Remote sensing of exoplanetary atmospheres with ground-based high-resolution near-infrared spectroscopy

A. Reiners; D. Shulyak; F. Yan; M. Rengel; U. Seemann

REVIEW 3 major objections 6 minor 43 references

Remote sensing of exoplanetary atmospheres with ground-based high-resolution near-infrared spectroscopy

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Ground-based high-resolution near-infrared spectra can recover a hot Jupiter's vertical temperature profile from 1 bar down to 10^-6 bar, and two infrared bands can pin down molecular abundances.

desk verdict A useful, honest feasibility study of CRIRES+ retrievals, but the 'assumption-free temperature' claim is overstated because the model assumes constant molecular mixing ratios and validates synthetic data with its own forward model. read the letter →

arxiv 1908.10695 v1 pith:FUVXH2L7 submitted 2019-08-26 astro-ph.EP astro-ph.IMastro-ph.SR

D. Shulyak , M. Rengel , A. Reiners , U. Seemann , F. Yan This is my paper · ORCID

classification astro-ph.EPastro-ph.IMastro-ph.SR

keywords exoplanetaryatmosphereshotJupitershigh-resolutionspectroscopyatmosphericretrievaloptimalestimationtemperatureprofilemolecularabundancesnear-infrared

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 establishes, by simulation, that ground-based high-resolution near-infrared spectroscopy—the kind the CRIRES+ instrument will deliver—can move exoplanet atmosphere studies from detecting molecules to measuring structure. Using the hot Jupiter HD 189733b as a test case, the authors generate synthetic emission spectra, add realistic noise, and run an optimal-estimation retrieval that treats the temperature in each of 50 atmospheric layers as a free parameter. They find that the vertical temperature distribution can be recovered from about 1 bar down to $10^{-4}$ bar with a single spectral band, and down to $10^{-6}$ bar when certain bands are combined, with temperature errors of 70–120 K depending on signal-to-noise. Molecular mixing ratios are harder: accurate values for H2O, CO, and CO2 require S/N above 10 and simultaneous observations in two bands, ideally near 1.6 and 2.3 micron. If correct, this means existing and near-future ground-based spectrographs can characterize hot-Jupiter atmospheres in detail, not just detect their molecules.

What carries the argument

The machinery is an optimal-estimation retrieval (the standard Bayesian maximum-a-posteriori method of atmospheric remote sensing) wrapped around a line-by-line radiative-transfer forward model. The forward model computes the emitted spectrum from a 1D, cloud-free, LTE atmosphere using high-resolution molecular opacities; the retrieval adjusts 50 layer temperatures, four molecular mixing ratios, and continuum scaling factors to fit the simulated spectrum, with a correlation length of 1.5 pressure scale heights smoothing the temperature profile. Averaging kernels tell which pressure levels each spectral region actually constrains, and it is this sensitivity map that lets the authors claim a wide retrievable pressure range. The key enabler is the large simultaneous wavelength coverage of CRIRES+, which puts thousands of molecular lines of different strengths into a single spectrum, so different line depths probe different atmospheric depths.

What would settle it

Add a 0.5% telluric absorption residual and a 1% continuum-normalization error to the simulated spectra and re-run the retrieval; if the recovered temperature profile shifts by more than the quoted 70–120 K at any pressure between 1 bar and $10^{-4}$ bar, the paper's feasibility claim is falsified for real data.

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Extended reading notes

Core claim

The central claim is that assumption-free retrieval of the vertical temperature structure of a hot Jupiter is feasible from high-resolution (R = 100,000) near-infrared emission spectra: the temperature in each atmospheric layer is a free parameter, with no parameterized T-P profile imposed. In simulated CRIRES+ observations of HD 189733b, the retrieved temperatures match the true profile to within about 120 K at S/N=5 and 70–90 K at higher S/N, over pressures from 1 bar to $10^{-4}$ bar (single band) and up to $10^{-6}$ bar with the right band combination. The authors also find that accurate molecular number densities for H2O, CO, and CO2 require S/N > 10 and at least two spectral regions, with the 1.50–1.70 micron and 2.28–2.38 micron combination preferred; CH4 cannot be constrained at HD 189733b's assumed abundance but becomes retrievable in cooler or carbon-rich atmospheres where it is more abundant.

Load-bearing premise

The feasibility result rests on the assumption that real CRIRES+ spectra of a hot Jupiter differ from the model only by added Gaussian noise: same molecular opacities, same one-dimensional cloud-free LTE atmosphere, and no telluric residuals, instrumental artifacts, or data-reduction errors; if any of these fail, the quoted 70–120 K temperature errors are lower bounds and the retrieved abundances could be biased.

Editorial extensions

If this is right

With a single CRIRES+ setting at 2.28–2.38 micron, even S/N=5 is enough to recover the temperature profile down to about $10^{-4}$ bar, though molecular mixing ratios at that noise level can be biased unless the initial guess is close.
Observing two bands, 1.50–1.70 and 2.28–2.38 micron, at S/N=10 constrains the mixing ratios of CO and CO2 about twice as tightly as either band alone and brings temperature errors down to 70–90 K.
Lowering the spectral resolution from R=100,000 to R=50,000 degrades abundance retrievals but barely affects temperature retrievals, so observers can trade resolution for S/N and integration time.
Only two known hot Jupiters, 51 Peg b and tau Boo b, can reach the required S/N with affordable integration times from the southern hemisphere, with 51 Peg b needing about 48 hours for the preferred two-band S/N=10 configuration.
Methane becomes retrievable in cooler planets or carbon-rich (C/O>1) atmospheres, where its mixing ratio is orders of magnitude higher than in HD 189733b.

Reading between the lines

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

The paper does not explore it, but its S/N thresholds imply a concrete survey design: rank known and future hot Jupiters by host-star brightness and planet-to-star contrast, and allocate two-band (1.6 and 2.3 micron) observations only to targets that can reach S/N=10 in the available nights.
Because the simulated and retrieved spectra share the same forward model, the 70–120 K temperature errors are lower bounds; a mismatched-parameter test (e.g., retrieving with an older opacity table or a cloudy atmosphere) would quantify how much real-systematic error adds.
The paper's constant-mixing-ratio assumption means the retrieved abundances are vertical averages; extending the method to retrieve altitude-dependent mixing ratios, which the optimal-estimation framework can in principle handle, would be the natural next step and would test chemistry models.
If real CRIRES+ data on 51 Peg b reproduce the retrieved profile shape, the same two-band strategy could be applied to cooler or carbon-rich hot Jupiters, where the paper predicts methane becomes accessible; those targets would be a direct test of equilibrium-chemistry predictions.

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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

Two things to know. First, this is a solid and useful feasibility study: it gives quantitative expectations for what CRIRES+ can retrieve from hot Jupiter emission spectra, and its main recommendations — two bands at 1.6 and 2.3 microns, S/N>10 for mixing ratios, S/N=5 for temperature — are concrete and testable. Second, the headline claim of 'assumption-free' temperature retrieval is overstated, because the model assumes constant-with-altitude molecular mixing ratios, and the synthetic observations share that assumption. The stress-test note lands.

The genuinely new piece is the CRIRES+-specific, instrument-matched retrieval study with a free temperature at each of 50 layers, multiple band combinations, S/N levels, and exposure-time estimates. Earlier work by Lee et al. (2012) and Brogi & Line (2019) is related, but this paper goes further on observational strategy. The validation against real HST/Spitzer photometry in Fig. 1 is a real check, and the multiple-initial-guess tests show the OE solution is stable in the cases considered.

The circularity is real: tauREx generates the synthetic observations and tauREx retrieves them, so recovering the input truth confirms internal consistency, not accuracy against reality. The authors admit this, and the photometric validation helps, but the quoted 70–120 K errors are conditional on the forward model being right. The constant-VMR assumption is more serious because it is easy to miss: Section 2.2 says it in one sentence, while 'assumption-free' appears in the abstract and introduction. Real hot Jupiters will have vertical composition gradients from thermochemistry and photochemistry, and those can be absorbed into a biased T-P profile. Figure 4 already shows a temperature-abundance degeneracy at S/N=5; that degeneracy becomes depth-dependent when VMRs vary with pressure. So the pressure-range claim should be read as 'under constant composition.' Telluric and instrumental systematics are also absent; the authors say this and note weak telluric lines could matter at the 1e-3 level, but they do not quantify it.

Minor: the correlation length lcorr=1.5 and the a priori temperature uncertainty of 200 K are hand-chosen to keep retrievals smooth, and results could depend on them. Also the 4.8–5.0 micron region is mostly unhelpful except for high-altitude pressure; that is a nuance, not a flaw.

This paper is for people planning ground-based high-resolution observations and for retrieval code developers. It deserves a serious referee. I would send it out, but require the authors to either qualify or rename the assumption-free claim, and ideally add a test with an independent forward model or with non-constant VMRs. If that is done, this will be a useful reference.

Referee Report

3 major / 6 minor

Summary. This paper presents a simulation-based feasibility study for retrieving the thermal structure and molecular abundances of hot-Jupiter atmospheres from high-resolution near-infrared emission spectroscopy with CRIRES+. The authors generate synthetic day-side spectra of HD 189733b with the tauREx forward model at R=100,000 and R=50,000 in five spectral windows, add Gaussian noise at S/N = 5-50, and invert them with an optimal-estimation retrieval that fits temperatures at 50 pressure levels, four constant volume mixing ratios, and a continuum scaling per band. They validate their retrieval code against HST/Spitzer photometry of HD 189733b, reproduce the Lee et al. (2012) temperature and water abundance, and then report that temperature can be recovered to roughly 70-120 K between about 1 bar and 10^-4 bar (and up to 10^-6 bar with the 2.3 + 4.9 micron combination) while molecular abundances require S/N>10 and two bands. They close with observational strategies and an exposure-time target list for CRIRES+.

Significance. If the quantitative claims survive more realistic error injection, this is a useful and timely feasibility study: it maps which CRIRES+ settings are most informative, shows that two-band combinations help break abundance degeneracies, and provides concrete integration-time estimates for a small target list. Strengths of the paper include the validation against real HST/Spitzer photometry (Fig. 1), the explicit use of averaging kernels to define the sounded pressure range, the robustness checks over multiple initial guesses (Fig. 2), and the candid statement of what is not simulated. The main limitation is that the headline accuracy numbers are derived from a closed self-consistency loop: the same tauREx forward model, with the same constant-VMR assumption, generates and inverts the spectra, so the quoted errors are formal precisions under an idealized, telluric-free model rather than demonstrated accuracies for real CRIRES+ data. The paper's independent photometric validation is an important partial counterweight, but it operates at low spectral resolution and does not exercise the R=100,000 opacities and line shapes that the high-resolution claims depend on.

major comments (3)

[§2.2, §4.1, Abstract] The paper's central claim of 'assumption-free' retrieval of the vertical temperature profile is not supported as stated, because the forward model assumes constant mixing ratios of all molecular species with altitude (§2.2: 'we assumed constant mixing ratios of molecular species') and the synthetic observed spectra are generated with that same tauREx forward model. The retrieval therefore cannot be surprised by a vertically varying abundance, and any real composition gradient (e.g., thermochemical CO/CH4 layering, photochemical H2O variations) can be absorbed into a biased T-P profile while still fitting the data; this systematic is absent from both the recovery test and from the posterior covariance in Eq. (3). I request a test in which the input spectra are generated with a pressure-dependent VMR (e.g., a chemical-equilibrium profile) and retrieved with the constant-VMR model, quantifying the resulting T-P bias against the quoted 70-120 K errors.
[§4.2, §5, §6] The claim that ground-based CRIRES+ observations can probe temperatures up to 10^-6 bar rests on the (2.28-2.38)+(4.80-5.00) micron combination, yet the 4.8-5.0 micron window is exactly the region the authors describe as having 'many more telluric lines' with noticeably worse old-CRIRES performance (§5), and all simulated observations omit telluric absorption, flat-fielding, order merging, and wavelength calibration (§2.2 and §6). As the paper itself states, the impact of weak telluric lines on retrievals 'should be tested with real observations.' Until such effects are added or the claims are relabeled as idealized no-telluric limits, the abstract's 10^-6 bar statement overstates what is demonstrated for the actual instrument.
[§4.1, Fig. 4, Abstract] The abstract states that 'the temperature can already be derived accurately with the lowest value that we considered (S/N=5),' but the paper's own Fig. 4 shows that for the 2.28-2.38 micron region at S/N=5, an initial VMR guess 0.5 dex above the truth drives the retrieval to a different temperature solution with the same normalized cost function phi=0.73. The text acknowledges that for S/N<=10 'the final values may deviate from the true ones (but are still within the 3 sigma error bars) depending on the initial guess.' The headline S/N=5 temperature-accuracy claim therefore does not hold for all allowed starting points, and the quoted errors from Eq. (3) do not include this initialization-dependent scatter. Please report the spread of retrieved temperature profiles over the full grid of initial guesses at each S/N, or restrict the accuracy claim to S/N>=10.

minor comments (6)

[§1] In the first paragraph, 'Photometric observations are the more efficient than spectroscopic observations' should read 'are more efficient than'.
[§3] The text 'HD 198733 b' appears to be a typo for HD 189733 b.
[§4.1] The text refers to 'the EO method' in one place; this should be 'OE method' for consistency.
[Fig. 3 caption] The caption states 'randomly color-coded for clarity?' with a stray question mark, and the color coding of the red crosses versus red dashed lines is not fully explained in the caption.
[Table 1] The column labeled 'd' with unit 'a.u.' is described as 'distance to the parent star' but is actually the orbital semi-major axis; please rename the column and footnote accordingly.
[Abstract] The sentence 'a simultaneous observations in two separate infrared regions ... helps to obtain' has a subject-verb agreement error; it should be 'simultaneous observations ... help'.

Circularity Check

1 steps flagged · score 4.0 of 10

Closed-loop tauREx validation makes the quoted temperature-retrieval accuracy partly an internal-consistency result, not an independent prediction.

other [Section 2.2 and Section 4.1]
"Clearly, these scaling factors should be very close to unity in the case of accurate retrieval because the observed and predicted spectra were computed with the same code. ... The data was simulated using temperature structure and mixing ratios of HD 189733 b as derived in Lee et al. (2012)."

The synthetic 'observed' spectra (y in Eq. 1) are generated with the same tauREx forward model F that is later used to compute the model spectra y_i in the retrieval. Because both sides share F - identical opacities, 1D/LTE/cloud-free assumptions, and the constant-VMR assumption - any systematic bias in F cancels when comparing model to data. The retrieval is effectively solving F(x) = F(x_true) + noise, so recovering x_true is the internal-consistency solution. The quoted 70-120 K temperature errors (Eq. 3) are posterior covariances under this same model and noise model, so they exclude forward-model bias.

full rationale

The paper is a feasibility study using simulated observations, and it explicitly acknowledges idealized conditions and the same-code generation/retrieval loop. There is no load-bearing self-citation, no uniqueness theorem imported from the authors, and no fitted parameter renamed as a prediction. The external validation against HST/Spitzer photometry and the use of the independent Lee et al. (2012) atmospheric structure as injected truth give the central claim some independent content. However, the high-resolution temperature-retrieval prediction is validated only with the same tauREx forward model used to create the synthetic observations, which is an inverse-crime style self-consistency loop: forward-model errors and the constant-VMR assumption are common to both data generation and retrieval, so the quoted precision does not include those systematic biases. This partial circularity warrants a score of 4, not higher, because the retrieval algorithm itself is meaningfully exercised with noise, multiple initial guesses, and different spectral regions, and the paper openly discloses its ideal-case setup.

Assumptions & free parameters 6 free parameters · 7 assumptions · 0 invented entities

The paper's central claims rest on the tauREx forward model, the OE retrieval setup, and the chosen a priori hyperparameters. No new physical entities are introduced. The main burden is the self-consistency of using the same forward model for data generation and retrieval, plus the hand-chosen smoothing parameters.

free parameters (6)

Temperature values at 50 atmospheric layers
Each layer's temperature is a free parameter in the retrieval, enabling assumption-free T-P profiles (Sections 2.1 and 2.2).
Volume mixing ratios of H2O, CO, CO2, CH4
Assumed constant with altitude; retrieved from spectra. CH4 could not be constrained at the assumed abundance (Section 4.1).
Continuum scaling factor per spectral region = close to unity
Accounts for normalization mismatch between observed and modeled spectra; included in retrieval (Section 2.2).
Correlation length lcorr = 1.5 pressure scale heights
Hand-chosen smoothing parameter for a priori temperature covariance; same as Irwin et al. 2008 (Eq. 4).
A priori temperature uncertainty = 200 K
Chosen to balance smoothness and retrieval fidelity; values between 100 K and 500 K were tested (Section 4).
A priori VMR uncertainties = 0.5 and 1.0 dex
Tested values; results are mostly insensitive to this choice except at low S/N (Section 4).

assumptions (7)

domain assumption Radiative transfer with a 1D, plane-parallel, cloud-free, local thermodynamic equilibrium atmosphere as implemented in tauREx.
The forward model and its geometric/thermodynamic assumptions are adopted without independent verification (Section 2.2).
domain assumption Molecular opacities from ExoMol and HITEMP line lists are complete and accurate at R=100,000.
The retrieval accuracy depends on these line lists; no validation at the specific resolution is presented (Section 2.2).
domain assumption Collision-induced absorption from H2-H2 and H2-He is correctly modeled by the chosen CIA tables.
CIA opacities affect continuum shape and are taken from Abel or Borysow references without in-paper verification (Section 2.2).
domain assumption Retrieval model assumes constant mixing ratios with altitude.
The paper states this assumption explicitly to reduce free parameters and to match Lee et al. (2012) (Section 2.2).
ad hoc to paper A priori covariance with exponential correlation (Eq. 4) and lcorr=1.5 correctly encodes the smoothness of the temperature profile.
The correlation length is chosen by hand and is a hyperparameter of the OE method, not derived from physics (Section 2.2).
domain assumption Synthetic observations generated with the same forward model are representative of real CRIRES+ spectra.
The paper explicitly excludes telluric absorption, flat-fielding, order merging, wavelength calibration, and other systematics (Sections 2.2 and 6).
domain assumption The HD 189733b T-P profile and mixing ratios from Lee et al. (2012) represent the true atmospheric state used to generate the synthetic truth.
All simulated 'observations' are injected with this external model, so the retrieval truth is only as accurate as Lee et al. (2012) (Section 3).

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Cite this review

Pith. "Pith review of Remote sensing of exoplanetary atmospheres with ground-based high-resolution near-infrared spectroscopy." pith.science (2026). https://pith.science/paper/FUVXH2L7

@misc{pith2026190810695,
  author       = {Pith},
  title        = {Pith review of: Remote sensing of exoplanetary atmospheres with ground-based high-resolution near-infrared spectroscopy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FUVXH2L7}},
  note         = {Machine review of arXiv:1908.10695}
}

abstract

Thanks to the advances in modern instrumentation we have learned about many exoplanets that span a wide range of masses and composition. Studying their atmospheres provides insight into planetary origin, evolution, dynamics, and habitability. Present and future observing facilities will address these important topics in great detail by using more precise observations, high-resolution spectroscopy, and improved analysis methods. We investigate the feasibility of retrieving the vertical temperature distribution and molecular number densities from expected exoplanet spectra in the near-infrared. We use the test case of the CRIRES+, instrument at the Very Large Telescope which will operate in the near-infrared between 1 and 5 micron and resolving powers of R=100000 and R=50000. We also determine the optimal wavelength coverage and observational strategies for increasing accuracy in the retrievals. We used the optimal estimation approach to retrieve the atmospheric parameters from the simulated emission observations of the hot Jupiter HD~189733b. The radiative transfer forward model is calculated using a public version of the tauREx software package. Our simulations show that we can retrieve accurate temperature distribution in a very wide range of atmospheric pressures between 1 bar and $10^{-6}$ bar depending on the chosen spectral region. Retrieving molecular mixing ratios is very challenging, but a simultaneous observations in two separate infrared regions around 1.6 micron and 2.3 micron helps to obtain accurate estimates; the exoplanetary spectra must be of relatively high signal-to-noise ratio S/N>10, while the temperature can already be derived accurately with the lowest value that we considered in this study (S/N=5).

Figures

Figures reproduced from arXiv: 1908.10695 by the authors.

**Figure 1.** Top panel: Observed day side fluxes of HD 189733b and our best fit model predictions (see legend in the plot). Bottom panel: Retrieved T-P profile along with corresponding averaging kernels and mixing ratios of molecular species. We used the T-P profile of Lee et al. (2012) a priori (shown as red crosses). Our best fit model is shown with a solid blue line and the shaded area represents 1σ error bars. The assumed a … view at source ↗

**Figure 2.** Left panel: Retrieved temperature distribution assuming four different initial guesses: three homogeneous at 1000 K, 1500 K, and 2000 K (vertical dashed lines), and the one from Waldmann et al. (2015a) (blue dashed line). The retrieved best fit profiles are shown with solid lines along with corresponding error bars as shaded areas. The profile from Lee et al. (2012) is shown as a solid red line. Right panel: Averagi… view at source ↗

**Figure 3.** Retrieved temperature and mixing ratios from five spectral regions with R=100 000 and S/N=10. In each panel, the first plot compares the best fit predicted spectrum (solid red line) with the simulated observations (black). The second plot is the temperature distribution as a function of atmospheric pressure (solid blue line) and with error bars shown as shaded areas. The red crosses and green dashed line are the tru… view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Retrievals of the H2O and CO mixing ratios from the (2.28 − 2.38) µm region assuming three different initial guesses (from top to bottom) and four S/N values (from left to right). The green dashed and red dotted lines are the initial guess and true solutions, respectiv…

**Figure 5.** Figure 5: Comparison of retrieved temperature and mixing ratios of four molecules assuming S/N=10 and two spectral resolutions of R=100 000 and R=50 000 for the top and bottom panels, respectively. The color-coding is the same as in [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Retrieved temperature and mixing ratios of four molecular species from a combination of different spectral regions with a reference region 2.28 − 2.38 µm. The retrievals are shown for the case of S/N=10. The color-coding is the same as in [PITH_FULL_IMAGE:figures/full…

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Pith tools

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

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

ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

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Barstow , J. K., Aigrain , S., Irwin , P. G. J., Fletcher , L. N., & Lee , J. M. 2013, , 434, 2616

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