 1.12.0, Zenodo, Zenodo, doi: 10.5281/zenodo.6984365

    Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2023, JWST Calibration Pipeline, 1.12.0, Zenodo, Zenodo, doi: 10.5281/zenodo.6984365

    2023 doi

  19. [27]

    Cabot, S. H. C., Madhusudhan, N., Welbanks, L., Piette, A., & Gandhi, S. 2020, MNRAS, 494, 363, doi: 10.1093/mnras/staa748

    2020 doi

  20. [28]

    2017, A&A, 608, A135, doi: 10.1051/0004-6361/201731956

    Casasayas-Barris, N., Palle, E., Nowak, G., et al. 2017, A&A, 608, A135, doi: 10.1051/0004-6361/201731956

    2017 doi

  21. [29]

    2019, A&A, 628, A9, doi: 10.1051/0004-6361/201935623

    Casasayas-Barris, N., Pall´ e, E., Yan, F., et al. 2019, A&A, 628, A9, doi: 10.1051/0004-6361/201935623

    2019 doi

  22. [30]

    M., Lovis, C., Bourrier, V., et al

    Cegla, H. M., Lovis, C., Bourrier, V., et al. 2016, A&A, 588, A127, doi: 10.1051/0004-6361/201527794

    2016 doi

  23. [31]

    M., Noyes, R

    Charbonneau, D., Brown, T. M., Noyes, R. W., & Gilliland, R. L. 2002, ApJ, 568, 377, doi: 10.1086/338770

    2002 doi

  24. [32]

    2020, A&A, 642, A54, doi: 10.1051/0004-6361/202038661

    Chen, G., Casasayas-Barris, N., Pall´ e, E., et al. 2020, A&A, 642, A54, doi: 10.1051/0004-6361/202038661

    2020 doi

  25. [33]

    2018, A&A, 616, A145, doi: 10.1051/0004-6361/201833033

    Chen, G., Pall´ e, E., Welbanks, L., et al. 2018, A&A, 616, A145, doi: 10.1051/0004-6361/201833033

    2018 doi

  26. [34]

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

    Chubb, K. L., Tennyson, J., & Yurchenko, S. N. 2020, MNRAS, 493, 1531, doi: 10.1093/mnras/staa229

    2020 doi

  27. [35]

    A., Yurchenko, S

    Coles, P. A., Yurchenko, S. N., & Tennyson, J. 2019, MNRAS, 490, 4638, doi: 10.1093/mnras/stz2778

    2019 doi

  28. [36]

    2023, Nature, 620, 292, doi: 10.1038/s41586-023-06230-1 D’Angelo, G., Durisen, R

    Coulombe, L.-P., Benneke, B., Challener, R., et al. 2023, Nature, 620, 292, doi: 10.1038/s41586-023-06230-1 D’Angelo, G., Durisen, R. H., & Lissauer, J. J. 2010, in Exoplanets, ed. S. Seager, 319–346, doi: 10.48550/arXiv.1006.5486 D’Angelo, G., & Lissauer, J. J. 2018, in Handb...

    2023 doi arXiv

  29. [37]

    K., de Mooij, E

    Deibert, E. K., de Mooij, E. J. W., Jayawardhana, R., et al. 2019, AJ, 157, 58, doi: 10.3847/1538-3881/aaf56b

    2019 doi

  30. [38]

    M., Bryant, E., et al

    Doyle, L., Cegla, H. M., Bryant, E., et al. 2022, MNRAS, 516, 298, doi: 10.1093/mnras/stac2178

    2022 doi

  31. [39]

    J., Hutchings, J

    Doyon, R., Willott, C. J., Hutchings, J. B., et al. 2023, PASP, 135, 098001, doi: 10.1088/1538-3873/acd41b

    2023 doi

  32. [40]

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

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

    2024 doi

  33. [41]

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

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

    2022 doi

  34. [42]

    2015, MNRAS, 450, 1879, doi: 10.1093/mnras/stv744 —

    Espinoza, N., & Jord´ an, A. 2015, MNRAS, 450, 1879, doi: 10.1093/mnras/stv744 —. 2016, MNRAS, 457, 3573, doi: 10.1093/mnras/stw224

    2015 doi

  35. [43]

    M., et al

    Espinoza, N., ´Ubeda, L., Birkmann, S. M., et al. 2023, PASP, 135, 018002, doi: 10.1088/1538-3873/aca3d3

    2023 doi

  36. [44]

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

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

    2023 doi

  37. [45]

    P., & Bridges, M

    Feroz, F., Hobson, M. P., & Bridges, M. 2009, MNRAS, 398, 1601, doi: 10.1111/j.1365-2966.2009.14548.x

    2009

  38. [46]

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

    Feroz, F., Hobson, M. P., Cameron, E., & Pettitt, A. N. 2013, ArXiv e-prints. https://arxiv.org/abs/1306.2144

    2013 arXiv

  39. [47]

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

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

    2013 doi

  40. [48]

    Fortney, J. J. 2005, MNRAS, 364, 649, doi: 10.1111/j.1365-2966.2005.09587.x

    2005

  41. [49]

    2014, Natur, 513, 526, doi: 10.1038/nature13785

    Fraine, J., Deming, D., Benneke, B., et al. 2014, Natur, 513, 526, doi: 10.1038/nature13785

    2014 doi

  42. [50]

    GharibNezhad, E., Shayesteh, A., & Bernath, P. F. 2013, MNRAS, 432, 2043, doi: 10.1093/mnras/stt510

    2013 doi

  43. [51]

    2010, Communications in Applied Mathematics and Computational Science, 5, 65, doi: 10.2140/camcos.2010.5.65

    Goodman, J., & Weare, J. 2010, Communications in Applied Mathematics and Computational Science, 5, 65, doi: 10.2140/camcos.2010.5.65

    2010 doi

  44. [52]

    2007, MNRAS, 382, 1859, doi: 10.1111/j.1365-2966.2007.12707.x

    Gordon, C., & Trotta, R. 2007, MNRAS, 382, 1859, doi: 10.1111/j.1365-2966.2007.12707.x

    2007

  45. [53]

    E., Rothman, L

    Gordon, I. E., Rothman, L. S., Hill, C., et al. 2017, JQSRT, 203, 3, doi: 10.1016/j.jqsrt.2017.06.038

    2017 doi

  46. [54]

    E., Rothman, L

    Gordon, I. E., Rothman, L. S., Hargreaves, R. J., et al. 2022, JQSRT, 277, 107949, doi: 10.1016/j.jqsrt.2021.107949

    2022

  47. [55]

    N., Yurchenko, S

    Gorman, M. N., Yurchenko, S. N., & Tennyson, J. 2019, MNRAS, 490, 1652, doi: 10.1093/mnras/stz2517

    2019 doi

  48. [56]

    Grant, D., & Wakeford, H. R. 2022, Exo-TiC/ExoTiC-LD: ExoTiC-LD v3.0.0, v3.0.0, Zenodo, doi: 10.5281/zenodo.7437681 52 Mayo et al

    2022 doi

  49. [57]

    P., Line, M

    Greene, T. P., Line, M. R., Montero, C., et al. 2016, ApJ, 817, 17, doi: 10.3847/0004-637X/817/1/17

    2016 doi

  50. [58]

    J., Espinoza, N., et al

    Gressier, A., MacDonald, R. J., Espinoza, N., et al. 2025, AJ, 169, 57, doi: 10.3847/1538-3881/ad97bf

    2025 doi

  51. [59]

    2021, AJ, 161, 19, doi: 10.3847/1538-3881/abc3c8

    Guilluy, G., Gressier, A., Wright, S., et al. 2021, AJ, 161, 19, doi: 10.3847/1538-3881/abc3c8

    2021 doi

  52. [60]

    2023, A&A, 676, A130, doi: 10.1051/0004-6361/202346419 Guzm´ an-Mesa, A., Kitzmann, D., Fisher, C., et al

    Guilluy, G., Bourrier, V., Jaziri, Y., et al. 2023, A&A, 676, A130, doi: 10.1051/0004-6361/202346419 Guzm´ an-Mesa, A., Kitzmann, D., Fisher, C., et al. 2020, AJ, 160, 15, doi: 10.3847/1538-3881/ab9176

    2023 doi

  53. [61]

    J., Gordon, I

    Hargreaves, R. J., Gordon, I. E., Rothman, L. S., et al. 2019, JQSRT, 232, 35, doi: 10.1016/j.jqsrt.2019.04.040

    2019 doi

  54. [62]

    2016, MNRAS, 455, L96, doi: 10.1093/mnrasl/slv158

    Helled, R., Lozovsky, M., & Zucker, S. 2016, MNRAS, 455, L96, doi: 10.1093/mnrasl/slv158

    2016 doi

  55. [63]

    R., Triaud, A

    Hellier, C., Anderson, D. R., Triaud, A. H. M. J., et al. 2019, MNRAS, 488, 3067, doi: 10.1093/mnras/stz1903

    2019 doi

  56. [64]

    2017, Exoplanetary Atmospheres: Theoretical Concepts and Foundations

    Heng, K. 2017, Exoplanetary Atmospheres: Theoretical Concepts and Foundations

    2017

  57. [65]

    J., Ehrenreich, D., Kitzmann, D., et al

    Hoeijmakers, H. J., Ehrenreich, D., Kitzmann, D., et al. 2019, A&A, 627, A165, doi: 10.1051/0004-6361/201935089

    2019 doi

  58. [66]

    1986, PASP, 98, 609, doi: 10.1086/131801

    Horne, K. 1986, PASP, 98, 609, doi: 10.1086/131801

    1986 doi

  59. [67]

    R., Burrows, A., & Deming, D

    Howe, A. R., Burrows, A., & Deming, D. 2017, ApJ, 835, 96, doi: 10.3847/1538-4357/835/1/96

    2017 doi

  60. [68]

    2022, A&A, 661, A80, doi: 10.1051/0004-6361/202142663

    Jakobsen, P., Ferruit, P., Alves de Oliveira, C., et al. 2022, A&A, 661, A80, doi: 10.1051/0004-6361/202142663

    2022 doi

  61. [69]

    S., D ´ ıaz, M

    Jenkins, J. S., D ´ ıaz, M. R., Kurtovic, N. T., et al. 2020, Nature Astronomy, 4, 1148, doi: 10.1038/s41550-020-1142-z

    2020 doi

  62. [70]

    G., Cauley, P

    Jensen, A. G., Cauley, P. W., Redfield, S., Cochran, W. D., & Endl, M. 2018, AJ, 156, 154, doi: 10.3847/1538-3881/aadca7 JWST Transiting Exoplanet Community Early Release Science Team, Ahrer, E.-M., Alderson, L., et al. 2023, Nature, 614, 649, doi: 10.1038/s41586-022-05269-w

    2018 doi

  63. [71]

    Kempton, E. M. R., Lupu, R., Owusu-Asare, A., Slough, P., & Cale, B. 2017, PASP, 129, 044402, doi: 10.1088/1538-3873/aa61ef

    2017 doi

  64. [72]

    Kempton, E. M. R., Perna, R., & Heng, K. 2014, ApJ, 795, 24, doi: 10.1088/0004-637X/795/1/24

    2014 doi

  65. [73]

    B., Fu, G., et al

    Kirk, J., Stevenson, K. B., Fu, G., et al. 2024, AJ, 167, 90, doi: 10.3847/1538-3881/ad19df

    2024 doi

  66. [74]

    2018, FastChem: An ultra-fast equilibrium chemistry, Astrophysics Source Code Library, record ascl:1804.025

    Kitzmann, D., & Stock, J. 2018, FastChem: An ultra-fast equilibrium chemistry, Astrophysics Source Code Library, record ascl:1804.025

    2018

  67. [75]

    Koll, D. D. B. 2022, ApJ, 924, 134, doi: 10.3847/1538-4357/ac3b48

    2022 doi

  68. [76]

    1950, Harvard College Observatory Circular, 454, 1

    Kopal, Z. 1950, Harvard College Observatory Circular, 454, 1

    1950

  69. [77]

    2013, Atomic Spectra Database

    Kramida, A., Ralchenko, Y., & Reader, J. 2013, Atomic Spectra Database. http://www.nist.gov/pml/data/asd.cfm

    2013

  70. [78]

    2015, PASP, 127, 1161, doi: 10.1086/683602

    Kreidberg, L. 2015, PASP, 127, 1161, doi: 10.1086/683602

    2015 doi

  71. [79]

    Kreidberg, L., Molli` ere, P., Crossfield, I. J. M., et al. 2022, AJ, 164, 124, doi: 10.3847/1538-3881/ac85be

    2022 doi

  72. [80]

    2023, MNRAS, 521, 1233, doi: 10.1093/mnras/stad480

    Lafarga, M., Brogi, M., Gandhi, S., et al. 2023, MNRAS, 521, 1233, doi: 10.1093/mnras/stad480

    2023 doi

  73. [81]

    J., & Chiang, E

    Lee, E. J., & Chiang, E. 2016, ApJ, 817, 90, doi: 10.3847/0004-637X/817/2/90

    2016 doi

  74. [82]

    E., Le Roy, R

    Li, G., Gordon, I. E., Le Roy, R. J., et al. 2013, JQSRT, 121, 78, doi: 10.1016/j.jqsrt.2013.02.005

    2013 doi

  75. [83]

    E., Rothman, L

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

    2015 doi

  76. [84]

    R., Mullens, E., Alderson, L., et al

    Louie, D. R., Mullens, E., Alderson, L., et al. 2025, AJ, 169, 86, doi: 10.3847/1538-3881/ad9688

    2025 doi

  77. [85]

    M., et al

    Lustig-Yaeger, J., Fu, G., May, E. M., et al. 2023, Nature Astronomy, 7, 1317, doi: 10.1038/s41550-023-02064-z

    2023 doi

  78. [86]

    MacDonald, R. J. 2023, The Journal of Open Source Software, 8, 4873, doi: 10.21105/joss.04873

    2023 doi

  79. [87]

    J., Goyal, J

    MacDonald, R. J., Goyal, J. M., & Lewis, N. K. 2020, ApJL, 893, L43, doi: 10.3847/2041-8213/ab8238

    2020 doi

  80. [88]

    J., & Lewis, N

    MacDonald, R. J., & Lewis, N. K. 2022, ApJ, 929, 20, doi: 10.3847/1538-4357/ac47fe

    2022 doi

  81. [89]

    J., & Madhusudhan, N

    MacDonald, R. J., & Madhusudhan, N. 2017, MNRAS, 469, 1979, doi: 10.1093/mnras/stx804 —. 2019, MNRAS, 486, 1292, doi: 10.1093/mnras/stz789

    2017 doi

  82. [90]

    2019, ARA&A, 57, 617, doi: 10.1146/annurev-astro-081817-051846

    Madhusudhan, N. 2019, ARA&A, 57, 617, doi: 10.1146/annurev-astro-081817-051846

    2019 doi

  83. [91]

    I., & Hu, Y

    Madhusudhan, N., Ag´ undez, M., Moses, J. I., & Hu, Y. 2016, SSRv, 205, 285, doi: 10.1007/s11214-016-0254-3

    2016 doi

  84. [92]

    2017, MNRAS, 469, 4102, doi: 10.1093/mnras/stx1139

    Madhusudhan, N., Bitsch, B., Johansen, A., & Eriksson, L. 2017, MNRAS, 469, 4102, doi: 10.1093/mnras/stx1139

    2017 doi

  85. [93]

    2015, Astronomy & Astrophysics, 573, A90

    Magic, Z., Chiavassa, A., Collet, R., & Asplund, M. 2015, Astronomy & Astrophysics, 573, A90

    2015

  86. [94]

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

    Mant, B. P., Yachmenev, A., Tennyson, J., & Yurchenko, S. N. 2018, MNRAS, 478, 3220, doi: 10.1093/mnras/sty1239

    2018 doi

  87. [95]

    2016, ApJL, 820, L8, doi: 10.3847/2041-8205/820/1/L8

    Matsakos, T., & K¨ onigl, A. 2016, ApJL, 820, L8, doi: 10.3847/2041-8205/820/1/L8

    2016 doi

  88. [96]

    M., MacDonald, R

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

    2023 doi

  89. [97]

    2016, A&A, 589, A75, doi: 10.1051/0004-6361/201528065

    Mazeh, T., Holczer, T., & Faigler, S. 2016, A&A, 589, A75, doi: 10.1051/0004-6361/201528065

    2016 doi

  90. [98]

    K., Masseron, T., Hoeijmakers, H

    McKemmish, L. K., Masseron, T., Hoeijmakers, H. J., et al. 2019, MNRAS, 488, 2836, doi: 10.1093/mnras/stz1818

    2019 doi

  91. [99]

    K., Yurchenko, S

    McKemmish, L. K., Yurchenko, S. N., & Tennyson, J. 2016, MNRAS, 463, 771, doi: 10.1093/mnras/stw1969

    2016 doi

  92. [100]

    E., Stevenson, K

    Moran, S. E., Stevenson, K. B., Sing, D. K., et al. 2023, ApJL, 948, L11, doi: 10.3847/2041-8213/accb9c W ASP-166b 53

    2023 doi

  93. [101]

    I., Visscher, C., Fortney, J

    Moses, J. I., Visscher, C., Fortney, J. J., et al. 2011, ApJ, 737, 15, doi: 10.1088/0004-637X/737/1/15

    2011 doi

  94. [102]

    K., & MacDonald, R

    Mullens, E., Lewis, N. K., & MacDonald, R. J. 2024, ApJ, 977, 105, doi: 10.3847/1538-4357/ad8575

    2024 doi

  95. [103]

    K., Gibson, N

    Nikolov, N., Sing, D. K., Gibson, N. P., et al. 2016, ApJ, 832, 191, doi: 10.3847/0004-637X/832/2/191 ¨Oberg, K. I., Murray-Clay, R., & Bergin, E. A. 2011, ApJL, 743, L16, doi: 10.1088/2041-8205/743/1/L16

    2016 doi

  96. [104]

    2022, MNRAS, 511, 5448, doi: 10.1093/mnras/stac371

    Owens, A., Dooley, S., McLaughlin, L., et al. 2022, MNRAS, 511, 5448, doi: 10.1093/mnras/stac371

    2022 doi

  97. [105]

    N., & Tennyson, J

    Owens, A., Yurchenko, S. N., & Tennyson, J. 2024, MNRAS, 530, 4004, doi: 10.1093/mnras/stae1110

    2024 doi

  98. [106]

    2022, Research Notes of the American Astronomical Society, 6, 155, doi: 10.3847/2515-5172/ac8676

    Isaacson, H., & Rice, M. 2022, Research Notes of the American Astronomical Society, 6, 155, doi: 10.3847/2515-5172/ac8676

    2022 doi

  99. [107]

    L., Kyuberis, A

    Polyansky, O. L., Kyuberis, A. A., Zobov, N. F., et al. 2018, MNRAS, 480, 2597, doi: 10.1093/mnras/sty1877

    2018 doi

  100. [108]

    N., & Tennyson, J

    Qu, Q., Yurchenko, S. N., & Tennyson, J. 2021, MNRAS, 504, 5768, doi: 10.1093/mnras/stab1154

    2021 doi

  101. [109]

    V., Apai, D., & Giampapa, M

    Rackham, B. V., Apai, D., & Giampapa, M. S. 2018, ApJ, 853, 122, doi: 10.3847/1538-4357/aaa08c —. 2019, AJ, 157, 96, doi: 10.3847/1538-3881/aaf892

    2018 doi

  102. [110]

    2024, The Journal of Open Source Software, 9, 6898, doi: 10.21105/joss.06898

    Radica, M. 2024, The Journal of Open Source Software, 9, 6898, doi: 10.21105/joss.06898

    2024 doi

  103. [111]

    R., et al

    Radica, M., Taylor, J., Wakeford, H. R., et al. 2025, MNRAS, 538, 1853, doi: 10.1093/mnras/staf402

    2025 doi

  104. [112]

    2023, MNRAS, 524, 835, doi: 10.1093/mnras/stad1762

    Radica, M., Welbanks, L., Espinoza, N., et al. 2023, MNRAS, 524, 835, doi: 10.1093/mnras/stad1762

    2023 doi

  105. [113]

    2024, ApJL, 962, L20, doi: 10.3847/2041-8213/ad20e4

    Radica, M., Coulombe, L.-P., Taylor, J., et al. 2024, ApJL, 962, L20, doi: 10.3847/2041-8213/ad20e4

    2024 doi

  106. [114]

    J., Boehm, N., Cagiano, S., et al

    Rauscher, B. J., Boehm, N., Cagiano, S., et al. 2014, PASP, 126, 739, doi: 10.1086/677681

    2014 doi

  107. [115]

    D., & Koesterke, L

    Redfield, S., Endl, M., Cochran, W. D., & Koesterke, L. 2008, ApJL, 673, L87, doi: 10.1086/527475

    2008 doi

  108. [116]

    V., & Tyuterev, V

    Rey, M., Nikitin, A. V., & Tyuterev, V. G. 2017, ApJ, 847, 105, doi: 10.3847/1538-4357/aa8909

    2017 doi

  109. [117]

    A., Bodenheimer, P., Lissauer, J

    Rogers, L. A., Bodenheimer, P., Lissauer, J. J., & Seager, S. 2011, ApJ, 738, 59, doi: 10.1088/0004-637X/738/1/59

    2011 doi

  110. [118]

    S., Gordon, I

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

    2010 doi

  111. [119]

    K., Mukherjee, S., et al

    Rustamkulov, Z., Sing, D. K., Mukherjee, S., et al. 2023, Nature, 614, 659, doi: 10.1038/s41586-022-05677-y

    2023 doi

  112. [120]

    L., et al

    Ryabchikova, T., Piskunov, N., Kurucz, R. L., et al. 2015, PhyS, 90, 054005, doi: 10.1088/0031-8949/90/5/054005

    2015 doi

  113. [121]

    2024, MNRAS, 531, 2731, doi: 10.1093/mnras/stae1230

    Holmberg, M. 2024, MNRAS, 531, 2731, doi: 10.1093/mnras/stae1230

    2024 doi

  114. [122]

    2020, AJ, 160, 231, doi: 10.3847/1538-3881/abb811

    Schlawin, E., Leisenring, J., Misselt, K., et al. 2020, AJ, 160, 231, doi: 10.3847/1538-3881/abb811

    2020 doi

  115. [123]

    J., et al

    Schlawin, E., Ohno, K., Bell, T. J., et al. 2024, ApJL, 974, L33, doi: 10.3847/2041-8213/ad7fef

    2024 doi

  116. [124]

    V., Ehrenreich, D., Wyttenbach, A., et al

    Seidel, J. V., Ehrenreich, D., Wyttenbach, A., et al. 2019, A&A, 623, A166, doi: 10.1051/0004-6361/201834776

    2019 doi

  117. [125]

    V., Ehrenreich, D., Bourrier, V., et al

    Seidel, J. V., Ehrenreich, D., Bourrier, V., et al. 2020, A&A, 641, L7, doi: 10.1051/0004-6361/202038497

    2020 doi

  118. [126]

    V., Cegla, H

    Seidel, J. V., Cegla, H. M., Doyle, L., et al. 2022, MNRAS, 513, L15, doi: 10.1093/mnrasl/slac027

    2022 doi

  119. [127]

    Serdyuchenko, A., Gorshelev, V., Weber, M., Chehade, W., & Burrows, J. P. 2014, Atmospheric Measurement Techniques, 7, 625, doi: 10.5194/amt-7-625-201410.5194/amtd-6-6613-2013

    2014 arXiv

  120. [128]

    K., Fortney, J

    Sing, D. K., Fortney, J. J., Nikolov, N., et al. 2016, Nature, 529, 59, doi: 10.1038/nature16068

    2016 doi

  121. [129]

    K., Rustamkulov, Z., Thorngren, D

    Sing, D. K., Rustamkulov, Z., Thorngren, D. P., et al. 2024, Nature, 630, 831, doi: 10.1038/s41586-024-07395-z

    2024 doi

  122. [130]

    Yurchenko, S. N. 2015, MNRAS, 446, 2337, doi: 10.1093/mnras/stu2246

    2015 doi

  123. [131]

    2014, JQSRT, 142, 66, doi: 10.1016/j.jqsrt.2014.03.012

    Tennyson, J. 2014, JQSRT, 142, 66, doi: 10.1016/j.jqsrt.2014.03.012

    2014 doi

  124. [132]

    Speagle, J. S. 2020, MNRAS, 493, 3132, doi: 10.1093/mnras/staa278

    2020 doi

  125. [133]

    W., Kitzmann, D., & Patzer, A

    Stock, J. W., Kitzmann, D., & Patzer, A. B. C. 2022, MNRAS, 517, 4070, doi: 10.1093/mnras/stac2623

    2022 doi

  126. [134]

    A., & Perevalov, V

    Tashkun, S. A., & Perevalov, V. I. 2011, JQSRT, 112, 1403, doi: 10.1016/j.jqsrt.2011.03.005

    2011 doi

  127. [135]

    C., Mann, A

    Thao, P. C., Mann, A. W., Feinstein, A. D., et al. 2024, AJ, 168, 297, doi: 10.3847/1538-3881/ad81d7

    2024 doi

  128. [136]

    2015, A&A, 574, A138, doi: 10.1051/0004-6361/201424868

    Mezger, K. 2015, A&A, 574, A138, doi: 10.1051/0004-6361/201424868

    2015 doi

  129. [137]

    R., Grosheintz, L., et al

    Tsai, S.-M., Lyons, J. R., Grosheintz, L., et al. 2017, ApJS, 228, 20, doi: 10.3847/1538-4365/228/2/20

    2017 doi

  130. [138]

    2021, ApJ, 923, 264, doi: 10.3847/1538-4357/ac29bc

    Tsai, S.-M., Malik, M., Kitzmann, D., et al. 2021, ApJ, 923, 264, doi: 10.3847/1538-4357/ac29bc

    2021 doi

  131. [139]

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

    2023 doi

  132. [140]

    S., Tennyson, J., Yurchenko, S

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

    2016 doi

  133. [141]

    2022, Hubble Ultraviolet-optical Survey of Transiting Legacy Exoplanets (HUSTLE) treasury program, HST Proposal

    Wakeford, H., Alderson, L., Batalha, N., et al. 2022, Hubble Ultraviolet-optical Survey of Transiting Legacy Exoplanets (HUSTLE) treasury program, HST Proposal. Cycle 30, ID. #17183

    2022

  134. [142]

    R., Sing, D

    Wakeford, H. R., Sing, D. K., Kataria, T., et al. 2017, Science, 356, 628, doi: 10.1126/science.aah4668

    2017 doi

  135. [143]

    2019, AJ, 157, 206, doi: 10.3847/1538-3881/ab14de 54 Mayo et al

    Welbanks, L., & Madhusudhan, N. 2019, AJ, 157, 206, doi: 10.3847/1538-3881/ab14de 54 Mayo et al

    2019 doi

  136. [144]

    F., et al

    Welbanks, L., Madhusudhan, N., Allard, N. F., et al. 2019, ApJL, 887, L20, doi: 10.3847/2041-8213/ab5a89

    2019 doi

  137. [145]

    J., Beatty, T

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

    2024 doi

  138. [146]

    2021, GGchem: Fast thermo-chemical equilibrium code, Astrophysics Source Code Library, record ascl:2104.018

    Woitke, P., & Helling, C. 2021, GGchem: Fast thermo-chemical equilibrium code, Astrophysics Source Code Library, record ascl:2104.018

    2021

  139. [147]

    H., et al

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

    2018 doi

  140. [148]

    N., Bernath, P., et al

    Wong, A., Yurchenko, S. N., Bernath, P., et al. 2017, MNRAS, 470, 882, doi: 10.1093/mnras/stx1211

    2017 doi

  141. [149]

    2015, ApJ, 806, 180, doi: 10.1088/0004-637X/806/2/180

    Wordsworth, R. 2015, ApJ, 806, 180, doi: 10.1088/0004-637X/806/2/180

    2015 doi

  142. [150]

    2017, A&A, 602, A36, doi: 10.1051/0004-6361/201630063

    Wyttenbach, A., Lovis, C., Ehrenreich, D., et al. 2017, A&A, 602, A36, doi: 10.1051/0004-6361/201630063

    2017 doi

  143. [151]

    N., Barber, R

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

    2011

  144. [152]

    N., Bond, W., Gorman, M

    Yurchenko, S. N., Bond, W., Gorman, M. N., et al. 2018a, MNRAS, 478, 270, doi: 10.1093/mnras/sty939

    doi

  145. [153]

    2020, MNRAS, 496, 5282, doi: 10.1093/mnras/staa1874

    Tennyson, J. 2020, MNRAS, 496, 5282, doi: 10.1093/mnras/staa1874

    2020 doi

  146. [154]

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

    Yurchenko, S. N., Owens, A., Kefala, K., & Tennyson, J. 2024, MNRAS, 528, 3719, doi: 10.1093/mnras/stae148

    2024 doi

  147. [155]

    N., Sinden, F., Lodi, L., et al

    Yurchenko, S. N., Sinden, F., Lodi, L., et al. 2018b, MNRAS, 473, 5324, doi: 10.1093/mnras/stx2738

    doi

  148. [156]

    N., Szab´ o, I., Pyatenko, E., & Tennyson, J

    Yurchenko, S. N., Szab´ o, I., Pyatenko, E., & Tennyson, J. 2018c, MNRAS, 480, 3397, doi: 10.1093/mnras/sty2050

    doi

  149. [157]

    N., Tennyson, J., Syme, A.-M., et al

    Yurchenko, S. N., Tennyson, J., Syme, A.-M., et al. 2022, MNRAS, 510, 903, doi: 10.1093/mnras/stab3267

    2022 doi

  150. [158]

    Zhang, M., Chachan, Y., Kempton, E. M. R., & Knutson, H. A. 2019, PASP, 131, 034501, doi: 10.1088/1538-3873/aaf5ad

    2019 doi

  151. [159]

    Zhang, M., Chachan, Y., Kempton, E. M. R., Knutson, H. A., & Chang, W. H. 2020, ApJ, 899, 27, doi: 10.3847/1538-4357/aba1e6

    2020 doi

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