pith. machine review for the scientific record. sign in

arxiv: 2605.15003 · v1 · submitted 2026-05-14 · 🌌 astro-ph.EP

Recognition: 1 theorem link

· Lean Theorem

JWST COMPASS Program: The 3--5μm transmission spectrum of LTT 1445 A b

Natasha E. Batalha , Nicole Wallack , Tyler Gordon , Nicholas F. Wogan , Katherine A. Bennett , Jea Adams Redai , Mercedes L\'opez-Morales , Johanna Teske
show 11 more authors
Jeff Valenti Munazza K. Alam Lili Alderson Artyom Aguichine Natalie M. Batalha Anna Gagnebin Peter Gao Annabella Meech Sarah E. Moran Hannah R. Wakeford Angie Wolfgang
Authors on Pith no claims yet

Pith reviewed 2026-05-15 03:11 UTC · model grok-4.3

classification 🌌 astro-ph.EP
keywords LTT 1445 A bJWST transmission spectrumexoplanet atmospheremetallicity limitsrocky planetNIRSpec G395Hgrey clouds
0
0 comments X

The pith

The JWST 3-5 micron transmission spectrum of LTT 1445 A b shows no detectable atmospheric features, limiting metallicity to at least 350 times solar under grey cloud models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reports the first JWST NIRSpec/G395H transmission spectrum of the rocky planet LTT 1445 A b over 3-5 microns from a single visit. Two independent reductions achieve median precisions of 23 ppm in NRS1 and 36 ppm in NRS2 across spectroscopic channels, with no statistically significant features appearing in the data. Using chemical equilibrium models that include grey opaque clouds, the non-detection sets a lower limit of roughly 350 times solar metallicity when the cloud deck sits deeper than 0.01 bar. Combining the JWST data with prior HST/WFC3 observations tightens the limit to 500 times solar. The authors note that additional transit and emission observations will be needed to search for any remaining atmospheric signatures.

Core claim

The 3-5 μm transmission spectrum of LTT 1445 A b obtained with JWST NIRSpec/G395H exhibits no statistically significant spectral features at the achieved precision, which in turn constrains the atmospheric metallicity to ≳350× solar for grey opaque cloud decks at pressures greater than 0.01 bar when chemical equilibrium models are assumed.

What carries the argument

The NIRSpec/G395H transmission spectrum reduced independently with Eureka! and ExoTiC-JEDI pipelines, interpreted through a grid of chemical equilibrium models that incorporate grey opaque clouds to translate the featureless spectrum into metallicity bounds.

If this is right

  • The atmosphere must be either extremely metal-rich or blanketed by thick clouds to explain the flat spectrum.
  • Combining JWST and HST data extends the metallicity lower bound to ≳500× solar.
  • Future transit and emission spectroscopy campaigns are required to test whether any atmospheric features become detectable at higher precision.
  • The current single-visit precision already rules out low-metallicity scenarios under the adopted cloud and chemistry assumptions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar high-metallicity or cloudy atmospheres may be common among close-in rocky planets around M dwarfs if this limit holds for the broader population.
  • Multi-visit observations could distinguish between a high-metallicity gas envelope and a truly bare rocky surface.
  • The wavelength range and precision achieved here set a practical benchmark for planning atmospheric studies of other nearby terrestrial exoplanets.

Load-bearing premise

The models assume that grey opaque clouds and chemical equilibrium capture all plausible atmospheres, so the lack of features truly excludes lower metallicities rather than arising from incomplete physics or residual systematics in the single-visit dataset.

What would settle it

A clear spectral feature appearing in a future multi-visit JWST transit observation or an emission spectrum that indicates lower metallicity would directly contradict the high-metallicity limit derived here.

Figures

Figures reproduced from arXiv: 2605.15003 by Angie Wolfgang, Annabella Meech, Anna Gagnebin, Artyom Aguichine, Hannah R. Wakeford, Jea Adams Redai, Jeff Valenti, Johanna Teske, Katherine A. Bennett, Lili Alderson, Mercedes L\'opez-Morales, Munazza K. Alam, Natalie M. Batalha, Natasha E. Batalha, Nicholas F. Wogan, Nicole Wallack, Peter Gao, Sarah E. Moran, Tyler Gordon.

Figure 1
Figure 1. Figure 1: White light curves and associated residuals for both reduction methods with NRS1 in blue and NRS2 in pink. The Eureka! data reduction is on the left and ExoTiC-JEDI data reduction is on the right. The points missing in the post-transit baseline were removed automatically using the outlier rejection for Eureka! and manually for ExoTiC-JEDI due to a high gain antenna move. 10 0 10 1 10 2 Bin Size (Number of … view at source ↗
Figure 2
Figure 2. Figure 2: RMS versus bin size for the white light curves of NRS1 in purple and NRS2 in pink. The residuals would follow the solid lines in the absence of red noise [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Spectrum of LTT 1445A b from both the ExoTiC-JEDI and Eureka! reductions. While there is an offset in the baselines for the two reductions in NRS1, the overall shapes of the spectra are in good agreement. 3.0 3.5 4.0 4.5 5.0 Wavelength (microns) 20 25 30 35 40 45 50 55 Transit Depth Precision (ppm) PandExo Expected Errors Eureka! ExoTiC-JEDI [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The expected errors on the transmission spec￾trum from PandExo (Batalha et al. 2017a), compared to the measured errors from our two reductions. Eureka! median precision of 36.4 ppm and ExoTiC-JEDI median precision of 36.7 ppm. Therefore, overall the quality of the spectral precision is in good alignment with predictions made prior to taking the observations, although slightly poorer in NRS2 as shown in pre… view at source ↗
Figure 5
Figure 5. Figure 5: Non-physical model interpretation of ExoTiC-JEDI (left) and Eureka! (right) reductions, showing structural dif￾ferences between the two. Each column contains a different model (outlined in §3.1) and is roughly ordered by increasing complexity. In individual figures the black line represents the median retrieved model, light pink the 1σ-banded model, and dark pink the 3σ-banded model. Statistical quantities… view at source ↗
Figure 6
Figure 6. Figure 6: Curves demonstrate how well the LTT 1445A b data can rule out atmospheric metallicity (and corresponding mean molecular weight). Solid and dashed lines represent the Eureka! and ExoTiC-JEDI reductions, respectively. In the left figure (a) all curves are computed with no opaque pressure level (“cloud-free”). Additionally, in (a) the dark lines show results for solar C/O (=0.458) and transparent lines show c… view at source ↗
Figure 7
Figure 7. Figure 7: Posterior probability distributions for the atmospheric abundances of main (either CO2, H2O, or CH4) gas species versus the opaque pressure level in our three-gas model fits. Each of these models are computed with H2/He backgrounds. The two-dimensional marginal distributions are shown with shaded contours representing the 1σ (39.3%), 2σ (86.5%), and 3σ (98.9%) credible regions. White space represents regio… view at source ↗
Figure 8
Figure 8. Figure 8: Spectra showing Eureka! data reduction with four representative models to contextualize the fitting results discussed in §3.3. We can rule out 100 × Solar metallicities with high confidence because of the large CH4 features present at 3.3 µm. Toward high metallicity (> 300 × Solar), a smaller amplitude CO2 feature becomes visible from 4.2-4.5µm, which is also not present in our observation. Lastly, we show… view at source ↗
Figure 9
Figure 9. Figure 9: On the left, we show our data in comparison with HST data from (Bennett et al. 2025a). Along with the data we show the 2σ (light pink) and 3σ (dark pink) widths for the retrieved transit depth baseline of each dataset. In dark blue we show a 100 × Solar metallicity model purely for reference. On the right, we show our ability to infer atmospheric metallicity when considering the data from Bennett et al. (2… view at source ↗
Figure 10
Figure 10. Figure 10: Left: Simulated emission spectra for various atmospheric compositions and thicknesses (all with zero surface albedo) compared to the MIRI LRS observations published in Wachiraphan et al. (2025). The black line is a zero-albedo blackbody (bare rock) and the cyan line is a 0.1 bar pure O2 atmosphere. Blue, orange, green and red lines are 500 × Solar metallicity atmospheres at chemical equilibrium. As a remi… view at source ↗
Figure 11
Figure 11. Figure 11: Simulated NIRISS, NIRCam, and NIRSpec observations of all 12 currently planned transits allocated for LTT 1445A b. In total this includes 2 visits of NIRSS/SOSS, 2 visits of NIRSpec/G395H, 4 visits of NIRCam/F322W2, and 4 visits of NIRCam/F444W from Programs #2512, #7073, and #7251. The NIRCam/DHS mode, which provides shortwave coverage, will also be utilized in #7251. All band wavelength ranges are shown… view at source ↗
read the original abstract

The search for an atmosphere on the closest rocky M dwarf planet, LTT 1445 A b, has been the subject of intense investigation from both the ground and space. Here, we present the first JWST transmission spectrum of LTT 1445 A b using a single visit spanning 3-5~$\mu$m using NIRSpec/G395H. We conduct two independent reductions of the data using both the Eureka! and ExoTiC-JEDI pipelines. Overall, we measure the NRS1 transit depths to a median precision of $\sim23$~ppm in 41 spectroscopic channels with uniform widths of 30 pixels ($\sim$ 0.02 $\mu$m), and the NRS2 transit depths to $\sim36$~ppm precision in 65 spectroscopic channels, also with uniform widths of 30 pixels. We rule out any statistically significant spectral features at this precision and place limits on atmospheric metallicity using a grid of chemical equilibrium models with grey opaque clouds. Using NIRSpec/G395H alone, we can place limits on the atmospheric metallicity of $\gtrsim350~\times$ Solar when the opaque pressure level is greater than 0.01~bars. We also conduct a combined analysis of JWST/NIRSpec and HST/WFC3 transmission data and find our atmospheric limits can be extended $\gtrsim500~\times$ Solar when considering both datasets. Future analyses both in transit and emission will uncover whether there are detectable atmospheric features.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents the first JWST NIRSpec/G395H transmission spectrum of the rocky exoplanet LTT 1445 A b over 3-5 μm from a single transit visit. Two independent data reductions (Eureka! and ExoTiC-JEDI) agree on transit depths to median precisions of ~23 ppm (NRS1, 41 channels) and ~36 ppm (NRS2, 65 channels). No statistically significant spectral features are detected. Limits on atmospheric metallicity (≳350× solar for opaque pressure levels >0.01 bar using JWST alone; ≳500× solar when combined with HST/WFC3) are derived from a grid of chemical-equilibrium forward models that include grey opaque clouds at fixed pressure levels.

Significance. If the non-detection is robust and the model assumptions appropriate, the result supplies useful observational constraints on a nearby rocky planet's atmosphere and demonstrates JWST's precision for such targets. The dual-pipeline agreement is a clear strength, as is the straightforward reporting of the null result. The quantitative metallicity bounds are model-dependent but provide a concrete benchmark for future work under the stated assumptions of chemical equilibrium and grey clouds.

major comments (1)
  1. [§4] §4 (Atmospheric constraints): The metallicity limits of ≳350× solar (abstract and §4) are obtained by comparing the observed flat spectrum against a grid of chemical-equilibrium models with grey opaque clouds. For the non-detection to translate into these bounds, every lower-metallicity atmosphere must produce a feature exceeding the 23–36 ppm precision. The manuscript does not test or discuss the effects of disequilibrium chemistry, photochemistry, or non-grey/patchy clouds; without such checks the quantitative claim rests on the completeness of the chosen grid rather than the data alone.
minor comments (2)
  1. [Results] The text should explicitly state the exact wavelength ranges and channel counts for NRS1 and NRS2 in the results section to match the abstract values.
  2. [Figures] Figure captions for the transmission spectrum should include a direct statement of the median precision per channel and note which models are overplotted.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive assessment and recommendation for minor revision. We address the major comment below.

read point-by-point responses

  1. Referee: [§4] §4 (Atmospheric constraints): The metallicity limits of ≳350× solar (abstract and §4) are obtained by comparing the observed flat spectrum against a grid of chemical-equilibrium models with grey opaque clouds. For the non-detection to translate into these bounds, every lower-metallicity atmosphere must produce a feature exceeding the 23–36 ppm precision. The manuscript does not test or discuss the effects of disequilibrium chemistry, photochemistry, or non-grey/patchy clouds; without such checks the quantitative claim rests on the completeness of the chosen grid rather than the data alone.

    Authors: We agree that the reported metallicity limits (≳350× solar from JWST alone) are derived under the assumptions of chemical equilibrium and grey opaque clouds at fixed pressure levels, as explicitly described in §4 and the abstract. The non-detection is robust across both reductions, but the quantitative translation to metallicity does rely on the completeness of this particular model grid. In the revised manuscript we will add a clarifying paragraph in §4 (and a brief note in the abstract) stating that these bounds assume chemical equilibrium and grey clouds, and that disequilibrium chemistry, photochemistry, or non-grey/patchy clouds could in principle permit lower metallicities without producing features above our 23–36 ppm precision. Such explorations are beyond the scope of the present work, which provides initial observational constraints using standard modeling assumptions; the featureless spectrum itself remains a model-independent result. revision: yes

Circularity Check

0 steps flagged

No significant circularity: observational non-detection with external forward-model limits

full rationale

The paper's chain begins with raw JWST NIRSpec/G395H time-series data processed through two fully independent pipelines (Eureka! and ExoTiC-JEDI). Transit depths are measured in fixed-width spectroscopic channels and tested for statistically significant features; none are found. Metallicity limits (≳350× solar for P_cloud > 0.01 bar) are then obtained by comparing the observed spectrum against a pre-computed grid of chemical-equilibrium forward models that assume grey opaque clouds. These models are external to the present dataset and are not fitted to it; the non-detection simply excludes the subset of the grid that would produce detectable features above the measured precision. No self-definitional equations, fitted-input predictions, load-bearing self-citations, or ansatz smuggling appear in the derivation. The result is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Limits rest on standard assumptions of chemical equilibrium and grey clouds rather than new free parameters or invented entities; the 0.01 bar pressure threshold is a model choice used to report the bound.

free parameters (1)
  • opaque pressure level threshold
    Model grid parameter chosen to report the metallicity limit; value of 0.01 bars defines when the ≳350× solar bound applies.
axioms (1)
  • domain assumption Chemical equilibrium models with grey opaque clouds accurately capture possible atmospheric compositions
    Invoked to convert non-detection into metallicity limits

pith-pipeline@v0.9.0 · 5659 in / 1287 out tokens · 50271 ms · 2026-05-15T03:11:47.084160+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

96 extracted references · 96 canonical work pages

  1. [1]

    L., et al

    Adams Redai, J., Wogan, N., Wallack, N. L., et al. 2025, AJ, 170, 219, doi: 10.3847/1538-3881/adee92

    work page doi:10.3847/1538-3881/adee92 2025

  2. [2]

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

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

    work page doi:10.3847/1538-3881/ad8eb5 2025

  3. [3]

    2022, Exo-TiC/ExoTiC-JEDI: v0.1-beta-release, v0.1, Zenodo, Zenodo, doi: 10.5281/zenodo.7185855

    Alderson, L., Grant, D., & Wakeford, H. 2022, Exo-TiC/ExoTiC-JEDI: v0.1-beta-release, v0.1, Zenodo, Zenodo, doi: 10.5281/zenodo.7185855

    work page doi:10.5281/zenodo.7185855 2022

  4. [4]

    R., Alam, M

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

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

  5. [5]

    E., Wakeford, H

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

    work page doi:10.3847/1538-3881/ad32c9 2024

  6. [6]

    E., Wallack, N

    Alderson, L., Moran, S. E., Wallack, N. L., et al. 2025, AJ, 169, 142, doi: 10.3847/1538-3881/adad64

    work page doi:10.3847/1538-3881/adad64 2025

  7. [7]

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

    Allard, N. F., Spiegelman, F., Leininger, T., & Molliere, P. 2019, A&A, 628, A120, doi: 10.1051/0004-6361/201935593

    work page doi:10.1051/0004-6361/201935593 2019

  8. [8]

    H., Espinoza, N., Boehm, V

    Allen, N. H., Espinoza, N., Boehm, V. A., et al. 2026, AJ, 171, 105, doi: 10.3847/1538-3881/ae28cb

    work page doi:10.3847/1538-3881/ae28cb 2026

  9. [9]

    Naumenko, O. V. 2016, Monthly Notices of the Royal Astronomical Society, 460, 4063, doi: 10.1093/mnras/stw1133

    work page doi:10.1093/mnras/stw1133 2016

  10. [10]

    B., Latouf, N., et al

    Barclay, T., Sheppard, K. B., Latouf, N., et al. 2025, AJ, 169, 241, doi: 10.3847/1538-3881/ada5f6

    work page doi:10.3847/1538-3881/ada5f6 2025

  11. [11]

    2025, Resampled Opacity Database for PICASO, Zenodo, doi: 10.5281/zenodo.14861730

    Batalha, N., Freedman, R., Gharib-Nezhad, E., & Lupu, R. 2025, Resampled Opacity Database for PICASO, Zenodo, doi: 10.5281/zenodo.14861730

    work page doi:10.5281/zenodo.14861730 2025

  12. [12]

    2026, Data for: ”JWST COMPASS Program: The 3–5µm transmission spectrum of LTT 1445 A b”, Zenodo, doi: 10.5281/zenodo.19957166

    Batalha, N., Wallack, N., & Gordon, T. 2026, Data for: ”JWST COMPASS Program: The 3–5µm transmission spectrum of LTT 1445 A b”, Zenodo, doi: 10.5281/zenodo.19957166

    work page doi:10.5281/zenodo.19957166 2026

  13. [13]

    2021, Seeing the Forest and the Trees: Unveiling Small Planet Atmospheres with a Population-Level Framework, JWST Proposal

    Batalha, N., Teske, J., Alam, M., et al. 2021, Seeing the Forest and the Trees: Unveiling Small Planet Atmospheres with a Population-Level Framework, JWST Proposal. Cycle 1, ID. #2512

    work page 2021

  14. [14]

    E., Marley, M

    Batalha, N. E., Marley, M. S., Lewis, N. K., & Fortney, J. J. 2019a, ApJ, 878, 70, doi: 10.3847/1538-4357/ab1b51 —. 2019b, ApJ, 878, 70, doi: 10.3847/1538-4357/ab1b51

    work page doi:10.3847/1538-4357/ab1b51

  15. [15]

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

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

    work page doi:10.1088/1538-3873/aa65b0

  16. [16]

    E., Rooney, C

    Batalha, N. E., Rooney, C. M., Visscher, C., et al. 2026, AJ, 171, 98, doi: 10.3847/1538-3881/ae29e5

    work page doi:10.3847/1538-3881/ae29e5 2026

  17. [17]

    2022, The Journal of Open Source Software, 7, 4503, doi: 10.21105/joss.04503

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

    work page doi:10.21105/joss.04503 2022

  18. [18]

    A., et al

    Bello-Arufe, A., Damiano, M., Bennett, K. A., et al. 2025, ApJL, 980, L26, doi: 10.3847/2041-8213/adaf22

    work page doi:10.3847/2041-8213/adaf22 2025

  19. [19]

    A., Sing, D

    Bennett, K. A., Sing, D. K., Stevenson, K. B., et al. 2025a, AJ, 169, 111, doi: 10.3847/1538-3881/ad9dd1

    work page doi:10.3847/1538-3881/ad9dd1

  20. [20]

    A., MacDonald, R

    Bennett, K. A., MacDonald, R. J., Peacock, S., et al. 2025b, AJ, 170, 205, doi: 10.3847/1538-3881/adf198

    work page doi:10.3847/1538-3881/adf198

  21. [21]

    K., Wachiraphan, P., & Murray, C

    Berta-Thompson, Z. K., Wachiraphan, P., & Murray, C. 2025, arXiv e-prints, arXiv:2507.02136, doi: 10.48550/arXiv.2507.02136

    work page doi:10.48550/arxiv.2507.02136 2025

  22. [22]

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

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

    work page doi:10.21105/joss.03001 2021

  23. [23]

    2025, JWST Calibration Pipeline, 1.18.0, doi: 10.5281/zenodo.7038885

    Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2025, JWST Calibration Pipeline, 1.18.0, doi: 10.5281/zenodo.7038885

    work page doi:10.5281/zenodo.7038885 2025

  24. [24]

    B., Fisher, C

    Claringbold, A. B., Fisher, C. E., Kirk, J., et al. 2026, MNRAS, 546, stag143, doi: 10.1093/mnras/stag143

    work page doi:10.1093/mnras/stag143 2026

  25. [25]

    2025, AJ, 170, 226, doi: 10.3847/1538-3881/adfc6a

    Coulombe, L.-P., Benneke, B., Krissansen-Totton, J., et al. 2025, AJ, 170, 226, doi: 10.3847/1538-3881/adfc6a

    work page doi:10.3847/1538-3881/adfc6a 2025

  26. [26]

    D., Bierson, C

    Dai, L., Zhang, X., Shao, W. D., Bierson, C. J., & Cui, J. 2022, Journal of Geophysical Research (Planets), 127, e07060, doi: 10.1029/2021JE007060

    work page doi:10.1029/2021je007060 2022

  27. [27]

    2024, ApJL, 968, L22, doi: 10.3847/2041-8213/ad5204

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

    work page doi:10.3847/2041-8213/ad5204 2024

  28. [28]

    2022, The Astronomical Journal, 164, 225 de Wit, J., Wakeford, H

    Damiano, M., Hu, R., Barclay, T., et al. 2022, The Astronomical Journal, 164, 225 de Wit, J., Wakeford, H. R., Gillon, M., et al. 2016, Nature, 537, 69

    work page 2022

  29. [29]

    Buchhave, L. A. 2023, AJ, 165, 169, doi: 10.3847/1538-3881/acbf39

    work page doi:10.3847/1538-3881/acbf39 2023

  30. [30]

    H., Glidden, A., et al

    Espinoza, N., Allen, N. H., Glidden, A., et al. 2025, ApJL, 990, L52, doi: 10.3847/2041-8213/adf42e

    work page doi:10.3847/2041-8213/adf42e 2025

  31. [31]

    E., Hooton, M

    Fisher, C. E., Hooton, M. J., Gressier, A., et al. 2026, MNRAS, 545, staf2187, doi: 10.1093/mnras/staf2187

    work page doi:10.1093/mnras/staf2187 2026

  32. [32]

    W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , http://adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

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

    work page doi:10.1086/670067 2013

  33. [33]

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

    Fortune, M., Gibson, N. P., Diamond-Lowe, H., et al. 2025, A&A, 701, A25, doi: 10.1051/0004-6361/202554198

    work page doi:10.1051/0004-6361/202554198 2025

  34. [34]

    Lewis, N. K. 2017, AJ, 153, 139, doi: 10.3847/1538-3881/aa5fab 18

    work page doi:10.3847/1538-3881/aa5fab 2017

  35. [35]

    2022, Astronomy & Astrophysics, 665, A19

    Garcia, L., Moran, S., Rackham, B., et al. 2022, Astronomy & Astrophysics, 665, A19

    work page 2022

  36. [36]

    R., Line, M

    Gharib-Nezhad, E., Iyer, A. R., Line, M. R., et al. 2021, ApJS, 254, 34, doi: 10.3847/1538-4365/abf504

    work page doi:10.3847/1538-4365/abf504 2021

  37. [37]

    J., et al

    Gillon, M., Ducrot, E., Bell, T. J., et al. 2025, arXiv e-prints, arXiv:2509.02128, doi: 10.48550/arXiv.2509.02128

    work page doi:10.48550/arxiv.2509.02128 2025

  38. [38]

    2025, ApJL, 990, L53, doi: 10.3847/2041-8213/adf62e

    Glidden, A., Ranjan, S., Seager, S., et al. 2025, ApJL, 990, L53, doi: 10.3847/2041-8213/adf62e

    work page doi:10.3847/2041-8213/adf62e 2025

  39. [39]

    E., Rothman, L

    Gordon, I. E., Rothman, L. S., Tan, Y., Kochanov, R. V., & Hill, C. 2017, in 72nd International Symposium on Molecular Spectroscopy, TJ08, doi: 10.15278/isms.2017.TJ08

    work page doi:10.15278/isms.2017.tj08 2017

  40. [40]

    A., Batalha, N

    Gordon, T. A., Batalha, N. M., Batalha, N. E., et al. 2026, AJ, 171, 178, doi: 10.3847/1538-3881/ae3de9

    work page doi:10.3847/1538-3881/ae3de9 2026

  41. [41]

    2024, The Journal of Open Source Software, 9, 6816, doi: 10.21105/joss.06816

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

    work page doi:10.21105/joss.06816 2024

  42. [42]

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

    work page doi:10.5281/zenodo.7437681 2022

  43. [43]

    P., Bell, T

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

    work page doi:10.1038/s41586-023-05951-7 2023

  44. [44]

    2022, A&A, 658, A133, doi: 10.1051/0004-6361/202142140

    Gressier, A., Mori, M., Changeat, Q., et al. 2022, A&A, 658, A133, doi: 10.1051/0004-6361/202142140

    work page doi:10.1051/0004-6361/202142140 2022

  45. [45]

    H., et al

    Gressier, A., Espinoza, N., Allen, N. H., et al. 2024, ApJL, 975, L10, doi: 10.3847/2041-8213/ad73d1

    work page doi:10.3847/2041-8213/ad73d1 2024

  46. [46]

    2010, A&A, 520, A27, doi: 10.1051/0004-6361/200913396

    Guillot, T. 2010, A&A, 520, A27, doi: 10.1051/0004-6361/200913396

    work page doi:10.1051/0004-6361/200913396 2010

  47. [47]

    J., Gordon, I

    Hargreaves, R. J., Gordon, I. E., Rey, M., et al. 2020, ApJS, 247, 55, doi: 10.3847/1538-4365/ab7a1a

    work page doi:10.3847/1538-4365/ab7a1a 2020

  48. [48]

    R., Millman, K

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

    work page doi:10.1038/s41586-020-2649-2 2020

  49. [49]

    J., & Jao, W.-C

    Henry, T. J., & Jao, W.-C. 2024, ARA&A, 62, 593, doi: 10.1146/annurev-astro-052722-102740 H¨ orst, S. M., He, C., Lewis, N. K., et al. 2018, Nature Astronomy, 2, 303, doi: 10.1038/s41550-018-0397-0

    work page doi:10.1146/annurev-astro-052722-102740 2024

  50. [50]

    R., Freedman, R

    Huang, X., Gamache, R. R., Freedman, R. S., Schwenke, D. W., & Lee, T. J. 2014, Journal of Quantitative Spectroscopy and Radiative Transfer, 147, 134 , doi: https://doi.org/10.1016/j.jqsrt.2014.05.015

    work page doi:10.1016/j.jqsrt.2014.05.015 2014

  51. [51]

    M.-R., Lupu, R., Owusu-Asare, A., Slough, P., & Cale, B

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

    work page doi:10.1088/1538-3873/aa61ef 2017

  52. [52]

    B., Fu, G., et al

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

    work page doi:10.3847/1538-3881/ad19df 2024

  53. [53]

    B., et al

    Kirk, J., Ahrer, E.-M., Claringbold, A. B., et al. 2025, MNRAS, 537, 3027, doi: 10.1093/mnras/staf208

    work page doi:10.1093/mnras/staf208 2025

  54. [54]

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

    work page doi:10.3847/1538-4357/ac3b48 2022

  55. [55]

    Kopparapu, R. K. 2013, ApJL, 767, L8, doi: 10.1088/2041-8205/767/1/L8

    work page doi:10.1088/2041-8205/767/1/l8 2013

  56. [56]

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

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

    work page doi:10.1086/683602 2015

  57. [57]

    Fortney, J. J. 2024, Nature Communications, 15, 8374, doi: 10.1038/s41467-024-52642-6

    work page doi:10.1038/s41467-024-52642-6 2024

  58. [58]

    F., & Salpeter, E

    Lenzuni, P., Chernoff, D. F., & Salpeter, E. E. 1991, ApJS, 76, 759, doi: 10.1086/191580

    work page doi:10.1086/191580 1991

  59. [59]

    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

    work page doi:10.1088/0067-0049/216/1/15 2015

  60. [60]

    2022, The Astronomical Journal, 164, 59

    Diamond-Lowe, H., et al. 2022, The Astronomical Journal, 164, 59

    work page 2022

  61. [61]

    Lodders, K., Palme, H., & Gail, H. P. 2009, Landolt B¨ ornstein, 4B, 712, doi: 10.1007/978-3-540-88055-434

    work page doi:10.1007/978-3-540-88055-434 2009

  62. [62]

    P., Xue, Q., et al

    Luque, R., Coy, B. P., Xue, Q., et al. 2025, AJ, 170, 49, doi: 10.3847/1538-3881/addb40

    work page doi:10.3847/1538-3881/addb40 2025

  63. [63]

    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

    work page doi:10.1038/s41550-023-02064-z 2023

  64. [64]

    Lyons, J. R. 2008, Journal of Sulfur Chemistry, 29, 269

    work page 2008

  65. [65]

    E., Morley, C

    Mang, J., Batalha, N. E., Morley, C. V., et al. 2026, arXiv e-prints, arXiv:2602.22468, doi: 10.48550/arXiv.2602.22468

    work page doi:10.48550/arxiv.2602.22468 2026

  66. [66]

    M., MacDonald, R

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

    work page doi:10.3847/2041-8213/ad054f 2023

  67. [67]

    L., et al

    Meech, A., Gao, P., Wallack, N. L., et al. 2026, arXiv e-prints, arXiv:2602.22329, doi: 10.48550/arXiv.2602.22329

    work page doi:10.48550/arxiv.2602.22329 2026

  68. [68]

    Wakeford, H. R. 2018, AJ, 156, 252, doi: 10.3847/1538-3881/aae83a

    work page doi:10.3847/1538-3881/aae83a 2018

  69. [69]

    E., H¨ orst, S

    Moran, S. E., H¨ orst, S. M., He, C., et al. 2022, Journal of Geophysical Research (Planets), 127, e06984, doi: 10.1029/2021JE006984

    work page doi:10.1029/2021je006984 2022

  70. [70]

    E., Stevenson, K

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

    work page doi:10.3847/2041-8213/accb9c 2023

  71. [71]

    K., Winters, J

    Pass, E. K., Winters, J. G., Charbonneau, D., et al. 2023, AJ, 166, 171, doi: 10.3847/1538-3881/acf561

    work page doi:10.3847/1538-3881/acf561 2023

  72. [72]

    L., Kyuberis, A

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

    work page doi:10.1093/mnras/sty1877 2018

  73. [73]

    2025, ApJL, 979, L5, doi: 10.3847/2041-8213/ada381

    Radica, M., Piaulet-Ghorayeb, C., Taylor, J., et al. 2025, ApJL, 979, L5, doi: 10.3847/2041-8213/ada381

    work page doi:10.3847/2041-8213/ada381 2025

  74. [74]

    S., Gordon, I

    Rothman, L. S., Gordon, I. E., Babikov, Y., et al. 2013, Journal of Quantitative Spectroscopy and Radiative Transfer, 130, doi: 10.1016/j.jqsrt.2013.07.002

    work page doi:10.1016/j.jqsrt.2013.07.002 2013

  75. [75]

    2025, Nature Astronomy, doi: 10.1038/s41550-025-02723-3 19

    Roy, P.-A., Benneke, B., Fournier-Tondreau, M., et al. 2025, Nature Astronomy, doi: 10.1038/s41550-025-02723-3 19

    work page doi:10.1038/s41550-025-02723-3 2025

  76. [76]

    Freedman, R. S. 2012, ApJ, 750, 74, doi: 10.1088/0004-637X/750/1/74

    work page doi:10.1088/0004-637x/750/1/74 2012

  77. [77]

    R., et al

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

    work page doi:10.3847/1538-3881/ad73cf 2024

  78. [78]

    2017, PASP, 129, 015001, doi: 10.1088/1538-3873/129/971/015001

    Schlawin, E., Rieke, M., Leisenring, J., et al. 2017, PASP, 129, 015001, doi: 10.1088/1538-3873/129/971/015001

    work page doi:10.1088/1538-3873/129/971/015001 2017

  79. [79]

    D., et al

    Taylor, J., Radica, M., Chatterjee, R. D., et al. 2025, MNRAS, 540, 3677, doi: 10.1093/mnras/staf894

    work page doi:10.1093/mnras/staf894 2025

  80. [80]

    E., Wallack, N

    Teske, J., Batalha, N. E., Wallack, N. L., et al. 2025a, AJ, 169, 249, doi: 10.3847/1538-3881/adb975 —. 2025b, AJ, 169, 249, doi: 10.3847/1538-3881/adb975

    work page doi:10.3847/1538-3881/adb975

Showing first 80 references.