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One JWST mode's phase-resolved retrieval detects carbon monoxide in WASP-39 b and pins C/O at 0.68 and metallicity at 15× solar.

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2026-08-01 15:28 UTC pith:J24TDSSP

load-bearing objection First genuine phase-resolved cross-correlation retrieval on JWST transit data, with a convincing CO detection story — but the likelihood's treatment of correlated native-pixel noise is unvalidated, so the claimed precision needs injection-recovery tests before I'd trust it. the 4 major comments →

arxiv 2607.18409 v1 pith:J24TDSSP submitted 2026-07-20 astro-ph.EP astro-ph.IM

Precise Determination of the Metallicity and C/O of WASP-39~b From a Single JWST Instrument Mode with Phase-Resolved Cross-Correlation Retrievals

Arjun B. Savel , Eliza M.-R. Kempton , Erin M. May , Matthew C. Nixon , Jegug Ih , Katherine A. Bennett , Joost P. Wardenier This is my paper
classification astro-ph.EP astro-ph.IM
keywords exoplanet atmospherescross-correlation retrievalsJWST transmission spectroscopyC/O ratioatmospheric metallicityhot Jupitercarbon monoxideWASP-39 b
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper challenges the assumption that measuring an exoplanet's atmospheric metallicity and C/O ratio requires broad, multi-instrument wavelength coverage. By applying phase-resolved cross-correlation retrievals—a technique borrowed from ground-based high-resolution spectroscopy—to a single archival JWST NIRSpec/G395H transit kept at native pixel resolution, the authors detect and bound the abundances of all major carbon- and oxygen-bearing molecules in WASP-39 b: H2O, CO, CO2, and SO2. Crucially, CO is detected decisively, while standard binned retrievals on the same data fail to detect it. From these abundances, they derive a metallicity of [(C+O)/H] = 1.2±0.2 (≈15× solar) and a C/O ratio of 0.68+0.10/−0.14, broadly consistent with previous multi-instrument analyses. The paper therefore proposes a path to extracting full chemical inventories from existing single-mode datasets, sidestepping the need for new multi-mode observations.

Core claim

The paper's central claim is that phase-resolved cross-correlation retrievals on native-pixel, unbinned NIRSpec/G395H transmission light curves recover all major carbon- and oxygen-bearing molecules in WASP-39 b and yield accurate bulk composition parameters. Specifically, the authors obtain bounded abundances for H2O (ΔlnZ = 67), CO (ΔlnZ = 25), CO2 (ΔlnZ = 475), and SO2 (ΔlnZ = 10), whereas a standard retrieval on the same data yields only ΔlnZ = 0.2 for CO. This allows a robust derivation of a volatile-proxy metallicity of 1.2±0.2 and C/O = 0.68+0.10/−0.14, consistent with prior multi-instrument results, while the traditional retrieval on the same single-mode data produces a biased C/O of

What carries the argument

The carrying mechanism is the phase-resolved cross-correlation likelihood, in which model and data are mean-subtracted and compared through their empirical variances and correlation coefficient at each orbital phase, effectively fitting the noise rather than assuming known Gaussian uncertainties. The forward model is computed at very high spectral resolution (R ≈ 10^6), Doppler-shifted to the planet's changing radial velocity during transit, broadened for rotation, and limb-darkened separately for morning and evening limbs, while the spectral continuum is preserved rather than removed. This combination lets the narrow ro-vibrational lines of CO add coherently across the transit and remain di

Load-bearing premise

The detection significances and abundance bounds assume that, after mean-subtraction, the residual noise in each native-pixel phase bin is independent and Gaussian with variance well estimated from the data itself; if pixel-to-pixel correlations inflate those variance estimates, the CO detection and bounded abundances could be overconfident.

What would settle it

Run the same phase-resolved cross-correlation retrieval on a null dataset—WASP-39 b G395H light curves with the transit removed, or out-of-transit exposures—and check whether H2O, CO, CO2, and SO2 still yield significant ΔlnZ; alternatively, inject a synthetic atmosphere with known abundances into the real light curves and verify that the retrieval recovers unbiased posteriors.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Full carbon and oxygen inventories—and thus reliable C/O and metallicity—can be obtained from a single NIRSpec/G395H transit, eliminating the need for multi-instrument 1–5 μm campaigns on hot Jupiters.
  • CO is confirmed as the dominant carbon carrier in WASP-39 b; the previously puzzling non-detection in G395H was a binning and time-integration artifact, not an absence of the molecule.
  • Standard binned, time-integrated retrievals on the same data return a severely biased C/O (≈0.04 vs 0.68), demonstrating that CO non-detection can hide the dominant carbon reservoir and skew composition inferences.
  • The approach recovers bounded abundances for H2O, CO2, and SO2 at precision comparable to low-resolution retrievals, and marks the first cross-correlation detection of SO2 in JWST transmission data.
  • Applying these phase-resolved cross-correlation retrievals to additional existing and future G395H observations is likely to extract substantially more atmospheric information from legacy JWST datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the noise assumptions survive injection-recovery testing, the method could make single-mode C/O measurements routine for any hot Jupiter with G395H data, effectively expanding the sample of planets with formation-relevant composition constraints without new observations.
  • The retrieval shows sensitivity to the 13CO/12CO isotopologue ratio—though it does not formally detect it—hinting that carbon isotope ratios may become accessible from JWST transmission data, a measurable that could test planet formation pathways.
  • Because the cross-correlation likelihood fits noise per phase and preserves the continuum, it may systematically reduce aerosol–abundance degeneracies; a testable prediction is that cloud-top pressure and aerosol parameters from this method will differ from binned-retrieval results across many planets.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper presents a phase-resolved cross-correlation retrieval applied to archival NIRSpec/G395H transit data of WASP-39 b, using native-pixel binning and the Brogi & Line (2019) likelihood within the CHIMERA forward model and nautilus sampler. The authors report bounded abundances for H2O, CO, CO2, and SO2 from this single instrument mode, and derive an envelope metallicity [(C+O)/H] = 1.2 ± 0.2 and C/O = 0.68^{+0.10}_{-0.14}, in contrast to a standard binned retrieval on the same data that fails to detect CO. The central claim is that phase-resolved cross-correlation retrievals can extract the full C/O inventory from a single JWST mode without requiring broad multi-instrument wavelength coverage.

Significance. If the statistical calibration is sound, this is a genuinely useful methodological result: it would demonstrate that high-resolution spectral information in JWST datasets can be unlocked by phase-resolved cross-correlation, enabling abundance and C/O/metallicity constraints from a single mode. The paper is careful in several respects: it re-reduces archival data, explicitly treats limb darkening and rotational broadening, uses a nested sampler with a documented benchmark against MultiNest (Appendix A), and compares its results to literature values and to the JTEC-ERS Model Synthesis effort. However, the central statistical claim rests on a likelihood whose calibration under correlated native-pixel noise is not established. The absence of injection-recovery or noise-only false-positive tests leaves the reported ΔlnZ values and posterior widths unvalidated in exactly the regime the method targets.

major comments (4)
  1. [§2.3, Eq. (3); §4.2.1] The likelihood treats the N data points per phase as independent draws with a single variance estimated from the data. At native pixel resolution, G395H pixels are oversampled (74 km/s pixel scale versus the ~111 km/s resolution element), so adjacent residuals are strongly correlated. The paper acknowledges the enhanced pixel-to-pixel correlations (§4.2.1) but does not model them. For correlated noise, the empirical variance s_d^2 is a biased estimator of the noise variance and the effective independent sample size is smaller than N; the likelihood therefore overstates evidence and underestimates posterior widths. This directly affects every reported ΔlnZ (including the extreme ΔlnZ = 475 for CO2) and the abundance error bars that propagate into the C/O and metallicity claims. Add a correlated-noise covariance model or, at minimum, injection-recovery and noise-only false-positive tests t
  2. [Abstract vs. §3.1] There is an internal inconsistency in the primary detection claim: the Abstract reports ΔlnZ = 25 for CO, while §3.1 reports ΔlnZ = 23 for the same species in the same retrieval. This must be reconciled, and evidence values should be quoted with their sampling uncertainty. More broadly, the absolute scale of these ΔlnZ values is not meaningful without calibration: a noise-only permutation test would show whether values such as ΔlnZ = 475 are plausible false-positive rates under correlated noise.
  3. [§4.2] The paper attributes the gain to native pixel resolution and phase-resolved analysis, but the method changes three things simultaneously: native-pixel binning, phase-resolved Doppler shifting, and the cross-correlation likelihood. No ablation study isolates the contribution of phase-resolving. Without a comparison to a time-integrated (phase-averaged) cross-correlation retrieval, or a native-pixel Gaussian-likelihood retrieval, the specific attribution in §4.2.2 is not demonstrated. The comparison standard is the authors' own R = 100 binned retrieval; a direct comparison to published G395H retrievals (e.g., Alderson et al. 2023) for the same data would strengthen the claim that the method recovers CO where standard techniques do not.
  4. [§2.3, Eq. (3)] The likelihood uses the empirical variance of the mean-subtracted data and model. Because the continuum is preserved (as argued in §2.2), the data variance includes astrophysical signal, not just noise. The statement that Eq. (3) is 'mathematically equivalent' to a Gaussian likelihood with error inflation holds only for white noise and a model that predicts the data up to a scale factor. In the presence of correlated noise and a non-flat continuum, the statistical interpretation of Eq. (3) differs. This reinforces the need for validation on mock data with known injected abundances and realistic correlated noise.
minor comments (6)
  1. [Fig. B1, §3.1] The retrieved Vsin i posterior peaks at 9.61 km/s with the prior upper bound at 10 km/s; the distribution is truncated at the boundary. This should be reported as such, or the prior widened/reparameterized to avoid an edge-hugging solution.
  2. [§2.1] The re-reduction with Eureka! is described, but no comparison of the extracted native-pixel spectra to the published Alderson et al. (2023) spectra is shown. A brief sanity check, such as residual scatter per detector or a comparison of the binned transmission spectrum, would be useful for reproducibility.
  3. [§2.2, §3.1] The non-isothermal test is mentioned only as 'not shown.' Given that temperature can correlate with abundances, this test should be shown or summarized numerically, even if in an appendix.
  4. [Fig. B1] The 'ISO' parameter is described as the log ratio 13C16O/12C16O, but the prior is U(−5,0) and the posterior is bimodal with a very broad distribution. The label should be clarified, and the bimodality discussed more explicitly than the brief passage in §3.1.
  5. [§3.2] The Abstract quotes [(C+O)/H] = 1.2 ± 0.2, while §3.2 reports '15^{+9}_{-6} × solar.' The conversion to dex should be stated explicitly so that the two expressions are transparently consistent.
  6. [§4.1] The comparison to the JTEC-ERS Model Synthesis effort relies on a private communication. If possible, cite a public data product, repository, or publication so that the comparison is independently checkable.

Circularity Check

0 steps flagged

No significant circularity: abundances are retrieved parameters, C/O and metallicity are derived posterior quantities, and the method is benchmarked against external literature and community model comparisons.

full rationale

The derivation chain is non-circular. The gas abundances in the cross-correlation retrieval are free parameters sampled against the data using the Brogi & Line likelihood (Eq. 3); the reported Delta-lnZ values come from evidence comparisons between nested retrieval models (Section 2.4), and C/O and [(C+O)/H] are posterior functions of the fitted VMRs (Section 3.2), not quantities fed into the forward model. No fitted parameter is relabeled as a prediction. The method's novelty is operational (native-pixel, phase-resolved, single-mode), and the central claims are benchmarked against independent literature values and the multi-team Model Synthesis comparison rather than against a self-cited uniqueness theorem. The only author-overlapping citations (Ih & Kempton 2021 on nested-sampling bias; Welbanks et al. 2026 as part of the Model Synthesis effort) are not load-bearing for the derivation. The paper itself flags the pixel-correlation caveat in Section 4.2.1, which is a statistical-calibration risk for Eq. 3 rather than evidence of circular reasoning: no step reduces the output to an input by construction or imports a forced conclusion from a self-citation chain.

Axiom & Free-Parameter Ledger

11 free parameters · 6 axioms · 0 invented entities

The central inference rests on retrieved gas abundances; the forward model assumes isothermal structure, fixed line lists, and negligible methane. No new physical entities are introduced.

free parameters (11)
  • CO abundance (log10 VMR) = -2.1 +0.2/-0.3
    Retrieved gas abundance; drives C/O and is the central detection.
  • H2O abundance (log10 VMR) = -2.5 ± 0.3
    Retrieved gas abundance; major oxygen carrier.
  • CO2 abundance (log10 VMR) = -3.4 ± 0.3
    Retrieved gas abundance; contributes to C/O and metallicity.
  • SO2 abundance (log10 VMR) = -5.6 ± 0.2
    Retrieved gas abundance; minor but detected.
  • 13C/12C ratio (log10) = -2.68 +1.49/-1.55
    Minor CO isotopologue ratio; correlates with Vsys bimodality.
  • Isothermal temperature T = 845 ± 38 K
    Thermal profile parameter.
  • Cloud top pressure (log10) = 1.86 +0.22/-0.20
    Gray cloud deck pressure level.
  • Planetary radius scaling xRp = 0.84 ± 0.02
    Scaling factor for reference pressure radius.
  • Systemic velocity Vsys = -53.4 +6.9/-6.7 km/s (bimodal)
    Velocity offset; correlated with CO isotopologue.
  • Semi-amplitude Kp = 246 +34/-64 km/s (unbound)
    Orbital RV semi-amplitude; poorly constrained.
  • Rotational broadening Vrot (Vsin i) = 9.6 +0.2/-1.2 km/s
    Convolution kernel width for planet rotation.
axioms (6)
  • domain assumption Molecular line lists (H2O, CO, CO2, SO2) are accurate
    Section 2.2; the retrieval relies on these opacity sources for detection and abundance.
  • domain assumption Isothermal temperature profile
    Table 1; non-isothermal tests are mentioned but 'not shown' (Section 3.1).
  • domain assumption Circular orbit with RV = Kp sin(2πφ) + Vsys
    Equation (1); assumes circular orbit and a simple RV model.
  • domain assumption CH4 is negligible
    Section 3.2; justified by prior non-detections and a mass-balance argument, but an assumed input.
  • ad hoc to paper Cross-correlation likelihood valid for correlated native-pixel noise
    Section 4.2.1; the likelihood assumes independent Gaussian noise, but the data have enhanced pixel-to-pixel correlations. No covariance model or injection test is provided.
  • domain assumption Limb darkening per morning/evening limb
    Section 2.2 item 4; follows Gandhi et al. 2022, which may not be validated for this dataset.

pith-pipeline@v1.3.0-alltime-deepseek · 18845 in / 13900 out tokens · 111764 ms · 2026-08-01T15:28:51.512729+00:00 · methodology

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read the original abstract

Measuring atmospheric metallicities and C/O ratios is a key goal of JWST exoplanet science, given their proposed link to planet formation. Achieving this goal has previously been shown to require broad wavelength coverage ($\sim$1--5 $\mu$m), typically demanding multiple instrument modes to complete the molecular inventory. Here, we show that the multi-instrument requirement can be circumvented using phase-resolved cross-correlation retrievals at native pixel resolution -- an approach more typically applied to ground-based high-resolution spectroscopy. By applying this novel analysis technique to an archival single-mode transit of the hot Jupiter WASP-39~b obtained with NIRSpec/G395H, we detect and obtain bounded abundances for all of its major carbon- and oxygen-bearing molecules: H$_2$O ($\Delta \ln(Z) = 67$), CO ($\Delta \ln(Z) = 25$), CO$_2$ ($\Delta \ln(Z) = 475$), and SO$_2$ ($\Delta \ln(Z) = 10$). Notably, while standard retrieval methods fail to detect CO in these same data ($\Delta \ln(Z) = 0.2$), our approach detects it decisively, confirming it as the dominant carbon carrier in WASP-39 b's atmosphere. From these abundances, we robustly derive a metallicity of $\rm [(C + O) / H] = 1.2 \pm 0.2$ and a C/O ratio of $0.68^{+0.10}_{-0.14} $, generally consistent with previous multi-instrument analyses. In comparison, traditional retrievals performed on the G395H data alone produce biased and inaccurate values of both parameters, driven primarily by the non-detection of CO as well as incomplete water band coverage. Our results establish phase-resolved cross-correlation retrievals as a powerful tool for extracting maximum atmospheric information from existing and future JWST data sets.

Figures

Figures reproduced from arXiv: 2607.18409 by Arjun B. Savel, Eliza M.-R. Kempton, Erin M. May, Jegug Ih, Joost P. Wardenier, Katherine A. Bennett, Matthew C. Nixon.

Figure 1
Figure 1. Figure 1: Schematic comparing our retrievals (left) and standard JWST retrieval methodology (right). The retrievals that we present in this work begin with a different data product and fit for additional parameters with a different likelihood function. 2020; E. Esparza-Borges et al. 2023), we preserve the shape of the spectral continuum in our cross-correlation retrievals. Given the wavelength coverage and moder￾ate… view at source ↗
Figure 2
Figure 2. Figure 2: Our native pixel resolution cross-correlation constraints on gases in WASP-39 b using G395H data vs. those acquired with standard methods (R = 100, phase-inte￾grated). Our method is sensitive to previously hidden gases’ abundance. Specifically, we obtain a bounded abundance for CO. Vertical dashed lines mark the 16th and 84th percentiles for each distribution. the expected value under a circular orbit (≈ 1… view at source ↗
Figure 3
Figure 3. Figure 3: The (volatile-proxy) envelope metallicity and C/O ratio of WASP-39 b, as inferred by our native pixel res￾olution, cross-correlation, phase-resolved free retrieval (pur￾ple). With a single NIRSpec/G395H transit, we are able to simultaneously constrain both chemical parameters. As points of comparison, we also show the posterior distribu￾tions obtained without including the contribution of CO abundance (yel… view at source ↗

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Forward citations

Cited by 1 Pith paper

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

  1. Constraining the lives and times of exoplanets through evolutionary Bayesian retrievals

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    Evolutionary Bayesian retrievals using the PROTEUS model can infer initial volatile inventories and interior conditions of exoplanets from synthetic observables, with best success for terrestrial-mass planets.

Reference graph

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