Pith. sign in

REVIEW 3 major objections 5 minor 110 references

Estimating dayside effective temperatures of hot Jupiters and associated uncertainties through Gaussian process regression

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Gaussian process regression can recover unbiased dayside effective temperatures of hot Jupiters from just three broad-band eclipse measurements, with uncertainties that behave like true 68% confidence intervals.

desk verdict A useful new application of GP regression to sparse secondary-eclipse photometry, with a nice catalogue, but the uncertainty calibration is partly circular because the signal variance was tuned on the same simulated data used for validation. read the letter →

arxiv 1908.02631 v1 pith:QLOXHJ5P submitted 2019-08-07 astro-ph.EP

Emily K. Pass , Nicolas B. Cowan , Patricio E. Cubillos , Jack G. Sklar This is my paper · ORCID
classification astro-ph.EP
keywords hotJupitersdaysideeffectivetemperatureGaussianprocessregressionsecondaryeclipsesSpitzerIRACHSTWFC3model-independentestimationradiative-equilibriummodelspectra
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper develops a model-independent way to estimate a hot Jupiter's dayside effective temperature from sparse secondary-eclipse measurements, using Gaussian process regression to interpolate brightness temperature across wavelength and to propagate both measurement noise and spectrum undersampling. Using 97,200 simulated data sets built from radiative-equilibrium hot Jupiter models, the authors show that when observations include white-light HST WFC3 plus the two warm Spitzer IRAC channels, the GP method returns unbiased effective temperatures with uncertainties that are neither systematically too large nor too small. The same simulations show that using only IRAC 3.6 and 4.5 µm data biases all estimators low by up to 20% at 1σ, because those bands probe the cooler upper atmosphere. Applied to the twelve hot Jupiters with published WFC3 and IRAC eclipse depths, the method yields dayside effective temperatures with 1σ uncertainties from ±66 K to ±136 K, and the paper asserts that the true temperature will fall inside each quoted interval 68% of the time.

What carries the argument

The carrying object is Gaussian process regression with the squared-exponential covariance kernel $k(r)=\sigma^2 \exp(-r^2/(2l^2))$, applied to brightness-temperature spectra converted from wavelength to frequency. The hyperparameters are fixed rather than fit to the sparse target data: the log length scale is set to $\ln(l^2)=-8.55$ (about $1.4\times10^{13}$ Hz, i.e., 0.19 µm at 2 µm), chosen from the low-resolution structure of water opacity, and the log signal variance is set to $\ln(\sigma^2)=-4$ (14% of the normalized brightness temperature), chosen from a 37-planet training sample. The GP uses a constant mean function equal to the inverse-error-weighted mean brightness temperature, so it behaves like the error-weighted mean method far from observed points but inflates uncertainty where the spectrum is undersampled. This uncertainty inflation is the key mechanism that the simpler estimators lack.

What would settle it

Take a planet with both sparse WFC3+IRAC eclipse measurements and a full JWST secondary-eclipse spectrum, integrate the full spectrum to obtain the true bolometric dayside temperature, and check whether the truth falls inside the quoted 1σ interval; if it does for fewer than about 68% of a sample of such planets, the coverage claim is falsified.

Watch

Extended reading notes

Core claim

The central claim is that a Gaussian process with a squared-exponential kernel, a correlation length scale fixed by water opacity, and a signal variance of 14% can recover unbiased dayside effective temperatures from as few as three broad-band measurements, with uncertainty estimates that are statistically accurate in all signal-to-noise regimes. On 97,200 simulated data sets, the GP produces z-scores—how far an estimate sits from the true temperature in units of its quoted uncertainty—centered near zero with standard deviations near one, whereas the error-weighted mean and linear-interpolation methods produce z-score spreads larger than one, meaning they underestimate the total error, especially at high signal-to-noise where undersampling dominates. The paper also establishes a limitation: with only the 3.6 and 4.5 µm IRAC bands, effective temperatures are systematically underestimated and known to no better than about 20% at 1σ, because those bands form in the cooler upper atmosphere and the model suite contains no thermal inversions.

Load-bearing premise

The method's claimed 68% coverage depends on real hot Jupiter spectra resembling the simulated suite: cloud-free, without thermal inversions, and with brightness-temperature variability around the assumed 14%.

Editorial extensions

If this is right

  • Dayside effective temperatures with reliable uncertainties can be obtained from just three broad-band eclipse measurements (WFC3 plus IRAC channels 1 and 2), so planets without full spectra no longer require a retrieval to get a trustworthy temperature.
  • IRAC-only 3.6 and 4.5 µm eclipse data should not be used to quote effective temperatures with precision better than about 20% at 1σ, regardless of estimator.
  • The error-weighted mean method, if used, should switch from inverse-variance weighting to inverse-error weighting to reduce outlier influence.
  • The twelve published temperatures, with 1σ uncertainties between ±66 K and ±136 K, constitute a testable prediction that upcoming space-based spectra will confirm or refute.

Reading between the lines

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

  • If the GP is applied to other band combinations, the fixed signal variance of 14% should be retrained: observations that resolve finer spectral structure would likely favour a shorter length scale and a smaller amplitude, otherwise the quoted uncertainties may become too conservative.
  • Injecting thermal-inversion models into the simulation suite is a direct stress test; it would likely show that the IRAC-only low-temperature bias shrinks or reverses, and it would reveal how much of the claimed 68% coverage depends on the no-inversion assumption.
  • The z-score validation used in this paper could usefully become a standard check for any future empirical temperature estimator, since it exposes underestimation of uncertainty that average accuracy alone does not.
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

3 major / 5 minor

Summary. The paper proposes a Gaussian-process regression method for estimating dayside effective temperatures of hot Jupiters from sparse secondary-eclipse photometry, specifically WFC3 white-light data plus warm Spitzer IRAC channels 1 and 2. The GP uses a squared-exponential kernel whose length scale is fixed from the HITEMP water spectrum and whose signal variance is fixed at 14%, with an inverse-error-weighted-mean prior. The authors benchmark the method against the error-weighted mean and linear interpolation on 97,200 simulated data sets generated from 324 cloud-free, radiative-equilibrium Pyrat Bay models, using Test/Teff ratios and z-scores as metrics. They find GP z-score distributions close to N(0,1) across SNR regimes while EWM and LI produce biased z-scores, and they caution that using only IRAC channels is significantly biased. The method is then applied to twelve hot Jupiters, yielding effective temperatures with uncertainties from 66 to 136 K, and the authors assert that 68% of the catalogue true temperatures will fall within the reported 1-sigma intervals.

Significance. If the calibration claims hold, this is a useful and fast model-independent estimator for sparse exoplanet eclipse data, and it provides a uniform catalogue of effective temperatures. The paper is transparent about its methods, provides public code, uses a large and reproducible simulation suite, and gives an honest treatment of the IRAC-only limitation. The main weakness is that the uncertainty calibration is not independently validated: the fixed signal variance is selected in part to make the same benchmark's z-scores look calibrated, and the simulation suite contains only non-inverted, cloud-free models, so the transfer of the 68% coverage statement to real planets is an extrapolation rather than a measured property.

major comments (3)
  1. [Section 2.1.2 and Section 3.1] The fixed log-signal variance (-4, 14%) is explicitly justified in Section 2.1.2 by the z-score behavior obtained in Section 3.1, and Section 3.1 uses the same 97,200 simulated data sets to validate the method. This makes the reported near-N(0,1) z-score distributions a consistency check rather than an independent test, and the headline comparison of GP against EWM and LI is therefore compromised as evidence for superior uncertainty estimation. I request a hold-out or cross-validated benchmark, or a sensitivity analysis showing that the conclusions are robust to signal variance over a physically plausible range, and a corresponding rephrasing of the validation claims.
  2. [Sections 2.2.1, 3.1, and 3.2] The simulation suite contains only cloud-free, non-inverted, radiative-equilibrium models, and Section 3.1 explicitly acknowledges the absence of thermal inversions and clouds. The physical argument for a 14% signal variance in Section 2.1.2 is based on a skin-temperature bound that assumes a non-inverted temperature profile. Consequently, the Section 3.2 assertion that 68% of catalogue true temperatures will fall within the reported 1-sigma intervals is not a measured coverage for real hot Jupiters, which may have thermal inversions or patchy clouds. Please either add simulations with inversions and clouds or replace that assertion with a clearly conditional statement about coverage under the simulation assumptions.
  3. [Abstract, Section 2.1, and Table 1] The temperature estimates are repeatedly described as "model-independent," but the GP result depends on the fixed kernel hyperparameters, the chosen mean function, and the normalization scheme. I recommend either defining the term carefully or describing the estimates as GP-prior-based or empirically calibrated, so that readers are not misled about the role of the adopted assumptions in the catalogue values.
minor comments (5)
  1. [Table 1] In the WASP-103 row, the IRAC channel 1 uncertainty appears as "±0.38" without a leading zero; if this is intended to be ±0.038, please correct the table.
  2. [Section 2.2.1] The text cites "Pyrat Bay (Cubillos et al., in prep.)," but the reference list contains only Cubillos (2016) and Blecic (2016); please provide the appropriate in-preparation citation or revise the text.
  3. [Equation (5)] The integral in Equation (5) is missing a closing parenthesis in the integrand; please rewrite it with unambiguous notation for the wavelength limits.
  4. [Throughout] The database is referred to variously as "exoplanets.org," "Exoplanets Data Explorer," and "exoplanet.org"; please use one consistent name.
  5. [Section 2.1.2] The sentence "This choice is consistent with theoretical expectations, as we have discussed" appears before the skin-layer discussion that follows; consider reordering or adding a pointer so the discussion is not introduced after its conclusion.

Circularity Check

1 steps flagged · score 4.0 of 10

Uncertainty calibration is partially circular: the fixed signal variance is tied to the same simulated z-score benchmark used to assert 68% coverage.

  1. fitted input called prediction [Section 2.1.2 (hyperparameter selection) and Section 3.1 (simulated benchmark); coverage claim in Section 3.2]
    "For this reason, we fix the log-signal variance hyperparameter as−4, or 14%. This choice is consistent with theoretical expectations, as we have discussed. It also becomes strongly motivated following our analysis in Section 3.1: with this hyperparameter, we retrieve statistically-appropriate distributions of effective temperature estimates."

    Signal variance controls GP output uncertainty. The paper fixes log sigma^2 = -4 partly because on the same 97,200 simulated data sets later used as the benchmark this value yields 'statistically-appropriate distributions of effective temperature estimates.' The z-score distributions in Sec 3.1 then validate GP uncertainty estimates, and Sec 3.2 asserts 68% of true temperatures will fall in the quoted 1-sigma intervals. Since the validation metric helped motivate the hyperparameter, near-N(0,1) z-scores are partly a consistency check on the tuning target, not an independent test. Partial, not total: length scale comes from HITEMP; central signal variance value comes from a 37-planet training set plus 16% skin-layer estimate; only the calibration/coverage claim is affected.

full rationale

The GP temperature estimates themselves are not derived from the benchmark: the mean function is the error-weighted mean of the observed brightness temperatures, the length scale is estimated from the HITEMP water line list, and the central signal variance is trained on 37 archival hot Jupiters and checked against the skin-layer estimate (16%). Thus the reported temperatures have independent content and are not forced by the simulation suite. However, the uncertainty-calibration claim is partially circular. In Sec 2.1.2 the paper fixes sigma^2 = 14% and states that this choice is 'strongly motivated' by the Sec 3.1 z-score distributions computed from the same 97,200 simulated observations later used as validation. The near-N(0,1) z-scores and the Sec 3.2 assertion that 68% of real temperatures will lie in the quoted 1-sigma intervals are therefore partly a restatement of the hyperparameter selection criterion rather than an independent, out-of-sample test. The circularity is partial because an external training set and a physical skin-layer argument independently point to the same hyperparameter value; had the choice been made a priori, the z-score check would have been a genuine test. A separate scope limitation (not circularity) is that the simulation suite contains only cloud-free, non-inverted, radiative-equilibrium models (Sec 2.2.1), so the 68% coverage claim for real planets is an extrapolation beyond the tested model family. Score 4 reflects one partially circular validation step with independent external grounding for the central method.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The method is not a first-principles derivation. It imports a GP kernel, a mean function, and two fixed hyperparameters from external databases (HITEMP, exoplanets.org) and from the authors' simulation benchmark. The central uncertainty claim therefore inherits all these assumptions, and the hyperparameter choice is partly tuned on the same simulated data used for validation.

free parameters (2)
  • Log length scale hyperparameter ln(l^2) = -8.55 (l = 1.4e13 Hz, equivalent to 0.19 um at 2 um)
    Chosen from HITEMP water spectrum binning analysis (Section 2.1.2, Figure 1), selecting the low-resolution smooth trend and fixing it for all planets. Not fit to target eclipse data, but it is a hand-picked prior that controls interpolation across wavelength.
  • Log signal variance hyperparameter ln(sigma^2) = -4 (14 percent brightness temperature amplitude)
    Estimated from 37 hot Jupiters in exoplanets.org and fixed partly because with this hyperparameter the authors retrieve statistically appropriate distributions of effective temperature estimates (Section 2.1.2). This is the key free parameter that sets the GP uncertainty at unobserved wavelengths.
assumptions (6)
  • domain assumption The 324 Pyrat Bay model spectra are representative of real hot Jupiter dayside emission.
    All benchmarking and z-score calibration rest on these cloud-free, radiative-equilibrium models with scaled-solar metallicity and Bond albedos of 0 or 0.3 (Section 2.2.1).
  • domain assumption The absence of thermal inversions and clouds in the simulated spectra does not change the robustness ranking of GP versus EWM and LI.
    The text explicitly notes the models lack inversions and clouds, making the IRAC-only case a worst case; the same omission affects the three-band z-scores used to support the central claim (Sections 2.2.1, 3.1).
  • ad hoc to paper The squared-exponential GP kernel with fixed frequency length scale and 14 percent signal variance is an appropriate prior for all hot Jupiter brightness-temperature spectra.
    Introduced specifically for this estimator; it controls the posterior at wavelengths away from observations and was partly tuned on the benchmark (Section 2.1.2).
  • domain assumption Reflected starlight is negligible in the infrared bands used.
    Equation 1 drops the geometric albedo term; Keating and Cowan (2017) find reflected light is non-negligible for WASP-43 b at near-IR, and WASP-43 b is in the archival sample.
  • domain assumption The photon-limited noise model and Monte Carlo propagation capture the dominant uncertainties in real eclipse measurements.
    Simulated uncertainties use photon statistics with three distance and precision scenarios (Section 2.2.3); real systematics from Spitzer and HST may differ.
  • domain assumption Published eclipse depths and system parameters are accurate and their uncertainties are Gaussian.
    Archival brightness temperatures are computed from literature eclipse depths and Exoplanet Data Explorer parameters; asymmetric errors are replaced by the larger value (Section 3.2).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Estimating dayside effective temperatures of hot Jupiters and associated uncertainties through Gaussian process regression." pith.science (2026). https://pith.science/paper/QLOXHJ5P

@misc{pith2026190802631,
  author       = {Pith},
  title        = {Pith review of: Estimating dayside effective temperatures of hot Jupiters and associated uncertainties through Gaussian process regression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QLOXHJ5P}},
  note         = {Machine review of arXiv:1908.02631}
}
abstract

In this work, we outline a new method for estimating dayside effective temperatures of exoplanets and associated uncertainties using Gaussian process (GP) regression. By applying our method to simulated observations, we show that the GP method estimates uncertainty more robustly than other model-independent approaches. We find that unbiased estimates of effective temperatures can be made using as few as three broad-band measurements (white-light HST WFC3 and the two warm Spitzer IRAC channels), although we caution that estimates made using only IRAC can be significantly biased. We then apply our GP method to the twelve hot Jupiters in the literature whose secondary eclipse depths have been measured by WFC3 and IRAC channels 1 and 2: CoRoT-2 b; HAT-P-7 b; HD 189733 b; HD 209458 b; Kepler-13A b; TrES-3 b; WASP-4 b; WASP-12 b; WASP-18 b; WASP-33 b; WASP-43 b; and WASP-103 b. We present model-independent dayside effective temperatures for these planets, with uncertainty estimates that range from $\pm$66 K to $\pm$136 K.

Figures

Figures reproduced from arXiv: 1908.02631 by the authors.

Figure 1
Figure 1. The maximum-likelihood log-length scale, ln(l 2 ), is shown as a function of the number of bins in the water spec￾trum. As resolution increases, more small-scale structure is avail￾able to the GP and shorter length scales are favoured. The differ￾ent lines represent the uncertainty assumed, with the GP able to discount small-scale structure as random fluctuations given suffi￾cient uncertainty. The lowest, mid, and h… view at source ↗
Figure 3
Figure 3. Distribution of z-scores and Test/Teff for each of the EWM, GP, and LI methods, tested on the 97,200 data sets described in Section 2.3 (with HST/WFC3/G141, Spitzer/IRAC/ch1, and Spitzer/IRAC/ch2 observations). Re￾sults are grouped by the SNR of the 4.5 µm eclipse depth. The z-score scale varies between panels to appropriately display the spread of the data. 1.0 10.0 ( m) 1250 1500 1750 2000 2250 2500 2750 3000 Brig… view at source ↗
Figure 4
Figure 4. A model spectrum, simulated observations, and Tb,p(λ) fits from the three methods. The contours show the 1σ, 2σ, and 3σ confidence intervals for the GP fit. methods. The second metric is the z-score: z = Test − Teff σ . (6) This metric evaluates the accuracy of the reported uncer￾tainty, as well as the accuracy of the effective temperature estimate. A method that accurately estimates both the ef￾fective temperature … view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Distribution of z-scores and Test/Teff for each of the EWM, GP, and LI methods, tested on the 97,200 data sets with observations only at 3.6 and 4.5 µm (that is, without simulated WFC3 data). Results are grouped by the SNR of the 4.5 µm eclipse depth and the z-score sc…
Figure 6
Figure 6. Figure 6: The irradiation temperatures and GP-estimated day￾side effective temperatures for the twelve archival planets. In each case, we find effective temperatures consistent with imperfect day￾night heat circulation. Teff, stellar log(g), and R 2 p/R 2 ∗ ) tabulated in the Ex…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

110 extracted references · 18 canonical work pages

  1. [1]

    A., Lindsay S

    Almosallam I. A., Lindsay S. N., Jarvis M. J., Roberts S. J., 2016, @doi [ ] 10.1093/mnras/stv2425 , 455, 2387

    doi:10.1093/mnras/stv2425 2016

  2. [2]

    W., O'Neil M., 2015, @doi [IEEE Transactions on Pattern Analysis and Machine Intelligence] 10.1109/TPAMI.2015.2448083 , 38

    Ambikasaran S., Foreman-Mackey D., Greengard L., Hogg D. W., O'Neil M., 2015, @doi [IEEE Transactions on Pattern Analysis and Machine Intelligence] 10.1109/TPAMI.2015.2448083 , 38

    arXiv 2015

  3. [3]

    C., Sousa S

    Ammler-von Eiff M., Santos N. C., Sousa S. G., Fernandes J., Guillot T., Israelian G., Mayor M., Melo C., 2009, @doi [ ] 10.1051/0004-6361/200912360 , 507, 523

    work page doi:10.1051/0004-6361/200912360 2009

  4. [4]

    Arcangeli J., et al., 2018, @doi [ ] 10.3847/2041-8213/aab272 , 855, L30

    doi:10.3847/2041-8213/aab272 2018

  5. [5]

    J., Scott P., 2009, @doi [ ] 10.1146/annurev.astro.46.060407.145222 , http://adsabs.harvard.edu/abs/2009ARA\

    Asplund M., Grevesse N., Sauval A. J., Scott P., 2009, @doi [ ] 10.1146/annurev.astro.46.060407.145222 , http://adsabs.harvard.edu/abs/2009ARA\

    arXiv 2009

  6. [6]

    C., Borchers B., Thurber C

    Aster R. C., Borchers B., Thurber C. H., 2013, Parameter Estimation and Inverse Problems. Elsevier

    2013

  7. [7]

    A., 1986, @doi [Annals of the New York Academy of Sciences] 10.1111/j.1749-6632.1986.tb47983.x , 470, 331

    Bahcall N. A., 1986, @doi [Annals of the New York Academy of Sciences] 10.1111/j.1749-6632.1986.tb47983.x , 470, 331

    arXiv 1986

  8. [8]

    S., Hauschildt P

    Barman T. S., Hauschildt P. H., Allard F., 2005, @doi [ ] 10.1086/444349 , 632, 1132

    doi:10.1086/444349 2005

Show all 110 references
  1. [9]

    G., Madhusudhan N., Tsiaras A., Zhao M., Gilliland R

    Beatty T. G., Madhusudhan N., Tsiaras A., Zhao M., Gilliland R. L., Knutson H. A., Shporer A., Wright J. T., 2017, @doi [ ] 10.3847/1538-3881/aa899b , 154, 158

    2017 doi

  2. [10]

    M., et al., 2011, @doi [ ] 10.1088/0004-637X/727/1/23 , 727, 23

    Beerer I. M., et al., 2011, @doi [ ] 10.1088/0004-637X/727/1/23 , 727, 23

    2011 doi

  3. [11]

    Beichman C., et al., 2014, @doi [ ] 10.1086/679566 , 126, 1134

    2014 doi

  4. [12]

    Benneke B., 2015, preprint ( @eprint arXiv 1504.07655 )

    2015 arXiv

  5. [13]

    Blecic J., 2016, preprint ( @eprint arXiv 1604.02692 )

    2016 arXiv

  6. [14]

    Blecic J., et al., 2013, @doi [ ] 10.1088/0004-637X/779/1/5 , 779, 5

    2013 doi

  7. [15]

    O., 2016, @doi [ ] 10.3847/0067-0049/225/1/4 , http://adsabs.harvard.edu/abs/2016ApJS..225....4B 225, 4

    Blecic J., Harrington J., Bowman M. O., 2016, @doi [ ] 10.3847/0067-0049/225/1/4 , http://adsabs.harvard.edu/abs/2016ApJS..225....4B 225, 4

    2016 doi

  8. [16]

    Blecic J., Dobbs-Dixon I., Greene T., 2017, @doi [ ] 10.3847/1538-4357/aa8171 , 848, 127

    2017 doi

  9. [17]

    Borysow A., 2002, @doi [ ] 10.1051/0004-6361:20020555 , http://adsabs.harvard.edu/abs/2002A\

    2002 doi

  10. [18]

    Borysow A., Frommhold L., 1989, @doi [ ] 10.1086/167515 , http://adsabs.harvard.edu/abs/1989ApJ...341..549B 341, 549

    1989 doi

  11. [19]

    Borysow J., Frommhold L., Birnbaum G., 1988, @doi [ ] 10.1086/166112 , http://adsabs.harvard.edu/abs/1988ApJ...326..509B 326, 509

    1988 doi

  12. [20]

    Borysow A., Frommhold L., Moraldi M., 1989, @doi [ ] 10.1086/167027 , http://adsabs.harvard.edu/abs/1989ApJ...336..495B 336, 495

    1989 doi

  13. [21]

    G., Fu Y., 2001, @doi [ ] 10.1016/S0022-4073(00)00023-6 , http://adsabs.harvard.edu/abs/2001JQSRT..68..235B 68, 235

    Borysow A., Jorgensen U. G., Fu Y., 2001, @doi [ ] 10.1016/S0022-4073(00)00023-6 , http://adsabs.harvard.edu/abs/2001JQSRT..68..235B 68, 235

    2001 doi

  14. [22]

    J., Stello D., 2009, @doi [ ] 10.1111/j.1365-2966.2009.14679.x , 395, 2226

    Brewer B. J., Stello D., 2009, @doi [ ] 10.1111/j.1365-2966.2009.14679.x , 395, 2226

    2009

  15. [23]

    S., Sharp C

    Burrows A., Marley M. S., Sharp C. M., 2000, @doi [ ] 10.1086/308462 , http://adsabs.harvard.edu/abs/2000ApJ...531..438B 531, 438

    2000 doi

  16. [24]

    Cartier K. M. S., et al., 2017, @doi [ ] 10.3847/1538-3881/153/1/34 , http://adsabs.harvard.edu/abs/2017AJ....153...34C 153, 34

    2017 doi

  17. [25]

    L., 2004, preprint ( @eprint arXiv astro-ph/0405087 )

    Castelli F., Kurucz R. L., 2004, preprint ( @eprint arXiv astro-ph/0405087 )

    2004 arXiv

  18. [26]

    A., Barman T., Allen L

    Charbonneau D., Knutson H. A., Barman T., Allen L. E., Mayor M., Megeath S. T., Queloz D., Udry S., 2008, @doi [ ] 10.1086/591635 , 686, 1341

    2008 doi

  19. [27]

    S., et al., 2000, in Breckinridge J

    Cheng E. S., et al., 2000, in Breckinridge J. B., Jakobsen P., eds, Vol. 4013, UV, Optical, and IR Space Telescopes and Instruments. pp 367--373, @doi 10.1117/12.394020

    2000 doi

  20. [28]

    B., Agol E., 2011, @doi [ ] 10.1088/0004-637X/729/1/54 , 729, 54

    Cowan N. B., Agol E., 2011, @doi [ ] 10.1088/0004-637X/729/1/54 , 729, 54

    2011 doi

  21. [29]

    B., Agol E., Charbonneau D., 2007, @doi [ ] 10.1111/j.1365-2966.2007.11897.x , 379, 641

    Cowan N. B., Agol E., Charbonneau D., 2007, @doi [ ] 10.1111/j.1365-2966.2007.11897.x , 379, 641

    2007

  22. [30]

    B., et al., 2015, @doi [ ] 10.1086/680855 , 127, 311

    Cowan N. B., et al., 2015, @doi [ ] 10.1086/680855 , 127, 311

    2015 doi

  23. [31]

    Croll B., et al., 2015, @doi [ ] 10.1088/0004-637X/802/1/28 , 802, 28

    2015 doi

  24. [32]

    Crossfield I. J. M., Hansen B. M. S., Barman T., 2012, @doi [ ] 10.1088/0004-637X/746/1/46 , 746, 46

    2012 doi

  25. [33]

    R., Deming D., Madhusudhan N., 2014, @doi [ ] 10.1088/0004-637X/795/2/166 , 795, 166

    Crouzet N., McCullough P. R., Deming D., Madhusudhan N., 2014, @doi [ ] 10.1088/0004-637X/795/2/166 , 795, 166

    2014 doi

  26. [34]

    E., 2016, preprint ( @eprint arXiv 1604.01320 )

    Cubillos P. E., 2016, preprint ( @eprint arXiv 1604.01320 )

    2016 arXiv

  27. [35]

    E., 2017, @doi [ ] 10.3847/1538-4357/aa9228 , http://adsabs.harvard.edu/abs/2017ApJ...850...32C 850, 32

    Cubillos P. E., 2017, @doi [ ] 10.3847/1538-4357/aa9228 , http://adsabs.harvard.edu/abs/2017ApJ...850...32C 850, 32

    2017 doi

  28. [36]

    B., Bowman W

    Deming D., Harrington J., Laughlin G., Seager S., Navarro S. B., Bowman W. C., Horning K., 2007, @doi [ ] 10.1086/522496 , 667, L199

    2007 doi

  29. [37]

    Deming D., et al., 2011, @doi [ ] 10.1088/0004-637X/726/2/95 , 726, 95

    2011 doi

  30. [38]

    Deming D., et al., 2012, @doi [ ] 10.1088/0004-637X/754/2/106 , 754, 106

    2012 doi

  31. [39]

    B., Bean J

    Diamond-Lowe H., Stevenson K. B., Bean J. L., Line M. R., Fortney J. J., 2014, @doi [ ] 10.1088/0004-637X/796/1/66 , 796, 66

    2014 doi

  32. [40]

    M., Aigrain S., Gibson N., Barstow J

    Evans T. M., Aigrain S., Gibson N., Barstow J. K., Amundsen D. S., Tremblin P., Mourier P., 2015, @doi [ ] 10.1093/mnras/stv910 , 451, 680

    2015 doi

  33. [41]

    M., et al., 2017, @doi [ ] 10.1038/nature23266 , 548, 58

    Evans T. M., et al., 2017, @doi [ ] 10.1038/nature23266 , 548, 58

    2017 doi

  34. [42]

    G., et al., 2004, @doi [ ] 10.1086/422843 , 154, 10

    Fazio G. G., et al., 2004, @doi [ ] 10.1086/422843 , 154, 10

    2004 doi

  35. [43]

    K., Line M

    Feng Y. K., Line M. R., Fortney J. J., Stevenson K. B., Bean J., Kreidberg L., Parmentier V., 2016, @doi [ ] 10.3847/0004-637X/829/1/52 , 829, 52

    2016 doi

  36. [44]

    W., Morton T

    Foreman-Mackey D., Hogg D. W., Morton T. D., 2014, @doi [ ] 10.1088/0004-637X/795/1/64 , 795, 64

    2014 doi

  37. [45]

    Foreman-Mackey D., Agol E., Ambikasaran S., Angus R., 2017, @doi [ ] 10.3847/1538-3881/aa9332 , 154, 220

    2017 doi

  38. [46]

    J., 2018, preprint ( @eprint 1804.08149 )

    Fortney J. J., 2018, preprint ( @eprint 1804.08149 )

    2018 arXiv

  39. [47]

    J., Marley M

    Fortney J. J., Marley M. S., Lodders K., Saumon D., Freedman R., 2005, @doi [ ] 10.1086/431952 , 627, L69

    2005 doi

  40. [48]

    J., Cooper C

    Fortney J. J., Cooper C. S., Showman A. P., Marley M. S., Freedman R. S., 2006, @doi [ ] 10.1086/508442 , 652, 746

    2006 doi

  41. [49]

    J., Marley M

    Fortney J. J., Marley M. S., Barnes J. W., 2007, @doi [ ] 10.1086/512120 , https://ui.adsabs.harvard.edu/abs/2007ApJ...659.1661F 659, 1661

    2007 doi

  42. [50]

    A., Charbonneau D., O'Donovan F

    Fressin F., Knutson H. A., Charbonneau D., O'Donovan F. T., Burrows A., Deming D., Mandushev G., Spiegel D., 2010, @doi [ ] 10.1088/0004-637X/711/1/374 , 711, 374

    2010 doi

  43. [51]

    Gandhi S., Madhusudhan N., 2018, @doi [ ] 10.1093/mnras/stx2748 , 474, 271

    2018 doi

  44. [52]

    Garhart E., et al., 2019, preprint ( @eprint arXiv 1901.07040 )

    2019 arXiv

  45. [53]

    P., Aigrain S., Roberts S., Evans T

    Gibson N. P., Aigrain S., Roberts S., Evans T. M., Osborne M., Pont F., 2012, @doi [ ] 10.1111/j.1365-2966.2011.19915.x , 419, 2683

    2012

  46. [54]

    X., Wright J

    Han E., Wang S. X., Wright J. T., Feng Y. K., Zhao M., Fakhouri O., Brown J. I., Hancock C., 2014, @doi [ ] 10.1086/678447 , 126, 827

    2014 doi

  47. [55]

    J., Schwartz J

    Hansen C. J., Schwartz J. C., Cowan N. B., 2014, @doi [ ] 10.1093/mnras/stu1699 , 444, 3632

    2014 doi

  48. [56]

    M., Madhusudhan N., Deming D., Knutson H., 2015, @doi [ ] 10.1088/0004-637X/806/2/146 , 806, 146

    Haynes K., Mandell A. M., Madhusudhan N., Deming D., Knutson H., 2015, @doi [ ] 10.1088/0004-637X/806/2/146 , 806, 146

    2015 doi

  49. [57]

    S., Skemer A

    Henderson C. S., Skemer A. J., Morley C. V., Fortney J. J., 2017, @doi [ ] 10.1093/mnras/stx1495 , 470, 4557

    2017 doi

  50. [58]

    G., et al., 2016, @doi [ ] 10.3847/0004-6256/152/2/44 , 152, 44

    Ingalls J. G., et al., 2016, @doi [ ] 10.3847/0004-6256/152/2/44 , 152, 44

    2016 doi

  51. [59]

    Irwin P. G. J., et al., 2008, @doi [ ] 10.1016/j.jqsrt.2007.11.006 , 109, 1136

    2008 doi

  52. [60]

    Jones E., Oliphant T., Peterson P., et al., 2001, SciPy : Open source scientific tools for Python , http://www.scipy.org/

    2001

  53. [61]

    A., et al., 2015, @doi [ ] 10.1088/0004-637X/810/2/118 , 810, 118

    Kammer J. A., et al., 2015, @doi [ ] 10.1088/0004-637X/810/2/118 , 810, 118

    2015 doi

  54. [62]

    B., 2017, @doi [ ] 10.3847/2041-8213/aa8b6b , 849, L5

    Keating D., Cowan N. B., 2017, @doi [ ] 10.3847/2041-8213/aa8b6b , 849, L5

    2017 doi

  55. [63]

    Kreidberg L., et al., 2018, @doi [ ] 10.3847/1538-3881/aac3df , 156, 17

    2018 doi

  56. [64]

    L., 1970, SAO Special Report, http://adsabs.harvard.edu/abs/1970SAOSR.309.....K 309

    Kurucz R. L., 1970, SAO Special Report, http://adsabs.harvard.edu/abs/1970SAOSR.309.....K 309

    1970

  57. [65]

    Lavie B., et al., 2017, @doi [ ] 10.3847/1538-3881/aa7ed8 , 154, 91

    2017 doi

  58. [66]

    Lecavelier Des Etangs A., Pont F., Vidal-Madjar A., Sing D., 2008, @doi [ ] 10.1051/0004-6361:200809388 , http://adsabs.harvard.edu/abs/2008A\

    2008 doi

  59. [67]

    Lee G., Dobbs-Dixon I., Helling C., Bognar K., Woitke P., 2016, @doi [ ] 10.1051/0004-6361/201628606 , http://adsabs.harvard.edu/abs/2016A

    2016 doi

  60. [68]

    Lee G. K. H., Wood K., Dobbs-Dixon I., Rice A., Helling C., 2017, @doi [ ] 10.1051/0004-6361/201629804 , http://adsabs.harvard.edu/abs/2017A

    2017 doi

  61. [69]

    E., Rothman L

    Li G., Gordon I. E., Rothman L. S., Tan Y., Hu S.-M., Kassi S., Campargue A., Medvedev E. S., 2015, @doi [ ] 10.1088/0067-0049/216/1/15 , http://adsabs.harvard.edu/abs/2015ApJS..216...15L 216, 15

    2015 doi

  62. [70]

    R., et al., 2013, @doi [ ] 10.1088/0004-637X/775/2/137 , 775, 137

    Line M. R., et al., 2013, @doi [ ] 10.1088/0004-637X/775/2/137 , 775, 137

    2013 doi

  63. [71]

    R., et al., 2016, @doi [ ] 10.3847/0004-6256/152/6/203 , 152, 203

    Line M. R., et al., 2016, @doi [ ] 10.3847/0004-6256/152/6/203 , 152, 203

    2016 doi

  64. [72]

    Springer International Publishing, Cham, pp 1--30

    Madhusudhan N., 2018, Atmospheric Retrieval of Exoplanets. Springer International Publishing, Cham, pp 1--30

    2018

  65. [73]

    Malik M., et al., 2017, @doi [ ] 10.3847/1538-3881/153/2/56 , http://adsabs.harvard.edu/abs/2017AJ....153...56M 153, 56

    2017 doi

  66. [74]

    Mancini L., et al., 2013, @doi [ ] 10.1093/mnras/stt1394 , 436, 2

    2013 doi

  67. [75]

    Mansfield M., et al., 2018, @doi [ ] 10.3847/1538-3881/aac497 , 156, 10

    2018 doi

  68. [76]

    S., Gelino C., Stephens D., Lunine J

    Marley M. S., Gelino C., Stephens D., Lunine J. I., Freedman R., 1999, @doi [ ] 10.1086/306881 , 513, 879

    1999 doi

  69. [77]

    M \'a rquez-Neila P., Fisher C., Sznitman R., Heng K., 2018, @doi [Nature Astronomy] 10.1038/s41550-018-0504-2

    2018 doi

  70. [78]

    Martioli E., et al., 2018, @doi [ ] 10.1093/mnras/stx3009 , 474, 4264

    2018 doi

  71. [79]

    V., Knutson H., Line M., Fortney J

    Morley C. V., Knutson H., Line M., Fortney J. J., Thorngren D., Marley M. S., Teal D., Lupu R., 2017, @doi [ ] 10.3847/1538-3881/153/2/86 , 153, 86

    2017 doi

  72. [80]

    Nymeyer S., et al., 2011, @doi [ ] 10.1088/0004-637X/742/1/35 , 742, 35

    2011 doi

  73. [81]

    T., 2010, Principles of Planetary Climate

    Pierrehumbert R. T., 2010, Principles of Planetary Climate

    2010

  74. [82]

    L., Heske A., Escudero Sanz I., Crouzet P.-E., 2016, in Space Telescopes and Instrumentation 2016: Optical, Infrared, and Millimeter Wave

    Puig L., Pilbratt G. L., Heske A., Escudero Sanz I., Crouzet P.-E., 2016, in Space Telescopes and Instrumentation 2016: Optical, Infrared, and Millimeter Wave. p. 99041W, @doi 10.1117/12.2230964

    2016 doi

  75. [83]

    M., 2014, @doi [ ] 10.1088/0004-637X/785/2/148 , 785, 148

    Ranjan S., Charbonneau D., D \'e sert J.-M., Madhusudhan N., Deming D., Wilkins A., Mandell A. M., 2014, @doi [ ] 10.1088/0004-637X/785/2/148 , 785, 148

    2014 doi

  76. [84]

    Adaptative computation and machine learning series, University Press Group Limited

    Rasmussen C., Williams C., 2006, Gaussian Processes for Machine Learning. Adaptative computation and machine learning series, University Press Group Limited

    2006

  77. [85]

    Roman M., Rauscher E., 2019, @doi [The Astrophysical Journal] 10.3847/1538-4357/aafdb5 , 872, 1

    2019 doi

  78. [86]

    S., et al., 2010, @doi [ ] 10.1016/j.jqsrt.2010.05.001 , 111, 2139

    Rothman L. S., et al., 2010, @doi [ ] 10.1016/j.jqsrt.2010.05.001 , 111, 2139

    2010 doi

  79. [87]

    B., Lightman A

    Rybicki G. B., Lightman A. P., 2004, Radiative Processes in Astrophysics. John Wiley & Sons

    2004

  80. [88]

    C., Cowan N

    Schwartz J. C., Cowan N. B., 2015, @doi [ ] 10.1093/mnras/stv470 , 449, 4192

    2015 doi

  81. [89]

    Statist.] 10.1214/aos/1176344136 , 6, 461

    Schwarz G., 1978, @doi [Ann. Statist.] 10.1214/aos/1176344136 , 6, 461

    1978

  82. [90]

    J., Hansen B

    Seager S., Richardson L. J., Hansen B. M. S., Menou K., Cho J. Y.-K., Deming D., 2005, @doi [ ] 10.1086/444411 , 632, 1122

    2005 doi

  83. [91]

    Shporer A., et al., 2014, @doi [ ] 10.1088/0004-637X/788/1/92 , 788, 92

    2014 doi

  84. [92]

    B., et al., 2010, @doi [ ] 10.1038/nature09013 , 464, 1161

    Stevenson K. B., et al., 2010, @doi [ ] 10.1038/nature09013 , 464, 1161

    2010 doi

  85. [93]

    B., et al., 2014a, @doi [Science] 10.1126/science.1256758 , 346, 838

    Stevenson K. B., et al., 2014a, @doi [Science] 10.1126/science.1256758 , 346, 838

    doi

  86. [94]

    B., Bean J

    Stevenson K. B., Bean J. L., Madhusudhan N., Harrington J., 2014b, @doi [ ] 10.1088/0004-637X/791/1/36 , 791, 36

    doi

  87. [95]

    B., et al., 2017, @doi [ ] 10.3847/1538-3881/153/2/68 , 153, 68

    Stevenson K. B., et al., 2017, @doi [ ] 10.3847/1538-3881/153/2/68 , 153, 68

    2017 doi

  88. [96]

    Swain M., et al., 2013, @doi [ ] 10.1016/j.icarus.2013.04.003 , 225, 432

    2013 doi

  89. [97]

    O., Deming D., Burrows A., Grillmair C

    Todorov K. O., Deming D., Burrows A., Grillmair C. J., 2014, @doi [ ] 10.1088/0004-637X/796/2/100 , 796, 100

    2014 doi

  90. [98]

    P., Tinetti G., Rocchetto M., Barton E

    Waldmann I. P., Tinetti G., Rocchetto M., Barton E. J., Yurchenko S. N., Tennyson J., 2015, @doi [ ] 10.1088/0004-637X/802/2/107 , 802, 107

    2015 doi

  91. [99]

    W., et al., 2004, @doi [ ] 10.1086/422992 , 154, 1

    Werner M. W., et al., 2004, @doi [ ] 10.1086/422992 , 154, 1

    2004 doi

  92. [100]

    N., Deming D., Madhusudhan N., Burrows A., Knutson H., McCullough P., Ranjan S., 2014, @doi [ ] 10.1088/0004-637X/783/2/113 , 783, 113

    Wilkins A. N., Deming D., Madhusudhan N., Burrows A., Knutson H., McCullough P., Ranjan S., 2014, @doi [ ] 10.1088/0004-637X/783/2/113 , 783, 113

    2014 doi

  93. [101]

    Wong I., et al., 2015, @doi [ ] 10.1088/0004-637X/811/2/122 , 811, 122

    2015 doi

  94. [102]

    Wong I., et al., 2016, @doi [ ] 10.3847/0004-637X/823/2/122 , 823, 122

    2016 doi

  95. [103]

    N., Tennyson J., 2014, @doi [ ] 10.1093/mnras/stu326 , http://adsabs.harvard.edu/abs/2014MNRAS.440.1649Y 440, 1649

    Yurchenko S. N., Tennyson J., 2014, @doi [ ] 10.1093/mnras/stu326 , http://adsabs.harvard.edu/abs/2014MNRAS.440.1649Y 440, 1649

    2014 doi

  96. [104]

    R., Wright J., Monnier J

    Zhao M., Milburn J., Barman T., Hinkley S., Swain M. R., Wright J., Monnier J. D., 2012, @doi [ ] 10.1088/2041-8205/748/1/L8 , 748, L8

    2012 doi

  97. [105]

    Zhou G., Bayliss D. D. R., Kedziora-Chudczer L., Tinney C. G., Bailey J., Salter G., Rodriguez J., 2015, @doi [ ] 10.1093/mnras/stv2138 , 454, 3002

    2015 doi

  98. [106]

    P., 2018, @doi [The Astronomical Journal] 10.3847/1538-3881/aae77c , 156, 268

    Zingales T., Waldmann I. P., 2018, @doi [The Astronomical Journal] 10.3847/1538-3881/aae77c , 156, 268

    2018 doi

  99. [107]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

  100. [108]

    N., 2013, Journal of Improbable Astronomy, 1, 1

    Author A. N., 2013, Journal of Improbable Astronomy, 1, 1

    2013

  101. [109]

    D., 2015, Journal of Interesting Stuff, 17, 198

    Jones C. D., 2015, Journal of Interesting Stuff, 17, 198

    2015

  102. [110]

    B., 2014, The Example Journal, 12, 345 (Paper I)

    Smith A. B., 2014, The Example Journal, 12, 345 (Paper I)

    2014

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.