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Stellar Contamination Correction Using Back-to-Back Transits of TRAPPIST-1 b and c

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Back-to-back transits strip stellar contamination from TRAPPIST-1 c, cutting the structured noise by a factor of 2.5 below 2 µm.

desk verdict First real test of the back-to-back transit correction idea; the short-wavelength proof works, but the long-wavelength and stacking claims rest on an extrapolation that a single epoch cannot support. read the letter →

arxiv 2412.16541 v1 pith:SHDFH7JK submitted 2024-12-21 astro-ph.EP astro-ph.IMastro-ph.SR

Alexander D. Rathcke , Lars A. Buchhave , Julien De Wit , Benjamin V. Rackham , Prune C. August , Hannah Diamond-Lowe , João M. Mendonça , Aaron Bello-Arufe
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Mercedes López-Morales Daniel Kitzmann Kevin Heng
This is my paper · ORCID
classification astro-ph.EPastro-ph.IMastro-ph.SR
keywords stellarcontaminationtransitspectroscopyTRAPPIST-1JWSTNIRSpecPRISMmulti-planetsystemsstarspotssecondaryatmospherestransmissionspectra
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 claims a model-independent way to remove stellar contamination from exoplanet transit spectra: use the quasi-simultaneous transit of an airless companion planet as an epoch-specific reference. Applying the idea to JWST NIRSpec PRISM observations of TRAPPIST-1 b and c taken on July 9, 2024, the authors divide c's transit spectrum by b's, erasing the water-absorption features at 1.4 and 1.8 µm and flattening most of the blue slope while reducing the noise floor from about 200 ppm to about 80 ppm below 2 µm, a factor of 2.5. If correct, this shifts the obstacle to atmospheric characterization from stellar-model-limited red noise to white noise that can be beaten down by stacking transits, making secondary-atmosphere signals of 60–250 ppm accessible. The same data also constrain the star's surface, pointing to cold (about 2000 K) and warm (about 2600 K) regions that are well mixed across the disk and change their covering fractions by about 0.1% per hour.

What carries the argument

The load-bearing object is the ratio of the two quasi-simultaneous transit spectra, $R(\lambda)=T_c(\lambda)/T_b(\lambda)$, rescaled by the weighted mean transit depth of TRAPPIST-1 b over 2–5 µm. Because the two planets have nearly equal radii and impact parameters and transit within hours of each other, the stellar contamination $\epsilon(\lambda)$ is assumed to be common to both spectra; forming the ratio cancels it, leaving the planet-to-planet spectral ratio, and multiplying by b's mean depth restores the corrected transit depth of c. Supporting this are a joint white-light-curve fit with shared quadratic limb-darkening coefficients, a linear trend, and a Gaussian Process for correlated noise; wavelength-bin fits that fix the common parameters; and an out-of-transit modeling pipeline that reconstructs the stellar photosphere from one to four spectral components using SPHINX model spectra with 2MASS photometry as a constraint. Allan variance plots, which track residual scatter as a function of bin size, are then used to quantify whether the structured red noise has been converted into white noise.

What would settle it

Take a longer-duration JWST observation of a back-to-back TRAPPIST-1 b/c pair with enough signal-to-noise to see contamination at 4–5 µm: if the corrected c spectrum still contains structured noise above about 80 ppm there, or if the same correction applied to b with c as the reference produces spectral features, then the identical-contamination and airless-anchor assumptions fail.

Watch

Extended reading notes

Core claim

The paper's central discovery is that back-to-back transits of TRAPPIST-1 b and c carry essentially the same stellar contamination, so b's spectrum can be used as a template to correct c's without any stellar model. After the ratio correction, the 1.4 and 1.8 µm water features disappear and the blue-end slope is roughly halved; the residual-scatter analysis shows the structured red noise in the 0.8–2.0 µm range drops by a factor of 2.5, from roughly 200 ppm to 80 ppm at a bin size of ten. At 2–5 µm the single-transit signal-to-noise is too low to measure contamination directly, but the out-of-transit spectral variability across the whole wavelength range and the success of the blue-end correction lead the authors to argue that redder contamination is mitigated to a similar degree, down to about 80 ppm. They also use the shape of the contamination to infer that TRAPPIST-1's photosphere is well mixed between about 2000 K and about 2600 K components, with the cooler component slightly underrepresented in the transit chord, so the long-wavelength contamination amplitude is roughly 280 ppm rather than the 1740 ppm expected if all inhomogeneity lay outside the chord.

Load-bearing premise

The correction holds only if TRAPPIST-1 b is truly airless and both planets occult the same distribution of stellar surface patches, so that any difference between the two transit spectra is stellar rather than planetary.

Editorial extensions

If this is right

  • Stellar contamination no longer needs a definitive model of the host star: any multi-planet system with an airless reference planet observed in back-to-back transits can get an epoch-specific correction from the data itself.
  • Stacking corrected transit spectra across epochs becomes statistically meaningful because the residual noise is approximately white, which the paper says opens the way to searching for 60–250 ppm secondary-atmosphere features such as CO2 at 4.3 µm.
  • TRAPPIST-1 b and c become interchangeable reference planets, roughly doubling the scheduling windows for multi-transit observations of the system.
  • Long-wavelength contamination is expected to be broad and smooth, about 80 ppm after correction, so it should not mask sharp planetary spectral features like CO2.
  • The observed roughly 0.1%-per-hour evolution of the cold and warm surface coverage means a correction taken from a different epoch would be inaccurate, reinforcing the need for quasi-simultaneous reference transits.

Reading between the lines

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

  • Inference: the same ratio technique could be transplanted to other compact multi-planet systems around active M dwarfs, provided at least one planet is confidently airless and the two transit chords are close; systems with larger impact-parameter differences would need a chord-aware correction factor.
  • Inference: the airless-anchor assumption is testable with this very dataset in reverse, because using c as the reference to correct b should produce a flat spectrum; if it instead shows spectral features, those would betray a thin atmosphere on c rather than stellar contamination.
  • Inference: the out-of-transit coverage evolution implies that contamination within a single transit may not be strictly constant, so modeling a time-dependent contamination term in the ratio could account for the residual blue slope that the authors attribute to stellar surface changes.
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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

3 major / 6 minor

Summary. The paper presents JWST/NIRSpec PRISM observations of quasi-simultaneous transits of TRAPPIST-1 b and c on July 9, 2024, and proposes using the transit spectrum of TRAPPIST-1 b as an epoch-specific, empirical stellar-contamination reference to correct the transit spectrum of TRAPPIST-1 c. The authors report that the ratio correction removes the 1.4 and 1.8 µm water features, reduces the blue slope, and lowers the Allan-variance noise floor in the 0.8–2.0 µm range from about 200 ppm to about 80 ppm, a factor of 2.5. They also analyze out-of-transit spectra in five time segments and infer a two-component stellar photosphere with cool (~2000 K) and warm (~2600 K) regions whose covering fractions vary by about 0.1% per hour. Based on the short-wavelength success and the out-of-transit variability, the paper argues that contamination at 2–5 µm is likewise reduced, enabling co-addition of multiple epochs to search for secondary atmospheres at 60–250 ppm. The paper is framed as a proof-of-concept and repeatedly acknowledges the low SNR at longer wavelengths, but the abstract and conclusions go beyond the directly measured result.

Significance. If the method is validated, it would provide a valuable empirical route to stellar-contamination correction in multi-planet systems with an airless reference planet, directly addressing a known limitation of model-based corrections for late M dwarfs. The observation was strategically scheduled to test a specific prediction of the TRAPPIST-1 JWST Community Initiative, and the paper includes a custom independent reduction pipeline, time-resolved out-of-transit stellar analysis, and an honest discussion of residual slopes and model uncertainties. The short-wavelength demonstration—removal of the 1.4/1.8 µm features and a factor-of-2.5 reduction in the 0.8–2.0 µm Allan-variance floor—is a concrete, quantitative result. However, the broader claims about 2–5 µm correction and the ability to stack spectra to detect 60–250 ppm signals are not established by the single epoch analyzed here; they are extrapolations that need to be reframed or supported by additional propagation of systematics. The paper is a promising proof-of-concept, but the strength of the conclusions currently exceeds the strength of the evidence.

major comments (3)
  1. [§3.2, Fig. 3 (bottom right); Abstract; §5] The long-wavelength benefit is asserted rather than measured. The paper states that in the 2–5 µm range the variance of the unbinned residuals is higher post-correction due to added photon noise from planet b, and that the lower SNR prevents clear detection of contamination or full assessment of mitigation. Despite this, the Abstract claims that contamination is reduced at longer wavelengths 'to a similar extent' and the Conclusions state that the correction 'reduces the challenge to primarily white noise' for atmospheric searches. The 2.5× reduction is demonstrated only in the 0.8–2.0 µm range; the reddest portion of the spectrum is consistent with white noise before correction, so no red contamination reduction is actually measured. The paper should present the 2–5 µm correction as an expectation supported by the short-wavelength analogy and the out-of-transit variability, not as a demonstrated result, and should quantify the sensitivity that can be claimed from a stacked epoch series given a redward systematic floor of order 110 ppm (1.2σ) rather than assuming it averages down.
  2. [§3.2, §1, §4.1, Fig. 4] The central correction assumes that b and c see identical stellar contamination and that b is featureless. These assumptions are only approximately met: the paper reports a residual blue-slope difference between the two planets, attributes it to evolving surface coverage or slightly different transit chords, and acknowledges in §4.1 that some atmospheric scenarios for b and c remain viable. Because the corrected c spectrum is formed by dividing the c spectrum by the b spectrum, any wavelength-dependent feature intrinsic to b—whether atmospheric or a chord-dependent stellar signal—enters the corrected c spectrum with sign reversed and is not removable by stacking. The 110 ppm (1.2σ) redward consistency is not an upper limit on such a systematic; at 1.2σ the true difference could be comparable to the 60–250 ppm target signals. The paper should propagate the uncertainty in the airless and identical-chord assumptions into the corrected spectrum, for example by injecting plausible b atmospheric models and spot configurations consistent with the Figure 4 constraint f1,chord = 0.495, and should explicitly state the residual systematic floor this places on the method.
  3. [§3.1.2, Fig. 2, §4.2] The out-of-transit inference that TRAPPIST-1 has a two-component photosphere with covering fractions varying by ~0.1% per hour is used as part of the argument that contamination extends to red wavelengths. This inference comes from fitting SPHINX models to flux-calibrated spectra with no uncertainty inflation, and the best-fit cool component is pinned at the lower grid boundary of 2000 K while the model effective temperature (2324 K) is below the literature value (2566±26 K). The paper is appropriately cautious in §4.2 that these are not definitive, but the abstract presents the 0.1% per hour variability as a finding. Because the red-contamination argument depends on the time variability of the stellar-surface model, the model dependence and boundary effects should be quantified or the claim should be moved into the interpretation section rather than the abstract.
minor comments (6)
  1. [§1] 'Same transit cord' should read 'same transit chord'.
  2. [Table 1] The inclination posterior for planet c is reported as '89.83761834+0.09' with far more significant digits than the uncertainty; round to a consistent precision.
  3. [Fig. 3 caption] The label 'Width of 4.3 µm CO2 feature' in the bottom-right panel is unclear; the caption should state the bin size or spectral width used for the Allan-variance comparison.
  4. [§3.2] The statement that the two transit spectra are consistent 'to within 110 ppm (1.2σ)' should clarify whether 110 ppm is the 1σ uncertainty or the maximum difference, and should note that a 1.2σ consistency does not set a tight upper limit on a possible systematic.
  5. [§4.2, Eq. (1)] The symbols f1,disk, f2,disk, f1,chord, and f2,chord are defined in the text but not in the equation; adding an explicit definition after Eq. (1) would improve readability.
  6. [Abstract and §5] The method is described as 'model-independent' in the abstract, but the long-wavelength discussion in §4 uses a best-fit stellar contamination model to estimate contamination amplitude. I suggest using 'empirical' or 'model-light' to describe the ratio step and reserving 'model-independent' for the short-wavelength correction only.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the back-to-back ratio correction is an empirical division of two observed transit spectra; the 2.5x reduction is measured, not fitted.

full rationale

The paper's central derivation is empirical. The corrected TRAPPIST-1 c spectrum is computed as the ratio of the observed c and b transit spectra, multiplied by a constant (the weighted mean b depth over 2–5 um); no free parameter is fit to make the correction. The claimed 2.5x reduction is an Allan-variance comparison of pre- and post-correction residuals, so it is a measurement, not a construction. The airless-planet premise for b enters from external results (Greene et al. 2023; Zieba et al. 2023), not from this paper's fit, and the paper explicitly acknowledges residual blue-slope and 110 ppm redward differences that limit the assumption of identical chords. The method's origin is attributed to TJCI2024, a paper with overlapping authors (de Wit, Rackham), but this paper supplies a new, independent JWST observation and empirical demonstration, so the self-citation is attributional rather than load-bearing. The only mild self-referentiality is the red-wavelength extrapolation: a stellar contamination model with f1,chord fit to the uncorrected c spectrum is used to estimate approximately 200 ppm red contamination and an expected approximately 80 ppm post-correction floor. The paper labels these as 'suggests' and 'we expect' and does not use them for the central blue-band claim; they are model-dependent extrapolations, not circular derivations. Overall the derivation chain is self-contained and empirically falsifiable.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claims rest on the airless-reference-planet assumption, the identical-contamination assumption, and a set of fitted stellar component parameters. The ratio correction itself is empirical and requires no fitted contamination model, but the long-wavelength benefit and the photospheric heterogeneity picture depend on fitted parameters and model assumptions. Standard transit geometry and limb darkening parameters are fitted but are not the source of the central claim.

free parameters (7)
  • Cool component temperature T1 = ~2000 K (grid lower boundary)
    Fitted temperature in the two-component stellar model; sits at the SPHINX grid edge, so it is an upper limit rather than a robust measurement.
  • Warm component temperature T2 = ~2600 K
    Fitted temperature of the secondary stellar component in the out-of-transit spectral decomposition.
  • Cool component full-disk covering fraction f1 = 0.556 for Sall, 0.555 to 0.557 across S1 to S5
    Fitted covering fraction; the small time variation supports the 0.1% per hour claim, but uncertainties are not model-inflated.
  • Transit-chord cool covering fraction f1,chord = 0.495
    Fitted to the uncorrected planet c transit spectrum; used to estimate that red contamination is about 200 ppm at 4 to 5 um and to argue for a 2.5x reduction there.
  • JWST absolute flux scale factor = not stated
    Multiplicative scale factor fit to align JWST spectra with 2MASS photometry; affects the effective temperature estimate.
  • Gaussian process hyperparameters (amplitude, length scale) = ln amplitude -15.65, ln length scale -5.67
    Fitted to the white light curve and fixed for wavelength bins; the GP choice can absorb correlated noise and shape the extracted transit spectra.
  • White noise parameter = ln white noise -19.10
    Fitted white noise term in the light curve model.
assumptions (5)
  • domain assumption TRAPPIST-1 b and c are effectively airless or have no spectral features at JWST precision.
    Invoked in Section 1 and assumed in the ratio correction; if b has an atmospheric feature, the correction would remove real signal from c. Prior limits are cited but the paper acknowledges viable atmospheric states remain.
  • domain assumption The stellar contamination signal is identical for the two quasi-simultaneous transits.
    Core premise of the ratio method; the paper notes residual slope differences possibly from evolving surface coverage, indicating this is only approximately true.
  • domain assumption SPHINX model spectra are adequate for TRAPPIST-1's photosphere.
    Used for all out-of-transit heterogeneity inferences in Section 3.1.1; the authors acknowledge limitations of ultracool dwarf models and the 2000 K grid boundary.
  • domain assumption The photosphere can be represented by discrete uniform spectral components.
    Model choice following Rackham and de Wit (2024); a continuous temperature distribution would change inferred covering fractions.
  • domain assumption A squared-exponential Gaussian process captures the instrumental systematics.
    Used in Section 3.2; if the correlated noise has a different structure, transit depths and the correction factor could be biased.

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Cite this review

Pith. "Pith review of Stellar Contamination Correction Using Back-to-Back Transits of TRAPPIST-1 b and c." pith.science (2026). https://pith.science/paper/SHDFH7JK

@misc{pith2026241216541,
  author       = {Pith},
  title        = {Pith review of: Stellar Contamination Correction Using Back-to-Back Transits of TRAPPIST-1 b and c},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SHDFH7JK}},
  note         = {Machine review of arXiv:2412.16541}
}
read the original abstract

Stellar surface heterogeneities, such as spots and faculae, often contaminate exoplanet transit spectra, hindering precise atmospheric characterization. We demonstrate a novel, epoch-based, model-independent method to mitigate stellar contamination, applicable to multi-planet systems with at least one airless planet. We apply this method using quasi-simultaneous transits of TRAPPIST-1 b and TRAPPIST-1 c observed on July 9, 2024, with JWST NIRSpec PRISM. These two planets, with nearly identical radii and impact parameters, are likely either bare rocks or possess thin, low-pressure atmospheres, making them ideal candidates for this technique, as variations in their transit spectra would be primarily attributed to stellar activity. Our observations reveal their transit spectra exhibit consistent features, indicating similar levels of stellar contamination. We use TRAPPIST-1 b to correct the transit spectrum of TRAPPIST-1 c, achieving a 2.5x reduction in stellar contamination at shorter wavelengths. At longer wavelengths, lower SNR prevents clear detection of contamination or full assessment of mitigation. Still, out-of-transit analysis reveals variations across the spectrum, suggesting contamination extends into the longer wavelengths. Based on the success of the correction at shorter wavelengths, we argue that contamination is also reduced at longer wavelengths to a similar extent. This shifts the challenge of detecting atmospheric features to a predominantly white noise issue, which can be addressed by stacking observations. This method enables epoch-specific stellar contamination corrections, allowing co-addition of planetary spectra for reliable searches of secondary atmospheres with signals of 60-250 ppm. Additionally, we identify small-scale cold (2000 K) and warm (2600 K) regions almost uniformly distributed on TRAPPIST-1, with overall covering fractions varying by 0.1% per hour.

Figures

Figures reproduced from arXiv: 2412.16541 by the authors.

Figure 1
Figure 1. Normalized white light curves of TRAPPIST-1 c and TRAPPIST-1 b observed with JWST/NIRSpec PRISM during their quasi-simultaneous transits on July 9, 2024. Top panel: A schematic of the geometry of the quasi-simultaneous transits is displayed at the top. The blue circles represent the data points after removing the systematics model (a combination of a linear slope and a Gaussian Process, see Section 3.2). The solid b… view at source ↗
Figure 2
Figure 2. Out-of-transit stellar spectra and model fits. Top row: Mean stellar spectrum for all out-of-transit data (Sall, which combines the S1 through S5 stellar spectra, as defined by the out-of-transit segments shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Transit spectra of TRAPPIST-1 b and TRAPPIST-1 c and the impact of the correction process. Top left panel: Transit spectra for TRAPPIST-1 b (blue) and TRAPPIST-1 c (orange), shown both at native pixel resolution (faded points) and binned into 0.1 µm intervals (solid points). Bottom left panel: Corrected transit spectrum of TRAPPIST-1 c (purple). The corrected spectrum was computed as the ratio of the TRAPPIST-1 c tr… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison of uncorrected transit spectra and stellar contamination factors derived from out-of-transit stellar spectra. The transit spectrum of planet c is shown along with stellar contamination models calculated from the mean stellar spectrum just before the transit …

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

  1. First JWST thermal phase curves of temperate terrestrial exoplanets reveal no thick atmosphere around TRAPPIST-1 b and c

    astro-ph.EP 2025-09 accept novelty 8.0 of 10

    First JWST phase curves of TRAPPIST-1 b and c rule out thick, heat-redistributing atmospheres; b is likely airless, c may retain a tenuous O2 atmosphere.

Reference graph

Works this paper leans on

43 extracted references · 2 canonical work pages · cited by 1 Pith paper

  1. [1]

    L., et al

    Agol, E., Dorn, C., Grimm, S. L., et al. 2021, PSJ, 2, 1, doi: 10.3847/PSJ/abd022

    doi:10.3847/psj/abd022 2021

  2. [2]

    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

  3. [3]

    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

  4. [4]

    Berardo, D., de Wit, J., & Rackham, B. V. 2024, ApJL, 961, L18, doi: 10.3847/2041-8213/ad1b5b

    doi:10.3847/2041-8213/ad1b5b 2024

  5. [5]

    Y., Lederer, S

    Burdanov, A. Y., Lederer, S. M., Gillon, M., et al. 2019, MNRAS, 487, 1634, doi: 10.1093/mnras/stz1375

    work page doi:10.1093/mnras/stz1375 2019

  6. [6]

    2024, JWST Calibration Pipeline, 1.15.1, Zenodo, doi: 10.5281/zenodo.12692459

    Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2024, JWST Calibration Pipeline, 1.15.1, Zenodo, doi: 10.5281/zenodo.12692459

    doi:10.5281/zenodo.12692459 2024

  7. [7]

    V., Gillon, M., et al

    Davoudi, F., Rackham, B. V., Gillon, M., et al. 2024, ApJL, 970, L4, doi: 10.3847/2041-8213/ad5c6c

    doi:10.3847/2041-8213/ad5c6c 2024

  8. [8]

    M., et al

    Ducrot, E., Sestovic, M., Morris, B. M., et al. 2018, AJ, 156, 218, doi: 10.3847/1538-3881/aade94

    doi:10.3847/1538-3881/aade94 2018

Show all 43 references
  1. [9]

    2024, arXiv e-prints, arXiv:2412.11627, doi: 10.48550/arXiv.2412.11627

    Ducrot, E., Lagage, P.-O., Min, M., et al. 2024, arXiv e-prints, arXiv:2412.11627, doi: 10.48550/arXiv.2412.11627

    2024 doi arXiv

  2. [10]

    J., Turbet, M., Villanueva, G

    Fauchez, T. J., Turbet, M., Villanueva, G. L., et al. 2019, ApJ, 887, 194, doi: 10.3847/1538-4357/ab5862

    2019 doi

  3. [11]

    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

  4. [12]

    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

  5. [13]

    J., Moran, S

    Garcia, L. J., Moran, S. E., Rackham, B. V., et al. 2022, A&A, 665, A19, doi: 10.1051/0004-6361/202142603 Back-to-back transits to correct for TLS 13

    2022 doi

  6. [14]

    J., & Rackham, B

    Garcia, L. J., & Rackham, B. V. 2023, lgrcia/spotter: 0.0.4-beta, 0.0.4-beta, Zenodo, doi: 10.5281/zenodo.10408696

    2023 doi

  7. [15]

    M., et al

    Gillon, M., Jehin, E., Lederer, S. M., et al. 2016, Nature, 533, 221, doi: 10.1038/nature17448

    2016 doi

  8. [16]

    Gillon, M., Triaud, A. H. M. J., Demory, B.-O., et al. 2017, Nature, 542, 456, doi: 10.1038/nature21360

    2017 doi

  9. [17]

    P., Bell, T

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

    2023 doi

  10. [18]

    R., & Line, M

    Iyer, A. R., & Line, M. R. 2020, ApJ, 889, 78, doi: 10.3847/1538-4357/ab612e

    2020 doi

  11. [19]

    2023, ApJ, 944, 41, doi: 10.3847/1538-4357/acabc2 JWST Transiting Exoplanet Community Early Release Science Team, Ahrer, E.-M., Alderson, L., et al

    Gharib-Nezhad, E. 2023, ApJ, 944, 41, doi: 10.3847/1538-4357/acabc2 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

    2023 doi

  12. [20]

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

    2013 doi

  13. [21]

    2023, arXiv e-prints, arXiv:2309.07047, doi: 10.48550/arXiv.2309.07047

    Lim, O., Benneke, B., Doyon, R., et al. 2023, arXiv e-prints, arXiv:2309.07047, doi: 10.48550/arXiv.2309.07047

    2023 doi arXiv

  14. [22]

    P., Meadows, V

    Lincowski, A. P., Meadows, V. S., Crisp, D., et al. 2018, ApJ, 867, 76, doi: 10.3847/1538-4357/aae36a

    2018 doi

  15. [23]

    P., Meadows, V

    Lincowski, A. P., Meadows, V. S., Zieba, S., et al. 2023, ApJL, 955, L7, doi: 10.3847/2041-8213/acee02

    2023 doi

  16. [24]

    E., Stevenson, K

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

    2023 doi

  17. [25]

    M., Agol, E., Davenport, J

    Morris, B. M., Agol, E., Davenport, J. R. A., & Hawley, S. L. 2018, ApJ, 857, 39, doi: 10.3847/1538-4357/aab6a5

    2018 doi

  18. [26]

    S., Rackham, B

    Narrett, I. S., Rackham, B. V., & de Wit, J. 2024, AJ, 167, 107, doi: 10.3847/1538-3881/ad1f6c

    2024 doi

  19. [27]

    2017, ApJ, 834, 151, doi: 10.3847/1538-4357/aa4f6c

    Rackham, B., Espinoza, N., Apai, D., et al. 2017, ApJ, 834, 151, doi: 10.3847/1538-4357/aa4f6c

    2017 doi

  20. [28]

    Rackham, B. V. 2023, speclib, 0.0-beta.0, Zenodo, Zenodo, doi: 10.5281/zenodo.7868050

    2023 doi

  21. [29]

    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

  22. [30]

    V., & de Wit, J

    Rackham, B. V., & de Wit, J. 2024, AJ, 168, 82, doi: 10.3847/1538-3881/ad5833

    2024 doi

  23. [31]

    V., Espinoza, N., Berdyugina, S

    Rackham, B. V., Espinoza, N., Berdyugina, S. V., et al. 2023, RAS Techniques and Instruments, 2, 148, doi: 10.1093/rasti/rzad009

    2023 doi

  24. [32]

    2024, arXiv e-prints, arXiv:2409.19333, doi: 10.48550/arXiv.2409.19333

    Radica, M., Piaulet-Ghorayeb, C., Taylor, J., et al. 2024, arXiv e-prints, arXiv:2409.19333, doi: 10.48550/arXiv.2409.19333

    2024 doi arXiv

  25. [33]

    M., & Kane, S

    Roettenbacher, R. M., & Kane, S. R. 2017, ApJ, 851, 77, doi: 10.3847/1538-4357/aa991e

    2017 doi

  26. [34]

    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

  27. [35]

    F., Cutri, R

    Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163, doi: 10.1086/498708 TRAPPIST-1 JWST Community Initiative, de Wit, J.,

    2006 doi

  28. [36]

    V., et al

    Doyon, R., Rackham, B. V., et al. 2024, Nature Astronomy, 8, 810, doi: 10.1038/s41550-024-02298-5

    2024 doi

  29. [37]

    Triaud, A. H. M. J., de Wit, J., Klein, F., et al. 2023, Nature Astronomy, doi: 10.1038/s41550-023-02157-9

    2023 doi

  30. [38]

    J., Leconte, J., et al

    Turbet, M., Fauchez, T. J., Leconte, J., et al. 2023, A&A, 679, A126, doi: 10.1051/0004-6361/202347539

    2023 doi

  31. [39]

    R., Lewis, N

    Wakeford, H. R., Lewis, N. K., Fowler, J., et al. 2019, AJ, 157, 11, doi: 10.3847/1538-3881/aaf04d

    2019 doi

  32. [40]

    I., Cernetic, M., et al

    Witzke, V., Shapiro, A. I., Cernetic, M., et al. 2021, A&A, 653, A65, doi: 10.1051/0004-6361/202140275

    2021 doi

  33. [41]

    J., & Catling, D

    Zahnle, K. J., & Catling, D. C. 2017, ApJ, 843, 122, doi: 10.3847/1538-4357/aa7846

    2017 doi

  34. [42]

    V., & Apai, D

    Zhang, Z., Zhou, Y., Rackham, B. V., & Apai, D. 2018, AJ, 156, 178, doi: 10.3847/1538-3881/aade4f

    2018 doi

  35. [43]

    2023, Nature, 620, 746, doi: 10.1038/s41586-023-06232-z

    Zieba, S., Kreidberg, L., Ducrot, E., et al. 2023, Nature, 620, 746, doi: 10.1038/s41586-023-06232-z

    2023 doi

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Reviewed August 11, 2026 · model on record in the stance chip above.