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REVIEW 3 major objections 6 minor 127 references

Open Source High-Resolution Exoplanet Atmosphere Retrievals with POSEIDON

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read One open-source framework unifies high-resolution emission and transmission retrievals and shows detrending choices change abundances.

desk verdict Open-source unification of HRCCS retrievals in POSEIDON is real and useful; the α≈0.5 offset deserves fuller quantitative treatment, but the paper should be peer reviewed. read the letter →

arxiv 2505.09933 v1 pith:MP3R6AWU submitted 2025-05-15 astro-ph.EP astro-ph.IM

Ruizhe Wang , Ryan J. MacDonald , Neale P. Gibson , Nikole K. Lewis This is my paper · ORCID
classification astro-ph.EPastro-ph.IM
keywords high-resolutionspectroscopyatmosphericretrievalexoplanetatmospheresHRCCStransmissionemissionBayesianinferencedetrending
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 sets out to make high-resolution cross-correlation spectroscopy (HRCCS) retrievals a routine, open-source activity rather than a custom-analysis specialty. It claims that one framework, added to the POSEIDON retrieval code, can handle both emission and transmission spectra, complete a typical retrieval in under 12 hours on CPUs alone, and reproduce previously published abundance results for WASP-77Ab and WASP-121b when the same detrending assumptions are used. The reason a reader should care is that this removes the GPU and specialist-code barriers that have kept HRCCS retrievals out of reach for many groups, and it exposes a systematic effect: different, equally standard detrending choices change the retrieved chemical abundances, enough that a spurious ammonia detection appears or disappears in WASP-77Ab.

What carries the argument

The load-bearing device is fast filtering: after detrending, a low-rank basis $U$ describes the telluric, stellar, and instrumental background in each spectral order; every trial model $M$ is then filtered by projecting it out of that basis, $M' = U(\Lambda U)^\dagger (\Lambda M)$, where $\Lambda$ weights pixels by their time- and wavelength-dependent uncertainties. Because $U(\Lambda U)^\dagger \Lambda$ is independent of the model, it is computed once per order and reused across the hundreds of thousands of models in a retrieval. This operation, rather than the forward model itself, is what makes the runtime drop to hours on a CPU.

What would settle it

Take a real or synthetic dataset, inject a planet signal at several levels of overlap with the telluric/systematic basis (for example, shift one strong line onto a deep telluric line, or add a stationary component to the injected signal), and run the retrieval. If the recovered mixing ratios drift away from the injected values as the overlap increases, the linear-projection assumption has failed; a clean version is to compare the recovered scale factor $\alpha$ to its injected value across the overlap grid, since the paper already finds $\alpha$ at roughly half the injected value in its fiducial tests.

Watch

Extended reading notes

Core claim

On its own terms, the paper's claim is that the distortion a detrending step (PCA, SYSREM, or NMF) imposes on a planet signal can be mimicked by a single linear projection applied to every trial forward model, and that with this operation in place a Bayesian retrieval over lines, temperatures, Doppler shifts, and scale factors is computationally cheap. Fast filtering replaces the old scheme of injecting each model into the background and refiltering it, cutting the per-model cost to a precomputable projection. The paper validates the framework with injection-recovery tests on realistic data, then re-derives the atmospheric properties of the hot Jupiter WASP-77Ab in emission and the ultra-hot Jupiter WASP-121b in transmission, finding results broadly consistent with previous published retrievals. It also reports that the retrieved signal scale factor comes out about half the injected value, that abundance posteriors shift with the choice of detrending method, and that a low-significance NH3 detection in WASP-77Ab is an artifact of the filter choice.

Load-bearing premise

The analysis assumes that applying the same linear filtering used on the data to the forward model reproduces how the real planet signal was distorted; a signal that overlaps the removed background loses part of itself to the filter, which would skew the retrieved abundances.

Editorial extensions

If this is right

  • Typical emission and transmission retrievals finish in about seven hours on twenty-four CPU cores, so HRCCS abundance analyses no longer require GPUs or proprietary code.
  • Reproducing the WASP-77Ab and WASP-121b results validates the method against independent analyses, which should increase confidence in, and scrutiny of, published high-resolution abundance constraints.
  • Because detrending choices shift abundance posteriors, published single-method HRCCS constraints carry a systematic uncertainty that the paper's experiments bound but do not remove.
  • The framework is positioned to combine high- and low-resolution data, since it reuses POSEIDON's forward models; this is the paper's stated goal for future work.
  • A spurious NH3 detection in WASP-77Ab that depends on filter choice implies that chemical detections made with a single filtering method should be re-checked with at least one other method.

Reading between the lines

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

  • A natural next step would be to treat the filter choice as a nuisance systematic, running every retrieval with two independent detrenders and quoting the spread as part of the error budget; the paper's results imply the spread can be comparable to the statistical uncertainty.
  • The roughly factor-of-two deficit in the retrieved scale factor suggests a calibration diagnostic: if injection tests covering realistic signal-to-noise and telluric overlap can map how much signal each filter removes, $\alpha$ could become a correction factor for abundance estimates rather than just a nuisance parameter.
  • Because the framework runs on CPUs in hours, the large archive of existing high-resolution observations could be re-analyzed at the population level, turning individual-target HRCCS retrievals into survey-scale measurements.
  • Combining the fast-filtering projection with POSEIDON's existing multidimensional forward models would allow a direct test of whether 3D morning-evening or day-night structures can survive the detrending distortion; this is an implication the paper notes as future work but does not demonstrate.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper introduces a high-resolution cross-correlation spectroscopy (HRCCS) retrieval framework implemented in the open-source POSEIDON code, supporting both emission and transmission spectroscopy. It describes the forward-model modifications, detrending methods (PCA, SYSREM, NMF), a fast-filtering preprocessing step, and a Gaussian likelihood mapping that follows previous work by Brogi & Line (2019) and Gibson et al. (2020, 2022). The framework is validated with injection tests on real IGRINS and UVES data and then applied to WASP-77Ab emission and WASP-121b transmission observations, broadly reproducing previously published retrievals while documenting sensitivity to the choice of detrending filtering.

Significance. If the central claim holds, this paper makes a substantial contribution to the exoplanet high-resolution spectroscopy community by providing an open-source, CPU-only, documented retrieval framework within the widely used POSEIDON code, with runtimes an order of magnitude faster than earlier GPU-based implementations. The validation strategy—injecting known signals into real data and comparing retrievals against published WASP-77Ab and WASP-121b results—is appropriate and non-circular. The paper also explicitly demonstrates that detrending choices (PCA vs SYSREM) propagate into retrieved abundances, including a spurious NH3 detection in WASP-77Ab, which is a valuable public caveat for HRCCS abundance studies.

major comments (3)
  1. [4.3, Eq. (4)] The injection tests recover the scale factor alpha at roughly half its injected value. The authors attribute this to degeneracy with the retrieved P-T profile and to the filtering process, but they do not demonstrate that the attenuation introduced by the fast-filtering projection M' = U(Lambda U)^dagger (Lambda M) is a uniform scalar. If the projection removes signal in a wavelength-dependent or line-strength-dependent manner, then a single scalar alpha cannot correct for it, and retrieved abundances derived from relative line depths will be biased. The paper states that abundances are 'accurately constrained' and points to corner plots, but it does not report quantitative injection-versus-retrieval statistics (e.g., bias and scatter for each species) in the text. To support the central validation claim, the authors should provide such statistics for both emission and transmission injection tests and show that the alpha deficit is not wavelength-dependent, for example by testing recovery on wavelength subsets or comparing line ratios in the filtered models.
  2. [4.3] Two supporting experiments are mentioned but not shown: the authors write that 'We confirm the scaling of the signal remains unchanged after the filtering through a noiseless realization of data' and that 'we have run another experiment demonstrating the scaling can be correctly retrieved when the true profile is isothermal.' These experiments are load-bearing because they are used to argue that the alpha offset is benign and attributable to T-P degeneracy rather than to filter-induced distortion. Without presenting these results (at least in an appendix), the claim that the alpha offset does not affect abundance accuracy is unsupported. The manuscript should include the relevant figures or tables, or remove these claims.
  3. [5.2, Figure 9] The application to WASP-77Ab shows that retrieved chemical abundances differ by about 0.2 dex between PCA and SYSREM (e.g., log H2O is -4.33 with fixed R_p and PCA versus -4.12 with fixed R_p and SYSREM), and that NH3 is spuriously detected with SYSREM but not with PCA. The text acknowledges these user-choice differences, but the paper still concludes that 'posteriors are in agreement' with Line et al. (2021). Given the central claim is a validated framework for reliable abundance constraints, the authors should quantify the systematic spread introduced by detrending choices and explicitly state the effective abundance precision of the framework (e.g., 0.2-0.5 dex) in the summary. Without this, the reliability claim is overstated.
minor comments (6)
  1. [1] The acronym 'HRCSS' appears in the Introduction ('HRCSS retrieval techniques have seen limited application') and should be 'HRCCS'.
  2. [2.4] In the paragraph on shifting the model to the data wavelength grid, 'This is typically done by by interpolation' contains a duplicated 'by'.
  3. [4.3, Table 2] Section 4.3 states that the planet radius was fixed in the transmission injection test, but Table 2 lists R_r,ref as a free parameter with a Gaussian prior. Please clarify whether the radius was fixed in that test and, if so, remove it from the table or mark it as fixed.
  4. [6.2] The sentence 'The offset in absolute abundances can be attributed the strong correlation between each chemical abundance and cloud deck pressure' is missing a 'to' after 'attributed'.
  5. [7] In the first sentence of Section 7, 'transmission and emission spectrosa' should be 'spectra'.
  6. [5.2] The paper states 'We have no concrete evidence of SYSREM outperforming PCA in preserving planet signal' but later the same section says the stronger CO detection 'hints at SYSREM's improvement in preserving planet signals.' These statements are in tension; please rephrase to be consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the retrieval pipeline is validated against injected signals and independent published retrievals, not derived from its own outputs.

full rationale

The paper's contribution is an open-source implementation of high-resolution cross-correlation spectroscopy retrievals, and its derivation chain does not reduce to its inputs by construction. The likelihood mapping in Section 2.5 is explicitly attributed to prior work (Brogi & Line 2019; Gibson et al. 2020), and the fast-filtering preprocessing in Section 2.4 (Eq. 4) is credited to Gibson et al. (2022); neither is presented as a new first-principles result. The central validation is against external benchmarks: injection tests with known atmospheric parameters into real datasets (Section 4), reproduction of previously published emission retrievals of WASP-77Ab (Section 5), and transmission retrievals of WASP-121b (Section 6). The self-citations to POSEIDON and Gibson et al. are to open-source code and prior methodology, and they are not load-bearing in a way that forces the conclusions. The paper candidly reports that the retrieved scale factor alpha is about half the injected value and that detrending choices change retrieved abundances, demonstrating that the framework is not engineered to return its inputs. The alpha offset and filtering-induced changes are modeling limitations, not circular reasoning, because the results are compared against independent published values and the code is publicly available for external checks.

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

The central claim does not rest on a fitted physical constant, but on methodological choices: the number of detrending components, filter widths, noise model coefficients, and the linear-projection approximation of filtering distortion. The 1D hydrogen-dominated atmospheric model and the isothermal transmission assumption are explicit modeling choices. No new physical entities are introduced.

free parameters (4)
  • Number of PCA/SYSREM components removed = 10
    Chosen empirically in Section 5.1 after comparing CCFobs, CCFinj, and DeltaCCF approaches (Figure 6). Retrieved abundances depend on this choice.
  • Blaze correction filter widths = median 501, Gaussian 100
    Adopted from Gibson et al. (2022); the widths are chosen empirically in Section 3.2.
  • Noise model coefficients a and b = fit per order
    Used in the heteroscedastic noise model sigma_i = sqrt(a F_i + b) estimated via maximum likelihood on PCA residuals (Section 2.3).
  • Detection mask size for CCF significance = variable, square mask covering 95% of outliers
    Detection significance changes with mask size (Figure 7); the authors propose a rule of thumb because there is no unique choice.
assumptions (5)
  • standard math Gaussian pixel noise with known standard deviation up to a constant factor beta, which is profiled out analytically.
    Used to map cross-correlation to a Gaussian likelihood (Section 2.5), following Brogi & Line (2019) and Gibson et al. (2020).
  • domain assumption The distortion induced by PCA/SYSREM detrending on the planet signal is adequately approximated by linear projection onto the low-rank basis U.
    Central to the fast-filtering approximation (Eq. 4 in Section 2.4). Injection tests show alpha retrieved at about half its injected value, so the approximation is imperfect and load-bearing.
  • domain assumption The planet atmosphere is 1D, hydrogen-dominated, with vertically constant mixing ratios and a fixed He/H2 ratio of 0.17.
    Forward model restriction stated in Section 2.1; limits applicability to hot giant planets and ignores 3D and gradient effects.
  • domain assumption Transmission spectra are insensitive to temperature gradients, justifying the isothermal atmosphere assumption.
    Validated in injection tests (Section 4.3, Figure 12); the authors conclude the isothermal assumption does not bias their WASP-121b retrievals.
  • domain assumption A PHOENIX stellar model accurately represents the stellar spectrum when computing Fp/Fs for emission spectroscopy.
    Used to construct the emission forward model in the injection step (Section 2.4, Eq. 2).

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Cite this review

Pith. "Pith review of Open Source High-Resolution Exoplanet Atmosphere Retrievals with POSEIDON." pith.science (2026). https://pith.science/paper/MP3R6AWU

@misc{pith2026250509933,
  author       = {Pith},
  title        = {Pith review of: Open Source High-Resolution Exoplanet Atmosphere Retrievals with POSEIDON},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MP3R6AWU}},
  note         = {Machine review of arXiv:2505.09933}
}
read the original abstract

High-resolution spectroscopy (R > 25,000) has opened new opportunities to characterize exoplanet atmospheres from the ground. By resolving individual lines in planetary emission and transmission spectra, one can sensitively probe the chemical inventory and temperature structure of exoplanets. However, a significant challenge to reliable and reproducible atmospheric inferences from high-resolution datasets has been the lack of open source codes for high-resolution retrievals. Here, we present a unified high-resolution retrieval framework, for both emission and transmission spectroscopy, made publicly available within the open source POSEIDON retrieval code. Our high-resolution retrieval framework is fast (typically < 12 hours), accessible (no GPUs required), and well-documented via Python notebooks. We validate our framework by reproducing previous emission retrievals of the hot Jupiter WASP-77Ab and transmission retrievals of the ultra-hot Jupiter WASP-121b. Our results are broadly consistent with those of published works when making the same data detrending assumptions, but we demonstrate that user choices can subtly propagate into retrieved chemical abundances.

Figures

Figures reproduced from arXiv: 2505.09933 by the authors.

Figure 1
Figure 1. Example high-resolution transmission and emission forward models. Left: emergent flux with log XH2O = −6. Right: transmission spectrum with log XFe = −6. Models scaled by α = 2 and models with doubled abundances are over-plotted for comparison. Models convolved by a Gaussian kernel with width 3 are also plotted. We provide a mathematical description of the data de￾trending and model preprocesssing in Section 2.4. We… view at source ↗
Figure 2
Figure 2. Panel 1: The observation injected with an enhanced signal (to be seen by eye for demonstration purposes). In this paper, red color theme is used for emission spectroscopy (left column) and blue is used for transmission spectroscopy (right column). Panel 2 left: Red consists of the background contribution from star, noise, and instrumental effects. Fp are injected planet signals at different phases. They highlight th… view at source ↗
Figure 3
Figure 3. Upper panel: Example transmission spectra shifted according to planet velocities at each phase weighted by the transit shape. Notice out-of-transit spectra are equal to 1. Lower panel: Removing the transit shape by dividing the median of wavelength for each phase. 2.5. Likelihood Mapping and Retrieval We acknowledge that the following derivation has ap￾peared several times in the literature (e.g. Brogi & Line 2019; … view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Upper panel: the reduced IGRINS observation of WASP-77Ab. The flux at each phase is calibrated to the same level. The emission signal of WASP-77Ab is buried under the background and noise. Lower panel: Residual after removing the first 4 principal components. The resid…
Figure 5
Figure 5. Figure 5: Upper panel: the raw UVES observation of WASP-121b. Middle Panel: the observation after blaze cor￾rection. The transmission features of the planet is buried under the background and noise. Lower panel: residual after filtering. blue arm covers 0.37–0.50 µm over 31 spec…
Figure 6
Figure 6. Figure 6: Detection significance (S/N) versus number of PCA iterations. Unlike Cheverall et al. (2023), we do not find that detection strengths decay as more principal com￾ponents are removed, nor do we find a significant advantage for one approach over the others. We therefore …
Figure 8
Figure 8. Figure 8: Upper: detection significance as a function of Kp and ∆Vsys. White dashed lines indicate the literature values and the marker denotes the location of the peak signal. Lower: Cross correlation value divided by the standard deviation per row, as a function of phase and r…
Figure 9
Figure 9. Figure 9: Results of the retrieval for the IGRINS WASP-77Ab data. See [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Detection significance of an injected signal as a function of Kp and ∆Vsys and detection significance as a function of phase and planet radial velocity (normalized by standard deviation per phase). Red: emission. Blue: transmission [PITH_FULL_IMAGE:figures/full_fig_p…
Figure 11
Figure 11. Figure 11: Full corner plot of retrieval results on injected data (log XH2O = log XCO = log XCH4 = log XNH3 = −4) along with retrieved P-T profile using SYSREM. Run time: 13 hours on 24 cores [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: Full corner plots of retrieval results on simulated WASP-121b data. The PT profile of the injection signal is parameterized by six parameters. Blue: retrieval assuming isothermal atmosphere. Red: retrieval with six-parameter P-T profile. Upper right: true P-T profile …
Figure 13
Figure 13. Figure 13: Full corner plot of the retrievals on IGRINS WASP-77Ab data along with retrieved P-T profile. Blue: PCA; run time: 11 hours on 24 cores. Red: SYSREM; run time: 6.6 hours on 24 cores [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]
Figure 14
Figure 14. Figure 14: Full corner plot of the retrieval for the combined red and blue WASP-121b data along with retrieved P-T profile. Run time: 7.3 hours on 24 cores [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]
Figure 15
Figure 15. Figure 15: Cross-correlation results with CO and H2O separately for WASP-77Ab. White dashed lines are approximately the published values [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.