Impact of planetary mass uncertainties on exoplanet atmospheric retrievals

Giovanna Tinetti; Ingo P Waldmann; Luke Keyte; Quentin Changeat

REVIEW 1 major objections 5 minor 1 cited by

Impact of planetary mass uncertainties on exoplanet atmospheric retrievals

T0 review · 1 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 The paper argues that planetary mass can be retrieved directly from transit spectra for clear, gaseous atmospheres, with better-than-10 percent precision, and that mass uncertainties leave retrieved temperatures and trace-gas abundances…

desk verdict A systematic, honestly-scoped retrieval simulation mapping when planetary mass uncertainties matter for transit spectra; the isothermal caveat is real but acknowledged, and the paper deserves a serious referee. read the letter →

arxiv 1908.06305 v3 pith:KSJKUYAQ submitted 2019-08-17 astro-ph.EP

Quentin Changeat , Luke Keyte , Ingo P Waldmann , Giovanna Tinetti This is my paper · ORCID

classification astro-ph.EP

keywords exoplanetatmospherestransitspectroscopyatmosphericretrievalplanetarymassBayesianinferencemeanmolecularweightclouddecksscaleheightdegeneracy

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 asks whether exoplanet mass must be measured by radial velocity or transit timing before atmospheric retrieval, or whether the transit spectrum itself carries enough mass information. Using analytic scaling of the transit depth and Bayesian retrievals on simulated and real spectra, it argues that for clear-sky H/He atmospheres the mass can be retrieved directly with better than 10 percent precision, and that uncertainty in the mass does not corrupt retrieved temperature or trace-gas abundances. The difficulties arise for high-altitude opaque clouds, where mass and radius trade off, and for secondary atmospheres, where mass is degenerate with the mean molecular weight. The practical conclusion is that future surveys can rely on direct mass retrieval for many gaseous planets, but should prioritize independent mass measurements for cloudy or heavy-atmosphere planets.

What carries the argument

The load-bearing object is the analytic transit-depth expression: the wavelength-dependent transit contribution is an integral over altitude of $1 - \exp[-\tau(z,\lambda)]$, with the optical depth $\tau$ built from number densities, molecular cross sections, and the scale height $H = k_B T (R_0+z)^2 / (\mu M_p G)$. Because the planetary mass appears only inside this scale height, every parameter that shares $H$ — temperature, mean molecular weight, radius — is a potential partner in degeneracy, and the paper exploits the wavelength dependence of molecular cross sections to separate them. The numerical companion is a fully Bayesian retrieval model run in paired mode, mass fixed versus mass free, on synthetic spectra at future-space-observatory quality and on real short-wavelength transit spectra. Clouds are modeled as completely opaque grey decks, chosen as the worst-case scenario for degeneracy with the radius.

What would settle it

Generate a high-signal-to-noise transit spectrum from a realistic non-isothermal temperature–pressure profile with a known mass and run the same retrieval with mass free: if the retrieved mass is biased by more than the claimed roughly 10 percent despite adequate wavelength coverage, the central claim would fail. A shorter test is to compare masses retrieved from transit spectra against independent radial-velocity masses for a sample of clear-sky gaseous exoplanets at future-observatory quality; systematic offsets would falsify the paper's conclusion.

Watch

Extended reading notes

Core claim

On the paper's own terms, the planetary mass is not an obstacle to transit-spectrum retrievals in the regimes where it has traditionally been assumed necessary to fix it externally. For clear-sky gaseous atmospheres, treating mass as a free parameter yields essentially the same posterior distributions as fixing it, with retrieved mass accurate to better than 10 percent given adequate wavelength coverage and signal-to-noise. For high-altitude opaque clouds, mass accuracy degrades, up to roughly 60 percent offset in the worst simulated case, and the bias correlates with a biased retrieved radius; yet temperature and trace-gas abundances remain unaffected by whether the mass is known. For secondary atmospheres with heavy main constituents, the mass is degenerate with the mean molecular weight, and adding clouds makes the mean molecular weight poorly constrained, so independent mass knowledge becomes important for identifying the main atmospheric constituent.

Load-bearing premise

The simulations assume a single isothermal, hydrostatic atmosphere, so the scale height is one global number; in a real atmosphere with strong vertical temperature gradients, the trade-off between mass and temperature could behave differently than shown.

Editorial extensions

If this is right

For clear-sky gaseous planets observed with broad wavelength coverage and adequate signal-to-noise, transit spectra alone can deliver the planetary mass to better than 10 percent precision, removing the need for an external mass prior in those retrievals.
Atmospheric composition and temperature retrievals are robust to mass ignorance across most tested scenarios, including cloudy hot Jupiters, so missions focused on chemistry need not wait for refined mass measurements.
For planets with heavy secondary atmospheres, an independent mass measurement breaks the mass–mean-molecular-weight degeneracy and is needed to identify the main atmospheric constituent.
High-altitude opaque clouds can bias the retrieved mass by up to roughly 60 percent even though the spectral changes correspond to less than 3 percent in radius; longer observations or higher signal-to-noise mitigate the degeneracy.
In survey planning, radial-velocity follow-up should prioritize low-gravity and super-Earth targets, where current mass errors often exceed 50 percent, over hot Jupiters where the mass can be retrieved from the spectrum itself.

Reading between the lines

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

Editorial inference: the isothermal assumption is the main boundary of the result; if real atmospheres have strong vertical temperature gradients, the mass–temperature degeneracy could either shrink or widen depending on how cross-section temperature dependence varies with altitude.
Editorial inference: the fully opaque grey-cloud model is the pessimistic end of the cloud spectrum; realistic clouds with spectral windows would recover some deep-atmosphere information, so mass retrieval in cloudy planets is likely to perform better than the worst cases shown.
Editorial inference: the same scale-height argument implies that combining transit spectra with independent radius or surface-gravity constraints, for example from asteroseismology or direct imaging, can substitute for mass priors and should be tested as a cheap way to break the mass–mean-molecular-weight degeneracy.
Editorial inference: since temperature and mass enter the scale height symmetrically, joint retrievals of mass and temperature from emission or phase-curve spectra, where the degeneracy structure differs, are a natural testable extension that the paper does not cover.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague — This is a systematic, honestly-scoped simulation study of how well planetary mass can be retrieved from transit spectra, and when ignoring mass uncertainties biases other atmospheric parameters. The main new output is a map of mass-precision thresholds across clear/cloudy primary and secondary atmospheres, using an ARIEL-like instrument model and fully Bayesian retrievals. The analytic derivation is standard but clearly presented, and the retrieval results reproduce the analytic expectations, which gives the internal logic a solid feel. The conclusion that for clear-sky, H/He-dominated planets mass can be retrieved to better than 10% (given adequate wavelength coverage and S/N) is well supported by their figures, as is the finding that temperature and trace-gas abundances are usually robust to mass ignorance. They also confirm and extend Batalha et al.'s degeneracy between mass and mean molecular weight for heavy secondary atmospheres, and show that clouds worsen things. These are useful, actionable results for mission planning and RV follow-up prioritization.

The soft spots are in proportion. The biggest is that everything is isothermal, both in the forward model and in the retrieval. The authors acknowledge this and defer non-isothermal profiles to future work, but the abstract and the headline <10% mass precision claim do not carry that caveat. Since temperature and mass enter the scale height symmetrically, a non-isothermal real atmosphere could break the clean separation they demonstrate. That is a legitimate external-validity concern, though not a flaw in what they actually simulated. The grey cloud deck is acknowledged as a worst-case, and more realistic clouds would likely reduce the degeneracies.

Minor: the phrase 'precision of more than 10%' is ambiguous — they mean uncertainty below 10%, not above. And the paper does not ship the exact retrieval outputs or a reproducibility package, though TauREx is open source, so a motivated reader could reconstruct the experiments.

Who is this for: anyone planning atmospheric characterization of low-gravity planets with JWST or ARIEL, and RV follow-up targeting teams who want to know which mass measurements matter. It is a solid, useful paper that deserves serious peer review, with the isothermal caveat stated clearly in the revision. My recommendation: send it to review, ask for an explicit caveat in the abstract.

Referee Report

1 major / 5 minor

Summary. This paper investigates whether exoplanet transit spectra can directly constrain the planetary mass and how mass uncertainties propagate to other retrieved atmospheric parameters. The authors first present an analytic expansion of the wavelength-dependent transit depth under standard assumptions (isothermal, hydrostatic, clear-sky or grey cloudy atmospheres), showing that the mass enters only through the scale height. They then use the TauREx Bayesian retrieval framework on simulated ARIEL-class observations for two classes of planets: hot Jupiters with H2/He-dominated atmospheres and super-Earths with N2-rich secondary atmospheres. For clear-sky gaseous atmospheres, they report that retrieving the mass as a free parameter yields the same posterior distributions for other parameters as fixing it, with a mass precision of about 7% and a relative accuracy better than 10%. For cloudy hot Jupiters, the mass and radius become degenerate for high-altitude opaque clouds, with mass errors up to 60%. For secondary atmospheres, the mass is degenerate with the mean molecular weight; an independent mass prior helps break this degeneracy, especially when clouds are present. The analysis is supported by an appendix with a step-by-step derivation of the optical-depth expression.

Significance. If the results hold, this is a useful and timely parameter study for JWST and ARIEL planning: it provides a systematic map of when the planetary mass can be fitted from transit spectra alone and, more importantly, identifies which retrieved parameters are robust to mass ignorance. The analytical derivation in Section 2 and the Appendix is self-contained and follows the standard transit formalism, and the retrieval experiments are internally consistent and use an open-source, widely used code. The strongest practical conclusion—that independent mass characterization is most valuable for cloudy secondary atmospheres because of the mass–mean-molecular-weight degeneracy—is physically well motivated and supported by the simulations. The paper is honest about the main modelling limitation (isothermal atmospheres) and explicitly defers non-isothermal profiles and eclipse spectra to future work.

major comments (1)

[Abstract and Section 3.1] The clear-sky mass-precision result (<10%) is obtained from simulations in which both the forward model and the retrieval assume an isothermal, hydrostatic atmosphere. As the authors note in Section 2.2, temperature and mass play symmetric roles in the scale height (Eq. 6), so the only handle separating them is the temperature dependence of molecular cross sections. In a real atmosphere with a vertical temperature gradient, an isothermal retrieval will fit some effective temperature, and the effective-temperature/mass correlation may differ from the simulated one, potentially biasing the retrieved mass and undermining the claim that temperature and trace-gas posteriors are unaffected by mass ignorance. The paper acknowledges this limitation in the methodology and appendix, but the abstract and the concluding 'clear-sky, gaseous atmospheres' statement do not carry the isothermal caveat. I recommend either adding an explicit qualification to the abstract and Section 4, or including a non-isothermal forward-model test (even a simple two-temperature profile) to show that the degeneracy behaves as simulated.

minor comments (5)

[Abstract and Section 4] The phrase 'precision of more than 10%' is ambiguous: the text later reports a 7% uncertainty, which is a precision better than 10%. Please rephrase to 'better than 10%' or 'about 7%' for clarity.
[Section 2.1, Eq. (5)] The coefficients K^T_ij, K^p_ij, and K^X_ij are used in Eq. (5) before they are defined in the Appendix (Eqs. 23–25). Consider defining them briefly in Section 2.1 or adding a forward reference to avoid forcing the reader to jump to the appendix.
[Figure 3] In the version provided, the vertical-axis label of Figure 3 appears garbled ('1.8 1 1.2 1.4 1.6 1.8'). Please check that the axis is properly labeled as 'normalised M_retrieved' with legible tick labels.
[Table 1] The row labeled 'HJ HST' should specify that the 170% mass error corresponds to the HST WFC3 retrieval with limited wavelength coverage and S/N, so that readers do not interpret it as a general statement about HST data.
[Section 2.2] The bullet point beginning 'The temperature has a similar role...' uses 'e.g:' without a space; this is a minor typographical issue but should be corrected in the final version.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: analytic and retrieval analyses are internally consistent under explicitly stated isothermal assumptions.

full rationale

The paper's derivation chain is self-contained and does not reduce to its inputs. Section 2 derives an analytic expression for the transit depth starting from the standard equation Catm(lambda) = 2*pi*integral(...) (Eq. 1) and computes the optical depth with an explicit isothermal, hydrostatic scale height H = kb*T*(R0+z)^2/(mu*Mp*G) (Eq. 6). The mass enters only through H, and the analytic discussion in Section 2.2 identifies the expected degeneracies (mass with temperature via scale height, mass with mean molecular weight in secondary atmospheres, mass with radius/clouds for opaque clouds). Section 3 then uses TauREx to generate synthetic spectra with known input parameters and performs retrievals with the mass free or fixed. The agreement between the analytic identifiability argument and the retrieval posteriors is a consistency check, not circularity: the retrieval code solves the same physical forward problem, but the analytic derivation does not assume the retrieval outcome, and the retrieval results are not forced to match the analytic predictions by construction. The central claim of <10% mass precision for clear-sky primary atmospheres is a simulation result under the paper's explicitly stated assumptions, and the paper acknowledges the isothermal limitation, deferring T-P gradient cases to future work. Self-citations of TauREx are appropriate because the code is used as a tool, and the paper also compares against HST data and external results (de Wit & Seager 2013, Batalha et al. 2017). No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors, and no ansatz is smuggled in via citation. The only mild caveat is that the analytic and numerical components share the same isothermal forward model, so their agreement is internal rather than an independent validation of the model against real atmospheres; this is a limitation on external validity, not circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper's central claims depend on scenario parameters (cloud pressure, mu, S/N) chosen by hand, and on physical assumptions (isothermal, hydrostatic, grey clouds, linear cross-section interpolation) standard in the retrieval literature. No new physical entities are introduced.

free parameters (3)

Cloud top pressure (Pclouds) = 10^-1, 10^-2, 5x10^-2, 10^-3 bar (case study)
Chosen by hand to probe the impact of cloud altitude on mass retrievability; the central conclusion about mass accuracy depends on this range.
Mean molecular weight (mu) = 2.3, 5.2, 7.6, 11.1, 27.8
Selected values span H/He to N2-rich secondary atmospheres; the mass-mu degeneracy strengthens with mu and is a key driver of the conclusions.
Signal-to-noise ratio = Single transit ARIEL noise model; S/N scan 3 to 10 in Fig. 9
Whether the mass is retrievable depends critically on the assumed S/N and wavelength coverage. These are observational choices, not fitted to data.

assumptions (4)

domain assumption Atmosphere is isothermal and in hydrostatic equilibrium
Used throughout the analytical derivation and TauREx retrievals (Section 3.1, Appendix). Limits generalization to real atmospheres with T-P gradients.
domain assumption Gray, completely opaque cloud deck parameterized by cloud-top pressure
Section 3.2, Eq. 28. Represented as a step in optical depth; the authors note this is a worst-case scenario.
domain assumption Cross sections are interpolated linearly in temperature and pressure
Section 2.1, Eqs. 3-4. Standard in retrieval codes, but can introduce errors for strong line variations.
domain assumption ARIEL instrument noise model from Mugnai et al. (2020) approximates future observations
Section 3.1. All quantitative precision claims depend on this noise model.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Impact of planetary mass uncertainties on exoplanet atmospheric retrievals." pith.science (2026). https://pith.science/paper/KSJKUYAQ

@misc{pith2026190806305,
  author       = {Pith},
  title        = {Pith review of: Impact of planetary mass uncertainties on exoplanet atmospheric retrievals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KSJKUYAQ}},
  note         = {Machine review of arXiv:1908.06305}
}

read the original abstract

In current models used to interpret exoplanet atmospheric observations, the planet mass is treated as a prior and is estimated independently with external methods, such as RV or TTV techniques. This approach is necessary as available spectroscopic data do not have sufficient wavelength coverage and/or SNR to infer the planetary mass. We examine here the impact of mass uncertainties on spectral retrieval analyses for a host of atmospheric scenarios. Our approach is both analytical and numerical: we first use simple approximations to extract analytically the influence of each parameter to the wavelength-dependent transit depth. We then adopt a fully Bayesian retrieval model to quantify the propagation of the mass uncertainty onto other atmospheric parameters. We found that for clear-sky, gaseous atmospheres the posterior distributions are the same when the mass is known or retrieved. The retrieved mass is very accurate, with a precision of more than 10%, provided the wavelength coverage and S/N are adequate. When opaque clouds are included in the simulations, the uncertainties in the retrieved mass increase, especially for high altitude clouds. However atmospheric parameters such as the temperature and trace-gas abundances are unaffected by the knowledge of the mass. Secondary atmospheres are more challenging due to the higher degree of freedom for the atmospheric main component, which is unknown. For broad wavelength range and adequate SNR, the mass can still be retrieved accurately and precisely if clouds are not present, and so are all the other atmospheric/planetary parameters. When clouds are added, we find that the mass uncertainties may impact substantially the retrieval of the mean molecular weight: an independent characterisation of the mass would therefore be helpful to capture/confirm the main atmospheric constituent.

Figures

Figures reproduced from arXiv: 1908.06305 by the authors.

**Figure 1.** Spectra (left) and posteriors distribution (right) for a hot-Jupiter with a clear-sky atmosphere. Orange plots: the mass is known. Green plots: the mass is retrieved. The blue crosses indicate either the simulated ARIEL observations (left plot) or the ground truth values (right plot). retrieved mass becomes less accurate when the cloud pressure is lower than 10−2 bar. At the same time, the 1-sigma spread around the … view at source ↗

**Figure 2.** Comparison between the known/retrieved mass cases as a function of cloud pressure. The clear-sky case is rendered by placing the cloud deck at 10 bar [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Normalised retrieved mass in the case of a gaseous planet as a function of cloud pressure. The green curve is the retrieved mass with its 1-sigma uncertainty. The blue line is the real value. The clear case is represented by a cloud deck at 10 bar. The retrieved mass is not affected by low altitude clouds (Pclouds ≈ 0.1 bar), while for high altitude completely opaque clouds, the retrieved mass starts to diverge from… view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: Spectra (left) and posteriors distribution (right) for a hot-Jupiter with a cloudy atmosphere (opaque cloud deck at 10−3 bar). Orange plots: the mass is known. Green plots: the mass is retrieved. The blue crosses indicate either the simulated ARIEL observations (left p…

**Figure 5.** Figure 5: Comparison of different forward models based on the cloudy case with cloud top pressure at 10−3 bar. Black: True model. Purple: True model where only the mass is changed to Mp = 0.9MJ . Green: True model where the mass is changed to the retrieved mean value. Orange: Tr…

**Figure 6.** Figure 6: Normalised retrieved mass (Mretrieved in green) for planets with a secondary atmosphere as a function of the mean molecular weight. The blue line represents the real value. At small µ, the atmosphere is dominated by a single gas species: H2. This case has already been …

**Figure 7.** Figure 7: ARIEL simulated spectra (left) and posteriors distribution (right) for a cloud-free atmosphere with µ = 11.1 (i.e. N2/He = 4). Orange plots: the mass is known. Green plots: the mass is retrieved. Blue crosses: simulated ARIEL observations obtained in one transit (left …

**Figure 8.** Figure 8: Impact of the mass on the retrieval of the radius, temperature, mean molecular weight and trace-gas abundances for different scenarios of heavy atmospheres represented by increasing values of µ. The simulated ARIEL observations are obtained in one transit significant (…

**Figure 9.** Figure 9: Normalised retrieved mass (Mretrieved in Green) for a N2-rich heavy atmosphere case (µ = 27.8) as a function of S/N. Blue line: real value. 1 1.2 1.4 1.6 1.8 Wavelength ( m) 0.01450 0.01455 0.01460 0.01465 0.01470 0.01475 (Rp/Rs) 2 Mass known Mass retrieved WFC3 Observ…

**Figure 10.** Figure 10: Hubble transit spectra (left) and posteriors distribution (right) for HD 209458 b (Tsiaras et al. (2018)). Orange plots: the mass is known. Green plots: the mass is retrieved. Blue crosses: Hubble observations. 11, we show the simulated spectra and posteriors for two …

**Figure 11.** Figure 11: ARIEL simulated spectra (left) and posteriors distribution (right) for a planet with a cloudy secondary atmosphere. The top cloud pressure is 10−2 bar. Top: µ = 11.1, bottom: µ = 7.6. Orange plots: the mass is known. Green plots: the mass is retrieved. Blue crosses: s…

**Figure 12.** Figure 12: Illustration of the transmission of the stellar radiation through an exoplanet atmosphere during a transit event. R0 is the radius at which when the planet becomes fully opaque in absence of clouds. For a given point in the atmosphere, z is the altitude normal to the …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

Revisiting TOI-4438 and TOI-442 planetary systems with new observations from SPIRou and TESS
astro-ph.EP 2026-07 accept novelty 6.0 of 10

Refined masses and radii for TOI-4438 b and TOI-442 b, stellar rotation periods from magnetic variability, and a single-transit planet candidate around TOI-4438.

Reference graph

Works this paper leans on

51 extracted references · 23 canonical work pages · cited by 1 Pith paper

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month note number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.co...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION format.doi doi empty "" "doi:" doi * if FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix ":" * if eprint field.or.null * if FUNCTION format.pid eprint empty format.doi format.eprint if FUNCTION n.dashify 't := "" t...
[3]

uc5N"v0Q g q&M] 9yV*9>^Ƅtc׶ i_ > ?\

thebibliography [1] 20pt to REFERENCES 6pt =0pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command Each re...

2017
[4]

11em plus .33em minus .07em @technote 4000 4000 100 4000 4000 500 `\.=1000 = #1 #1 #1 0pt [0pt][0pt] #1 * \| ** #1 \@IEEEauthorblockNstyle \@IEEEauthorblockAstyle \@IEEEauthordefaulttextstyle \@IEEEauthorblockconfadjspace -0.25em \@IEEEauthorblockNtopspace 0.0ex \@IEEEauthorblockAtopspace 0.0ex \@IEEEauthorblockNinterlinespace 2.6ex \@IEEEauthorblockAinte...

work page
[5]

F., Changeat, Q., Waldmann, I

Al-Refaie, A. F., Changeat, Q., Waldmann, I. P., & Tinetti, G. 2019, TauREx III: A fast, dynamic and extendable framework for retrievals, , , arXiv:1912.07759

arXiv 2019
[6]

K., Aigrain , S., Irwin , P

Barstow , J. K., Aigrain , S., Irwin , P. G. J., & Sing , D. K. 2017, , 834, 50

2017
[7]

J., Hill , C., Yurchenko , S

Barton , E. J., Hill , C., Yurchenko , S. N., et al. 2017, , 187, 453

work page 2017
[8]

E., Kempton , E

Batalha , N. E., Kempton , E. M. R., & Mbarek , R. 2017, , 836, L5

work page 2017

Show all 51 references

[9]

L., Stevenson , K

Bean , J. L., Stevenson , K. B., Batalha , N. M., et al. 2018, Publications of the Astronomical Society of the Pacific, 130, 114402

2018
[10]

2015, Strict Upper Limits on the Carbon-to-Oxygen Ratios of Eight Hot Jupiters from Self-Consistent Atmospheric Retrieval, , , arXiv:1504.07655

Benneke, B. 2015, Strict Upper Limits on the Carbon-to-Oxygen Ratios of Eight Hot Jupiters from Self-Consistent Atmospheric Retrieval, , , arXiv:1504.07655

2015 arXiv
[11]

2019, Monthly Notices of the Royal Astronomical Society, 484, 3233

Borsato, L., Malavolta, L., Piotto, G., et al. 2019, Monthly Notices of the Royal Astronomical Society, 484, 3233. https://doi.org/10.1093/mnras/stz181

2019 doi
[12]

Brown, T. M. 2001, The Astrophysical Journal, 553, 1006. https://doi.org/10.1086

2001
[13]

2018, PyratBay retrieval code, ,

Cubillos. 2018, PyratBay retrieval code, , . https://pcubillos.github.io/pyratbay/index.html

2018
[14]

2013, Science, 342, 1473

de Wit , J., & Seager , S. 2013, Science, 342, 1473

2013
[15]

2019, The Astronomical Journal, 157, 242

Edwards, B., Mugnai, L., Tinetti, G., Pascale, E., & Sarkar, S. 2019, The Astronomical Journal, 157, 242. https://doi.org/10.3847

2019
[16]

2018, Experimental Astronomy, doi:10.1007/s10686-018-9611-4

Edwards, B., Rice, M., Zingales, T., et al. 2018, Experimental Astronomy, doi:10.1007/s10686-018-9611-4

2018 doi
[17]

2018, Monthly Notices of the Royal Astronomical Society, 481, 4698–4727

Fisher, C., & Heng, K. 2018, Monthly Notices of the Royal Astronomical Society, 481, 4698–4727. http://dx.doi.org/10.1093/mnras/sty2550

2018 doi
[18]

Fortney , J. J. 2005, , 364, 649

2005
[19]

2018, , 474, 271

Gandhi , S., & Madhusudhan , N. 2018, , 474, 271

2018
[20]

S., Wilzewski , J

Gordon , I., Rothman , L. S., Wilzewski , J. S., et al. 2016, in AAS/Division for Planetary Sciences Meeting Abstracts, Vol. 48, AAS/Division for Planetary Sciences Meeting Abstracts \#48, 421.13

2016
[21]

Griffith, C. A. 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372, 20130086. http://dx.doi.org/10.1098/rsta.2013.0086

2014
[22]

2016, Atmospheric Retrievals from Exoplanet Observations and Simulations with BART , NASA Proposal id.16-XPR16-10, ,

Harrington , J. 2016, Atmospheric Retrievals from Exoplanet Observations and Simulations with BART , NASA Proposal id.16-XPR16-10, ,

2016
[23]

2017, Monthly Notices of the Royal Astronomical Society, 470, 2972–2981

Heng, K., & Kitzmann, D. 2017, Monthly Notices of the Royal Astronomical Society, 470, 2972–2981. http://dx.doi.org/10.1093/mnras/stx1453

2017 doi
[24]

2015, The Astrophysical Journal, 803, L9

Heng, K., Wyttenbach, A., Lavie, B., et al. 2015, The Astrophysical Journal, 803, L9. http://dx.doi.org/10.1088/2041-8205/803/1/L9

2015 doi
[25]

N., & Tennyson , J

Hill , C., Yurchenko , S. N., & Tennyson , J. 2013, , 226, 1673

2013
[26]

Irwin , P. G. J., Teanby , N. A., de Kok , R., et al. 2008, , 109, 1136

2008
[27]

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

Kempton, E. M.-R., Lupu, R., Owusu-Asare, A., Slough, P., & Cale, B. 2017, Publications of the Astronomical Society of the Pacific, 129, 044402. http://dx.doi.org/10.1088/1538-3873/aa61ef

2017 doi
[28]

2019, arXiv e-prints, arXiv:1910.01070

Kitzmann , D., Heng , K., Oreshenko , M., et al. 2019, arXiv e-prints, arXiv:1910.01070

2019 arXiv
[29]

M., Mordasini, C., et al

Lavie, B., Mendonça, J. M., Mordasini, C., et al. 2017, The Astronomical Journal, 154, 91. http://dx.doi.org/10.3847/1538-3881/aa7ed8

2017 doi
[30]

2008, Astronomy & Astrophysics, 481, L83–L86

Lecavelier des Etangs, A., Pont, F., Vidal-Madjar, A., & Sing, D. 2008, Astronomy & Astrophysics, 481, L83–L86. http://dx.doi.org/10.1051/0004-6361:200809388

2008 doi
[31]

Lee, J.-M., Heng, K., & Irwin, P. G. J. 2013, The Astrophysical Journal, 778, 97. https://doi.org/10.1088

2013
[32]

R., & Parmentier, V

Line, M. R., & Parmentier, V. 2016, The Astrophysical Journal, 820, 78. http://dx.doi.org/10.3847/0004-637X/820/1/78

2016 doi
[33]

R., Zhang , X., Vasisht , G., et al

Line , M. R., Zhang , X., Vasisht , G., et al. 2012, , 749, 93

2012
[34]

R., Wolf , A

Line , M. R., Wolf , A. S., Zhang , X., et al. 2013, , 775, 137

2013
[35]

D., Coughlin, J

L \' o pez-Morales, M., Haywood, R. D., Coughlin, J. L., et al. 2016, The Astronomical Journal, 152, 204. https://doi.org/10.3847

2016
[36]

J., & Madhusudhan , N

MacDonald , R. J., & Madhusudhan , N. 2017, , 469, 1979

2017
[37]

2009, , 707, 24

Madhusudhan , N., & Seager , S. 2009, , 707, 24

2009
[38]

P., van Boekel, R., et al

Mollière, P., Wardenier, J. P., van Boekel, R., et al. 2019, Astronomy & Astrophysics, 627, A67. http://dx.doi.org/10.1051/0004-6361/201935470

2019 doi
[39]

2020, Experimental Astronomy

Mugnai , L., Pascale , E., Edwards , B., Papageorgiou , A., & Sarkar , S. 2020, Experimental Astronomy

2020
[40]

W., & Min, M

Ormel, C. W., & Min, M. 2019, Astronomy & Astrophysics, 622, A121. http://dx.doi.org/10.1051/0004-6361/201833678

2019 doi
[41]

2019, Monthly Notices of the Royal Astronomical Society, 482, 1485

Pinhas, A., Madhusudhan, N., Gandhi, S., & MacDonald, R. 2019, Monthly Notices of the Royal Astronomical Society, 482, 1485. http://dx.doi.org/10.1093/mnras/sty2544

2019 doi
[42]

P., Venot , O., Lagage , P

Rocchetto , M., Waldmann , I. P., Venot , O., Lagage , P. O., & Tinetti , G. 2016, , 833, 120

2016
[43]

S., & Gordon , I

Rothman , L. S., & Gordon , I. E. 2014, in 13th International HITRAN Conference, June 2014, Cambridge, Massachusetts, USA

2014
[44]

G., Collins , K

Stassun , K. G., Collins , K. A., & Gaudi , B. S. 2017, , 153, 136

2017
[45]

N., Al-Refaie, A

Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., et al. 2016, Journal of Molecular Spectroscopy, 327, 73 , new Visions of Spectroscopic Databases, Volume II. http://www.sciencedirect.com/science/article/pii/S0022285216300807

2016
[46]

2018, Experimental Astronomy, doi:10.1007/s10686-018-9598-x

Tinetti , G., Drossart , P., Eccleston , P., et al. 2018, Experimental Astronomy, doi:10.1007/s10686-018-9598-x

2018 doi
[47]

P., Zingales , T., et al

Tsiaras , A., Waldmann , I. P., Zingales , T., et al. 2018, , 155, 156

2018
[48]

P., Rocchetto , M., Tinetti , G., et al

Waldmann , I. P., Rocchetto , M., Tinetti , G., et al. 2015 a , , 813, 13

2015
[49]

P., Tinetti , G., Rocchetto , M., et al

Waldmann , I. P., Tinetti , G., Rocchetto , M., et al. 2015 b , , 802, 107

2015
[50]

2019, The Astronomical Journal, 157, 206

Welbanks, L., & Madhusudhan, N. 2019, The Astronomical Journal, 157, 206. http://dx.doi.org/10.3847/1538-3881/ab14de

2019 doi
[51]

Zhang , M., Chachan , Y., Kempton , E. M. R., & Knutson , H. A. 2019, , 131, 034501

2019

Pith tools

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

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month note number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.co...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION format.doi doi empty "" "doi:" doi * if FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix ":" * if eprint field.or.null * if FUNCTION format.pid eprint empty format.doi format.eprint if FUNCTION n.dashify 't := "" t...

[3] [3]

uc5N"v0Q g q&M] 9yV*9>^Ƅtc׶ i_ > ?\

thebibliography [1] 20pt to REFERENCES 6pt =0pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command Each re...

2017

[4] [4]

11em plus .33em minus .07em @technote 4000 4000 100 4000 4000 500 `\.=1000 = #1 #1 #1 0pt [0pt][0pt] #1 * \| ** #1 \@IEEEauthorblockNstyle \@IEEEauthorblockAstyle \@IEEEauthordefaulttextstyle \@IEEEauthorblockconfadjspace -0.25em \@IEEEauthorblockNtopspace 0.0ex \@IEEEauthorblockAtopspace 0.0ex \@IEEEauthorblockNinterlinespace 2.6ex \@IEEEauthorblockAinte...

work page

[5] [5]

F., Changeat, Q., Waldmann, I

Al-Refaie, A. F., Changeat, Q., Waldmann, I. P., & Tinetti, G. 2019, TauREx III: A fast, dynamic and extendable framework for retrievals, , , arXiv:1912.07759

arXiv 2019

[6] [6]

K., Aigrain , S., Irwin , P

Barstow , J. K., Aigrain , S., Irwin , P. G. J., & Sing , D. K. 2017, , 834, 50

2017

[7] [7]

J., Hill , C., Yurchenko , S

Barton , E. J., Hill , C., Yurchenko , S. N., et al. 2017, , 187, 453

work page 2017

[8] [8]

E., Kempton , E

Batalha , N. E., Kempton , E. M. R., & Mbarek , R. 2017, , 836, L5

work page 2017