Pith. sign in

REVIEW 6 minor 102 references

The SPACE Program II: No discernible spectral features in the transmission spectrum of the sub-Neptune HD 191939 b observed with HST/WFC3

T0 review · 0 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Pith's one-line read The HST/WFC3 transmission spectrum of the sub-Neptune HD 191939 b, reduced three independent ways, is featureless and consistent with a flat line, giving 2.0–3.2σ evidence against a clear solar-metallicity atmosphere.

desk verdict A careful, honest flat-line WFC3 result for a 880 K sub-Neptune; the stellar-activity caveat is real but handled as well as the data allow. read the letter →

arxiv 2608.05962 v1 pith:ULMKNHNW submitted 2026-08-06 astro-ph.EP

classification astro-ph.EP
keywords exoplanetatmospherestransmissionspectroscopysub-NeptuneHD191939bHST/WFC3cloudsandhazesstellaractivityatmosphericmetallicity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper reports the near-infrared transmission spectrum of the sub-Neptune HD 191939 b, a planet of about 3.4 Earth radii orbiting a G-type star 53.6 parsecs away, observed with Hubble's WFC3 camera as part of the SPACE program and reduced with three independent pipelines. The central result is that the spectrum shows no discernible spectral features between 1.1 and 1.7 microns: it is statistically consistent with a flat line, with the strongest reduction rejecting a flat line at only 1.3σ. Against a cloud-free, solar-metallicity, equilibrium-chemistry atmosphere, the data deliver moderate evidence of 2.0–3.2σ, so that scenario is deemed unlikely but still possible. The paper concludes that aerosol hazes, condensate clouds, or a high-mean-molecular-weight (super-solar-metallicity) atmosphere is the likely reason the features are muted, and it places the flat spectrum in the context of an apparent population trend in which sub-Neptunes may mute absorption more strongly than similarly warm giant planets.

What carries the argument

The load-bearing object is the co-added WFC3 transmission spectrum of HD 191939 b, binned into 27 channels from 1.12 to 1.66 μm and produced by three independent reduction pipelines — PACMAN, Eureka!, and Iraclis — so that pipeline-level assumptions are not the source of the flat result. The statistical workhorse is the null-hypothesis test: the observed transit depths are compared with a flat line and with a forward model of a cloud-free, solar-metallicity, equilibrium-chemistry atmosphere, with chi-square p-values converted into Gaussian significance levels. The atmospheric retrievals use the petitRADTRANS framework, which supplies the model spectra and the flat-line reference against which all molecular models are scored by Bayes factors. The host star's STIS ultraviolet spectrum and optical photometric monitoring provide the context needed to interpret a possible stellar-activity bias, particularly during the third visit.

What would settle it

A decisive check is to observe additional transits of HD 191939 b when the host star is photometrically quiet and to re-derive the co-added spectrum with the same three pipelines; if a molecular feature such as the 1.4 μm water band emerges, the flat-line claim fails. A complementary test is to model the stellar contamination directly using the measured BVRI photometric variability and spot or facula contrasts; if removing the wavelength-dependent contamination turns the flat spectrum into one with features, the flatness is an artifact of stellar activity.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that HD 191939 b's WFC3 transmission spectrum is featureless at the precision of these observations. The co-added transit depths are constant with wavelength whether the data are reduced with PACMAN, Eureka!, or Iraclis, three independent pipelines that differ in calibration, extraction, and fitting choices. Against a cloud-free solar-metallicity equilibrium-chemistry model, the data reject at 3.2σ (PACMAN, all visits), 2.0σ (Eureka!), and 2.7σ (Iraclis); when the possibly activity-contaminated Visit 3 is excluded from the PACMAN reduction, the rejection is 2.4σ. Against a flat line, none of the reductions is rejected beyond 1.3σ. In Bayesian retrievals, no molecular model (equilibrium chemistry, free chemistry, H2O only, CH4 only) is significantly preferred over a flat line, with all Bayes factors below the 'barely worth mentioning' threshold, and the posteriors expose a cloud-metallicity degeneracy: high-altitude aerosols, a high mean molecular weight, or both can reproduce the same flat spectrum. The paper reads this as probable aerosol cover or metal enrichment, while explicitly allowing that a clear solar-metallicity atmosphere cannot be conclusively ruled out.

Load-bearing premise

The conclusion that the spectrum is genuinely featureless assumes that stellar activity, specifically unocculted star spots or bright faculae, did not imprint a wavelength-dependent bias on the measured transits; if such a bias occurred during the third visit, real planetary absorption features could be hidden in the combined flat spectrum.

Editorial extensions

If this is right

  • If the flat spectrum is genuine, HD 191939 b's atmosphere is likely veiled by hydrocarbon haze, condensate clouds, a high-mean-molecular-weight (super-solar-metallicity) composition, or some combination of these.
  • The moderate 2.0–3.2σ rejection of a clear, solar-metallicity, equilibrium-chemistry atmosphere weakens the simplest picture of a feature-rich, hydrogen-dominated sub-Neptune at an equilibrium temperature of about 880 K.
  • A super-solar metallicity for HD 191939 b would fit the known trend of increasing atmospheric metal enrichment toward lower planet masses, adding another metal-enriched sub-Neptune to the growing sample.
  • Relative to the population relation between scale-height-normalized H2O feature amplitude and equilibrium temperature, HD 191939 b's feature is smaller but still consistent within 1.85σ; if the discrepancy strengthens, sub-Neptunes may mute spectral features more efficiently than comparable-temperature giant planets.
  • Because stellar activity during one visit can shift measured transit depths, future transit observations of HD 191939 b, c, and d should be accompanied by simultaneous photometric monitoring and activity characterization.

Reading between the lines

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

  • The cloud-metallicity degeneracy means a featureless WFC3 spectrum cannot by itself distinguish a hazy low-metallicity envelope from a clear metal-rich one; a measurement of a molecular feature or of a cloud-opacity spectral slope at longer wavelengths would break this degeneracy.
  • If unocculted star spots or faculae contaminated Visit 3, the co-added flatness could be partly instrumental rather than planetary; a transit observation taken when the star is photometrically quiet, analyzed against the measured spot and facula contrasts, would settle this directly.
  • The population implication left open by the paper is that planet radius may matter as much as equilibrium temperature in setting haze and metal enrichment; this could be tested by comparing sub-Neptunes and giant planets across the same temperature range in the growing SPACE sample.
  • If HD 191939 b does turn out to be metal-rich, the system's outer giant planets provide a formation tie-in, because such giants may have blocked volatile-rich pebble drift into the inner disk; a future measurement of a high carbon-to-oxygen ratio would support that formation channel.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 6 minor

Summary. HD 191939 b, a 3.41 R⊕ sub-Neptune at T_eq ≈ 880 K, was observed with HST/WFC3 G141 during three transits. The paper reduces the data with three independent pipelines (PACMAN, Eureka!, Iraclis), yielding transmission spectra that are mutually consistent and consistent with a flat line: the flat-line null is rejected at only 0.08–1.3σ depending on pipeline, while a clear, solar-metallicity equilibrium-chemistry forward model is disfavored at 2.0–3.2σ. Atmospheric retrievals with petitRADTRANS find no model (H2O-only, CH4-only, free chemistry, equilibrium chemistry) that is preferred over a flat line (|ln B| < 1.15). STIS UV spectra and ground-based BVRI photometry are used to characterize stellar activity; Visit 3 coincides with a photometric maximum and shows anomalous white-light residuals, and excluding it reduces the rejection of the clear model to 2.4σ without changing the flat-spectrum conclusion. The authors interpret the flat spectrum as likely due to aerosols and/or a high mean molecular weight.

Significance. If the result stands, it provides a well-characterized sub-Neptune datum at ~880 K, extending the sample of featureless WFC3 spectra and challenging simple T_eq-based population trends such as the Brande et al. (2024) relation. The paper's main strengths are its three independent reductions with public pipelines, explicit frequentist null-hypothesis tests and Bayesian model comparison, transparent identification and exclusion of a possibly contaminated visit, and public data products. The residual risk from chromatic stellar contamination is real: the authors show that activity can affect Visit 3, and no contemporaneous photometry exists for Visits 1 and 2. However, this is an acknowledged caveat about the interpretation rather than an internal inconsistency in the measured spectrum, and the paper's central claim is appropriately limited to the absence of discernible features in the observed spectrum.

minor comments (6)
  1. [§1.3 and §2.2] The distance to HD 191939 is given as 53.61 pc in Section 1.3 but 53.91 pc in Section 2.2; the discrepancy should be resolved.
  2. [§5] The conclusions state that photometry suggests 'anomalously high apparent magnitudes' during Visit 3, but high magnitudes correspond to fainter fluxes, which contradicts the brightness maximum described in Sections 2.3 and 4.2; this should be rephrased as low magnitudes or high flux.
  3. [Table 3] Table 3 is labeled 'Bayesian evidences Z' but the entries are natural logarithms ln(Z); the label and the definition of the Bayes factors should be clarified.
  4. [References] The reference list contains a duplicate entry for Feroz et al. (2019); one copy should be removed.
  5. [§2.1.3] The pipeline name appears as both 'IRACLIS' in the section heading and 'Iraclis' in the text; the capitalization should be standardized.
  6. [§3.1] The sentence describing the constant-offset adjustment should state explicitly that the weighted mean of the model is matched to the weighted mean of the data, to avoid ambiguity about which quantity is being centered.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the featureless-spectrum result is a direct measurement cross-checked with three independent reduction pipelines.

full rationale

The central claim is an observational measurement, not a derivation: the transmission spectrum is produced directly from HST/WFC3 time-series fits (Section 2.1), and the featureless conclusion is a statistical description of the measured 27-channel transit depths (weighted averages and chi-square values in Figures 6-7). The null hypothesis tests (Section 3.1) compare the data to a forward equilibrium-chemistry model computed with petitRADTRANS from external line lists (Polyansky et al. 2018; Yurchenko et al. 2020; etc.) with fixed stellar and planetary parameters; the only fitted degree of freedom is a vertical offset equalizing the model and data means, which does not imprint spectral shape. The flat-line null is the mean of the data, used as a goodness-of-fit baseline, not as an independent prediction, and the paper does not present it as a derived result. Atmospheric retrievals (Section 3.2) fit free parameters and are used only to show that cloudy and high-metallicity scenarios remain consistent; no retrieved parameter is renamed a prediction. Self-citations to PACMAN (Zieba & Kreidberg 2022), the Kreidberg et al. (2014) systematics model, Kahle et al. (2025) photometry, and Brande et al. (2024) population fits are methodological or contextual and are not load-bearing; the reduction is cross-checked with Eureka! and Iraclis. The paper itself flags the Visit-3 stellar activity concern and the absence of photometry during Visits 1-2 (Section 4.2), which is a robustness and correctness limitation rather than a circular step. No equation or fitted parameter reduces to the claimed result by construction.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim (a flat transmission spectrum) is a direct measurement derived from the light curves and depends on the instrument systematics model and the adopted stellar parameters, not on the retrieval free parameters. The retrieval parameters (cloud pressure, metallicity, temperature) are only used to interpret the flatness as consistent with aerosols or a high mean-molecular-weight atmosphere. No new physical entities are introduced.

free parameters (4)
  • Cloud top pressure log10(Pcloud) = -2.52 +3.20/-1.72 (posterior median, PACMAN equilibrium retrieval, Figure 12)
    Free parameter in all atmospheric retrievals (Table 2). A grey cloud deck at this pressure can mute all molecular features. The posterior is unconstrained and spans the prior range, so it is an assumed mechanism for the flatness, not a measurement.
  • Atmospheric metallicity [M/H] = 2.19 +0.54/-1.99 (posterior median)
    Free parameter in the equilibrium-chemistry retrieval (Table 2). The paper invokes super-solar metallicity as a plausible explanation for the muted features (Section 4.3). The posterior is unconstrained, so it is a possible fit, not a detection.
  • Isothermal temperature T_iso = 768.57 +591.16/-568.29 K
    Free parameter in all retrievals. Degenerate with metallicity and cloud pressure; does not anchor the flat-spectrum conclusion.
  • Reference pressure log10(Pref) = -0.88 +1.97/-2.00
    Free parameter in all retrievals; unconstrained in the data.
assumptions (5)
  • domain assumption Stellar and planetary parameters (period, inclination, a/R*, mass, radius) fixed to literature values from Lubin et al. 2022 and Orell-Miquel et al. 2023.
    Used in the transit model fits (Section 2.1.1). Errors in these values would affect derived transit depths, but the flat-spectrum conclusion is robust to modest parameter errors.
  • domain assumption Limb darkening coefficients from model grids (Stagger, PHOENIX, Kurucz) are accurate for HD 191939 A.
    Fixed in the light curve fits; incorrect limb darkening can introduce wavelength-dependent biases in the transmission spectrum.
  • domain assumption The WFC3 systematics model (linear baseline + exponential ramp, with first-orbit treatment) adequately removes instrumental effects.
    Standard practice in the field (Kreidberg et al. 2014); residuals are near photon noise in spectroscopic channels, supporting the assumption.
  • domain assumption An isothermal pressure-temperature profile and a grey cloud deck adequately represent the planet's atmosphere in retrievals.
    Used in all petitRADTRANS retrievals; the paper notes the degeneracy between cloud and metallicity, and the conclusion about flatness does not depend on these assumptions.
  • domain assumption Equilibrium chemistry with solar metallicity and C/O=0.55 is a meaningful null hypothesis for the cloud-free case.
    The authors state it is 'unlikely that these assumptions are true for HD 191939 b', so it is a benchmark model, not a physical claim about the planet.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The SPACE Program II: No discernible spectral features in the transmission spectrum of the sub-Neptune HD 191939 b observed with HST/WFC3." pith.science (2026). https://pith.science/paper/ULMKNHNW

@misc{pith2026260805962,
  author       = {Pith},
  title        = {Pith review of: The SPACE Program II: No discernible spectral features in the transmission spectrum of the sub-Neptune HD 191939 b observed with HST/WFC3},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ULMKNHNW}},
  note         = {Machine review of arXiv:2608.05962}
}
abstract

The atmospheres of sub-Neptunes provide a window into their internal structure and history, shedding light on the origin of this common, but enigmatic, class of exoplanets. However, the physical and chemical processes that shape sub-Neptunes' transmission spectra, in particular cloud and haze formation, are not well understood. To identify possible correlations between transmission spectra and UV irradiation, the SPACE (Sub-neptune Planetary Atmosphere Characterization Experiment) Program observed an array of sub-Neptunes and their host stars using the Hubble Space Telescope (HST), measuring the planets' transmission spectra between $1.1\,\mu$m and $1.7\,\mu$m with the Wide Field Camera 3 (WFC3) and the stars' UV spectra with the Space Telescope Imaging Spectrograph (STIS). Here, we present the observations of HD 191939 b carried out as part of the SPACE Program, which reveal no significant spectral features in the transmission spectrum. The data deliver moderate evidence at significance levels between $2.0\,\sigma$ and $3.2\,\sigma$ against a cloud-free atmosphere with solar metallicity, rendering this scenario unlikely, but still possible. A super-solar metallicity of HD 191939 b might be consistent with the known trend of increasing atmospheric metallicity with decreasing planet mass. Both hydrocarbon haze formation and cloud condensation can be efficient at HD 191939 b's zero-albedo equilibrium temperature of $(880\pm 20)\,$K, particularly in atmospheres with super-solar metallicity, possibly additionally muting absorption features.

Figures

Figures reproduced from arXiv: 2608.05962 by the authors.

Figure 1
Figure 1. Mass-radius diagram of known exoplanets together with model curves taken from A. Aguichine et al. (2021) and L. Zeng et al. (2019). Exoplanet data shown with colored dots were taken from the NASA Exoplanet Archive (J. L. Christiansen et al. 2025) on April 2, 2026. The transiting exoplanets in the HD 191939 system are highlighted with larger markers, error bars and a red outline. hereafter Visits 1, 2 and 3) were obs… view at source ↗
Figure 2
Figure 2. White light curves of HD 191939 b’s primary transits observed with HST/WFC3 and reduced and mod￾eled with PACMAN. The systematics-corrected light curve only shows the exposures that were analyzed in the light curve fit. A reference line at zero was added in the residuals panel to guide the eye. after mid-transit shows an anomalously low flux, too (see [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Spectroscopic light curves and fit models with the corresponding residuals from the PACMAN reduction. The data are shown with alternating light and dark colors for visual clarity. Solid black lines at zero were added in the residuals’ panel for reference [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (9 more)
Figure 5
Figure 5. Figure 5: Normalized residual RMS σN /σ1 between best-fit models and data for the white and spectroscopic light curves from the PACMAN reduction as a function of the number of data points N per bin. The expected normalized residual RMS in the absence of red noise (F. Pont et al.…
Figure 4
Figure 4. Figure 4: Posteriors of the parameters of the spectroscopic light curve fits from the PACMAN reduction inferred using MCMCs. Black squares show the posteriors of parameters that are employed for all visits’ data and colored circles, dia￾monds and triangles depict posterior media…
Figure 6
Figure 6. Figure 6: HD 191939 b’s transmission spectrum observed with HST/WFC3 and reduced with independent reduction pipelines considering all visits and a model spectrum assuming no aerosols in the atmosphere and solar metallicity (see Section 3.1). Colored markers with error bars show …
Figure 7
Figure 7. Figure 7: HD 191939 b’s transmission spectrum obtained using the PACMAN and Eureka! reduction pipelines along with the best-fit model spectra from atmospheric retrievals run with petitRADTRANS. The upper row shows the data and models and the lower row shows the residuals of all …
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: STIS spectra of HD 191939 A combined with the Lyman α reconstruction. The spectrum is scaled to the distance of HD 191939 b and compared with the quiet Sun from T. N. Woods et al. (2009). The STIS spectrum has been downsampled to remove negative flux points [PITH_FULL…
Figure 10
Figure 10. Figure 10: Light curves of HD 191939 A in the BVRI bands observed with the automated 24-inch telescope at Van Vleck Observatory, Wesleyan University. Binned daily photometry is shown with large, colored dots. Each error bar represents the standard deviation of the underlying ind…
Figure 11
Figure 11. Figure 11: Lomb-Scargle periodograms of the binned daily photometry. We also display the corresponding 1% false-alarm probability (FAP) levels in the BVRI bands and an estimated upper limit of HD 191939 A’s rotational period from v sin i = (1.6 ± 0.3) km/s (J. Lubin et al. 2024)…
Figure 12
Figure 12. Figure 12: Posterior probabilities of the equilibrium chemistry retrieval on the PACMAN reduction considering all visits. Solid blue lines indicate the samples’ medians and dashed black lines show the 16th and 84th percentiles. cially good since all data points have overlapping …

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

102 extracted references · 2 canonical work pages

  1. [1]

    2021, ApJ, 914, 84, doi: 10.3847/1538-4357/abfa99

    Aguichine, A., Mousis, O., Deleuil, M., & Marcq, E. 2021, ApJ, 914, 84, doi: 10.3847/1538-4357/abfa99

  2. [2]

    2025, ApJL, 985, L10, doi: 10.3847/2041-8213/add010

    Ahrer, E.-M., Radica, M., Piaulet-Ghorayeb, C., et al. 2025, ApJL, 985, L10, doi: 10.3847/2041-8213/add010

  3. [3]

    B., Sing, D., et al

    Ashtari, R., Stevenson, K. B., Sing, D., et al. 2025, AJ, 169, 106, doi: 10.3847/1538-3881/ada353 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Astropy Collaboration...

  4. [4]

    2025, A&A, 700, A105, doi: 10.1051/0004-6361/202555577

    Tsai, S.-M. 2025, A&A, 700, A105, doi: 10.1051/0004-6361/202555577

  5. [5]

    N., Daylan, T., et al

    Badenas-Agusti, M., G¨ unther, M. N., Daylan, T., et al. 2020, AJ, 160, 113, doi: 10.3847/1538-3881/aba0b5

  6. [6]

    M., Carr, J

    Banzatti, A., Pontoppidan, K. M., Carr, J. S., et al. 2023, ApJL, 957, L22, doi: 10.3847/2041-8213/acf5ec

  7. [7]

    S., et al

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

  8. [8]

    M., Rowe, J

    Batalha, N. M., Rowe, J. F., Bryson, S. T., et al. 2013, ApJS, 204, 24, doi: 10.1088/0067-0049/204/2/24

Show all 102 references
  1. [9]

    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

  2. [10]

    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

  3. [11]

    2024, arXiv e-prints, arXiv:2403.03325, doi: 10.48550/arXiv.2403.03325

    Benneke, B., Roy, P.-A., Coulombe, L.-P., et al. 2024, arXiv e-prints, arXiv:2403.03325, doi: 10.48550/arXiv.2403.03325

  4. [12]

    2018, A&A, 612, A30, doi: 10.1051/0004-6361/201731931

    Bitsch, B., Morbidelli, A., Johansen, A., et al. 2018, A&A, 612, A30, doi: 10.1051/0004-6361/201731931

  5. [13]

    N., Buchhave, L

    Bitsch, B., Raymond, S. N., Buchhave, L. A., et al. 2021, A&A, 649, L5, doi: 10.1051/0004-6361/202140793

  6. [14]

    A., Clarke, C

    Booth, R. A., Clarke, C. J., Madhusudhan, N., & Ilee, J. D. 2017, MNRAS, 469, 3994, doi: 10.1093/mnras/stx1103

  7. [15]

    J., Koch, D

    Borucki, W. J., Koch, D. G., Basri, G., et al. 2011, ApJ, 736, 19, doi: 10.1088/0004-637X/736/1/19

  8. [16]

    Brande, J., Crossfield, I. J. M., Kreidberg, L., et al. 2024, ApJL, 961, L23, doi: 10.3847/2041-8213/ad1b5c

  9. [17]

    C., & Kempton, E

    Breza, B., Nixon, M. C., & Kempton, E. M.-R. 2025, ApJL, 993, L46, doi: 10.3847/2041-8213/ae0c07

  10. [18]

    2014, A&A, 564, A125, doi: 10.1051/0004-6361/201322971

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

  11. [19]

    2024, Nature Astronomy, 8, 463, doi: 10.1038/s41550-023-02183-7

    Burn, R., Mordasini, C., Mishra, L., et al. 2024, Nature Astronomy, 8, 463, doi: 10.1038/s41550-023-02183-7

  12. [20]

    2025, arXiv e-prints, arXiv:2511.01548, doi: 10.48550/arXiv.2511.01548

    Carone, L., Helling, C., Gernjak, S., Leitner, H., & Janz, T. 2025, arXiv e-prints, arXiv:2511.01548, doi: 10.48550/arXiv.2511.01548

  13. [21]

    2025, ApJ, 994, 43, doi: 10.3847/1538-4357/ae0cbf

    Murray-Clay, R. 2025, ApJ, 994, 43, doi: 10.3847/1538-4357/ae0cbf

  14. [22]

    L., McElroy, D

    Christiansen, J. L., McElroy, D. L., Harbut, M., et al. 2025, PSJ, 6, 186, doi: 10.3847/PSJ/ade3c2

  15. [23]

    2000, A&A, 363, 1081

    Claret, A. 2000, A&A, 363, 1081

  16. [24]

    A., Yurchenko, S

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

  17. [25]

    Crossfield, I. J. M., & Kreidberg, L. 2017, AJ, 154, 261, doi: 10.3847/1538-3881/aa9279

  18. [26]

    2017, AJ, 154, 39, doi: 10.3847/1538-3881/aa738b

    Tinetti, G. 2017, AJ, 154, 39, doi: 10.3847/1538-3881/aa738b

  19. [27]

    M.-R., Nixon, M

    Davenport, B., Kempton, E. M.-R., Nixon, M. C., et al. 2025, ApJL, 984, L44, doi: 10.3847/2041-8213/adcd76

  20. [28]

    2013, ApJ, 774, 95, doi: 10.1088/0004-637X/774/2/95

    Deming, D., Wilkins, A., McCullough, P., et al. 2013, ApJ, 774, 95, doi: 10.1088/0004-637X/774/2/95

  21. [29]

    P., & Bridges, M

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

  22. [31]

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

    Feroz, F., Hobson, M. P., Cameron, E., & Pettitt, A. N. 2019, The Open Journal of Astrophysics, 2, 10, doi: 10.21105/astro.1306.2144

  23. [32]

    2016, The Journal of Open Source Software, 1, 24, doi: 10.21105/joss.00024

    Foreman-Mackey, D. 2016, The Journal of Open Source Software, 1, 24, doi: 10.21105/joss.00024

  24. [33]

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

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

  25. [34]

    J., Mordasini, C., Nettelmann, N., et al

    Fortney, J. J., Mordasini, C., Nettelmann, N., et al. 2013, ApJ, 775, 80, doi: 10.1088/0004-637X/775/1/80 Fr¨ ohlich, C. 2013, SSRv, 176, 237, doi: 10.1007/s11214-011-9780-1

  26. [35]

    J., Petigura, E

    Fulton, B. J., Petigura, E. A., Howard, A. W., et al. 2017, AJ, 154, 109, doi: 10.3847/1538-3881/aa80eb Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1, doi: 10.1051/0004-6361/201833051 19

  27. [36]

    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

  28. [37]

    2005, Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica®Support (Cambridge University Press)

    Gregory, P. 2005, Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica®Support (Cambridge University Press)

  29. [38]

    J., Gordon, I

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

  30. [39]

    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

  31. [40]

    2023, A&A, 671, A122, doi: 10.1051/0004-6361/202243956

    Helling, C., Samra, D., Lewis, D., et al. 2023, A&A, 671, A122, doi: 10.1051/0004-6361/202243956

  32. [41]

    E., & Tian, M

    Heng, K., Owen, J. E., & Tian, M. 2025, ApJ, 994, 28, doi: 10.3847/1538-4357/ae0acc

  33. [42]

    W., Sinukoff, E., Blunt, S., et al

    Howard, A. W., Sinukoff, E., Blunt, S., et al. 2025, ApJS, 278, 52, doi: 10.3847/1538-4365/adc5e4

  34. [43]

    2025, arXiv e-prints, arXiv:2507.12622, doi: 10.48550/arXiv.2507.12622

    Hu, R., Bello-Arufe, A., Tokadjian, A., et al. 2025, arXiv e-prints, arXiv:2507.12622, doi: 10.48550/arXiv.2507.12622

  35. [44]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

  36. [45]

    2013, A&A, 553, A6, doi: 10.1051/0004-6361/201219058

    Husser, T.-O., Wende-von Berg, S., Dreizler, S., et al. 2013, A&A, 553, A6, doi: 10.1051/0004-6361/201219058

  37. [46]

    2025, ApJ, 987, 174, doi: 10.3847/1538-4357/add3fe

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

  38. [47]

    E., Isella, A., et al

    Izidoro, A., Schlichting, H. E., Isella, A., et al. 2022, ApJL, 939, L19, doi: 10.3847/2041-8213/ac990d

  39. [48]

    A., Blecic, J., Ashtari, R., et al

    Kahle, K. A., Blecic, J., Ashtari, R., et al. 2025, A&A, 701, A184, doi: 10.1051/0004-6361/202554916

  40. [49]

    2021, ApJ, 921, 84, doi: 10.3847/1538-4357/ac1e96

    Banzatti, A. 2021, ApJ, 921, 84, doi: 10.3847/1538-4357/ac1e96

  41. [50]

    M.-R., Zhang, M., Bean, J

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

  42. [51]

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

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

  43. [52]

    L., D´ esert, J.-M., et al

    Kreidberg, L., Bean, J. L., D´ esert, J.-M., et al. 2014, Nature, 505, 69, doi: 10.1038/nature12888

  44. [53]

    R., Parmentier, V., et al

    Kreidberg, L., Line, M. R., Parmentier, V., et al. 2018, AJ, 156, 17, doi: 10.3847/1538-3881/aac3df

  45. [54]

    F., Kubyshkina, D., Fossati, L., et al

    Krenn, A. F., Kubyshkina, D., Fossati, L., et al. 2024, A&A, 686, A301, doi: 10.1051/0004-6361/202348584

  46. [55]

    2014, A&A, 572, A35, doi: 10.1051/0004-6361/201423814

    Lambrechts, M., Johansen, A., & Morbidelli, A. 2014, A&A, 572, A35, doi: 10.1051/0004-6361/201423814

  47. [56]

    2003, ApJL, 598, L121, doi: 10.1086/380815

    Lammer, H., Selsis, F., Ribas, I., et al. 2003, ApJL, 598, L121, doi: 10.1086/380815

  48. [57]

    D., & Fortney, J

    Lopez, E. D., & Fortney, J. J. 2014, ApJ, 792, 1, doi: 10.1088/0004-637X/792/1/1

  49. [58]

    D., Rustamkulov, Z., Sing, D

    Lothringer, J. D., Rustamkulov, Z., Sing, D. K., et al. 2021, ApJ, 914, 12, doi: 10.3847/1538-4357/abf8a9

  50. [59]

    2022, AJ, 163, 101, doi: 10.3847/1538-3881/ac3d38

    Lubin, J., Van Zandt, J., Holcomb, R., et al. 2022, AJ, 163, 101, doi: 10.3847/1538-3881/ac3d38

  51. [60]

    A., Van Zandt, J., et al

    Lubin, J., Petigura, E. A., Van Zandt, J., et al. 2024, AJ, 168, 196, doi: 10.3847/1538-3881/ad79ed

  52. [61]

    2022, Science, 377, 1211, doi: 10.1126/science.abl7164

    Luque, R., & Pall´ e, E. 2022, Science, 377, 1211, doi: 10.1126/science.abl7164

  53. [62]

    2015, A&A, 573, A90, doi: 10.1051/0004-6361/201423804

    Magic, Z., Chiavassa, A., Collet, R., & Asplund, M. 2015, A&A, 573, A90, doi: 10.1051/0004-6361/201423804

  54. [63]

    2023, A&A, 677, L7, doi: 10.1051/0004-6361/202347169

    Mah, J., Bitsch, B., Pascucci, I., & Henning, T. 2023, A&A, 677, L7, doi: 10.1051/0004-6361/202347169

  55. [64]

    2024, A&A, 686, L17, doi: 10.1051/0004-6361/202450322

    Mah, J., Savvidou, S., & Bitsch, B. 2024, A&A, 686, L17, doi: 10.1051/0004-6361/202450322

  56. [65]

    2022, A&A, 664, A162, doi: 10.1051/0004-6361/202243742

    Mancini, L., Esposito, M., Covino, E., et al. 2022, A&A, 664, A162, doi: 10.1051/0004-6361/202243742

  57. [66]

    2010, Data Structures for Statistical Computing in Python, doi: 10.25080/Majora-92bf1922-00a

    McKinney, W. 2010, Data Structures for Statistical Computing in Python, doi: 10.25080/Majora-92bf1922-00a

  58. [67]

    E., & Young, E

    Misener, W., Schlichting, H. E., & Young, E. D. 2023, MNRAS, 524, 981, doi: 10.1093/mnras/stad1910 Molli` ere, P., Wardenier, J. P., van Boekel, R., et al. 2019, A&A, 627, A67, doi: 10.1051/0004-6361/201935470

  59. [68]

    V., Fortney, J

    Morley, C. V., Fortney, J. J., Marley, M. S., et al. 2015, ApJ, 815, 110, doi: 10.1088/0004-637X/815/2/110

  60. [69]

    2024, The Journal of Open Source Software, 9, 5875, doi: 10.21105/joss.05875 ¨Oberg, K

    Nasedkin, E., Molli` ere, P., & Blain, D. 2024, The Journal of Open Source Software, 9, 5875, doi: 10.21105/joss.05875 ¨Oberg, K. I., Murray-Clay, R., & Bergin, E. A. 2011, ApJL, 743, L16, doi: 10.1088/2041-8205/743/1/L16

  61. [70]

    2023, A&A, 669, A40, doi: 10.1051/0004-6361/202244120

    Orell-Miquel, J., Nowak, G., Murgas, F., et al. 2023, A&A, 669, A40, doi: 10.1051/0004-6361/202244120

  62. [71]

    E., & Wu, Y

    Owen, J. E., & Wu, Y. 2013, ApJ, 775, 105, doi: 10.1088/0004-637X/775/2/105

  63. [72]

    E., & Wu, Y

    Owen, J. E., & Wu, Y. 2017, ApJ, 847, 29, doi: 10.3847/1538-4357/aa890a

  64. [73]

    2006, A&A, 453, 1129, doi: 10.1051/0004-6361:20054449

    Paardekooper, S.-J., & Mellema, G. 2006, A&A, 453, 1129, doi: 10.1051/0004-6361:20054449

  65. [74]

    2023, Nature, 620, 516, doi: 10.1038/s41586-023-06317-9

    Perotti, G., Christiaens, V., Henning, T., et al. 2023, Nature, 620, 516, doi: 10.1038/s41586-023-06317-9

  66. [75]

    2024, ApJL, 974, L10, doi: 10.3847/2041-8213/ad6f00

    Piaulet-Ghorayeb, C., Benneke, B., Radica, M., et al. 2024, ApJL, 974, L10, doi: 10.3847/2041-8213/ad6f00

  67. [76]

    L., Kyuberis, A

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

  68. [77]

    2006, MNRAS, 373, 231, doi: 10.1111/j.1365-2966.2006.11012.x

    Pont, F., Zucker, S., & Queloz, D. 2006, MNRAS, 373, 231, doi: 10.1111/j.1365-2966.2006.11012.x

  69. [78]

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

    Rackham, B. V., Apai, D., & Giampapa, M. S. 2019, AJ, 157, 96, doi: 10.3847/1538-3881/aaf892

  70. [79]

    Redfield, S., & Linsky, J. L. 2008, ApJ, 673, 283, doi: 10.1086/524002

  71. [80]

    A., & Seager, S

    Rogers, L. A., & Seager, S. 2010, ApJ, 712, 974, doi: 10.1088/0004-637X/712/2/974 20

  72. [81]

    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

  73. [82]

    2023, ApJL, 954, L52, doi: 10.3847/2041-8213/acebf0

    Roy, P.-A., Benneke, B., Piaulet, C., et al. 2023, ApJL, 954, L52, doi: 10.3847/2041-8213/acebf0

  74. [83]

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

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

  75. [84]

    D., & Bitsch, B

    Schneider, A. D., & Bitsch, B. 2021, A&A, 654, A71, doi: 10.1051/0004-6361/202039640

  76. [85]

    Bower, D. J. 2024, ApJL, 962, L8, doi: 10.3847/2041-8213/ad206e

  77. [86]

    Lopez, E. D. 2016, ApJ, 831, 64, doi: 10.3847/0004-637X/831/1/64

  78. [87]

    P., Sing, D

    Thorngren, D. P., Sing, D. K., & Mukherjee, S. 2026, ApJS, 283, 10, doi: 10.3847/1538-4365/ae0e71

  79. [88]

    P., Rocchetto, M., et al

    Tsiaras, A., Waldmann, I. P., Rocchetto, M., et al. 2016a, ApJ, 832, 202, doi: 10.3847/0004-637X/832/2/202

  80. [89]

    P., et al

    Tsiaras, A., Rocchetto, M., Waldmann, I. P., et al. 2016b, ApJ, 820, 99, doi: 10.3847/0004-637X/820/2/99

  81. [90]

    P., Zingales, T., et al

    Tsiaras, A., Waldmann, I. P., Zingales, T., et al. 2018, AJ, 155, 156, doi: 10.3847/1538-3881/aaaf75

  82. [91]

    VanderPlas, J. T. 2018, ApJS, 236, 16, doi: 10.3847/1538-4365/aab766

  83. [92]

    T., & Ivezi´ c,ˇZ

    VanderPlas, J. T., & Ivezi´ c,ˇZ. 2015, ApJ, 812, 18, doi: 10.1088/0004-637X/812/1/18

  84. [93]

    M., Haldemann, J., Ronco, M

    Venturini, J., Guilera, O. M., Haldemann, J., Ronco, M. P., & Mordasini, C. 2020, A&A, 643, L1, doi: 10.1051/0004-6361/202039141

  85. [94]

    E., et al

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

  86. [95]

    Young, E. D. 2025, ApJL, 988, L55, doi: 10.3847/2041-8213/adf185

  87. [96]

    N., Chamberlin, P

    Woods, T. N., Chamberlin, P. C., Harder, J. W., et al. 2009, Geophys. Res. Lett., 36, L01101, doi: 10.1029/2008GL036373

  88. [97]

    Youngblood, A., & Newton, E. R. 2022, allisony/lyapy: First release created for citation purposes in the literature, v1.0.0 Zenodo, doi: 10.5281/zenodo.6949067

  89. [98]

    2025, AJ, 170, 342, doi: 10.3847/1538-3881/ae0d88

    Youngblood, A., France, K., Koskinen, T., et al. 2025, AJ, 170, 342, doi: 10.3847/1538-3881/ae0d88

  90. [99]

    L., Allen, M., & Pinto, J

    Yung, Y. L., Allen, M., & Pinto, J. P. 1984, ApJS, 55, 465, doi: 10.1086/190963

  91. [100]

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

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

  92. [101]

    S., & Fortney, J

    Zahnle, K., Marley, M. S., & Fortney, J. J. 2009, arXiv e-prints, arXiv:0911.0728, doi: 10.48550/arXiv.0911.0728

  93. [102]

    B., Sasselov, D

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

  94. [103]

    2022, The Journal of Open Source Software, 7, 4838, doi: 10.21105/joss.04838

    Zieba, S., & Kreidberg, L. 2022, The Journal of Open Source Software, 7, 4838, doi: 10.21105/joss.04838

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.