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REVIEW 3 major objections 5 minor 81 references

Toward a unified framework for helium observations and interpretation of atmospheric escape

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A model transmission spectrum must be convolved as a ratio of stellar fluxes, not as an absorption profile; at JWST/NIRSpec resolution this changes a recovered He I signal from 3% to 2.52%.

desk verdict The convolution-bias claim is solid and worth taking seriously, but the paper's quantitative results are conditional on assumptions about FWHM and the instrumental line profile that the authors themselves flag. read the letter →

arxiv 2608.10707 v1 pith:QAVMN5YI submitted 2026-08-11 astro-ph.EP

Carteret Yann , Bourrier Vincent This is my paper · ORCID
classification astro-ph.EP
keywords atmosphericescapemetastableheliumtriplettransmissionspectroscopyinstrumentalconvolutionlow-resolutionretrievalshigh-resolutionJWSTNIRPS
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's central claim is that the standard shortcut in low-resolution transmission spectroscopy—convolving a model absorption spectrum with the instrument profile and comparing that directly to observations—introduces a systematic bias that grows as spectral resolution drops. The observed transmission spectrum is by construction a ratio of in-transit to out-of-transit stellar fluxes, each already degraded by convolution and resampling (Eq. 1), and convolution and ratio-taking do not commute. For the WASP-69 stellar spectrum, a true 3% Gaussian He I absorption with 0.7 Å FWHM is recovered as only $2.52\pm0.09\%$ by the direct-convolution approach at NIRSpec resolution—a 16% relative underestimate—and the paper derives an analytic bias factor $\kappa$ that predicts when the shortcut is safe. With the correct method, the paper then compares NIRPS, JWST/NIRSpec, and JWST/NIRISS, finding that NIRPS and NIRISS detect similar He I levels, NIRSpec wins for faint targets, and low-resolution space data cannot resolve outflow dynamics but are the right tool for measuring outflow extent and baseline coverage; if this is right, low-resolution helium pipelines must be rebuilt around Eq. 1.

What carries the argument

The load-bearing object is Eq. 1, the definition of the observed absorption spectrum as one minus the ratio of convolved in-transit to out-of-transit stellar fluxes, and the bias factor $\kappa(\lambda)$ derived in Appendix A.1. $\kappa$ is the ratio between the correctly computed absorption and the naive direct convolution of the planetary absorption profile; for Gaussian lines it has a closed form (Eq. A.5) that depends only on the widths, depths, and relative positions of the stellar and planetary lines and on the instrumental kernel. In the high-resolution and broadband limits $\kappa\to 1$, so the shortcut is harmless; in the intermediate-resolution regime relevant to He I with JWST, $\kappa$ can reach about 1.6 for a deep narrow stellar line, meaning the naive approach underestimates the true absorption and biases retrieved parameters. The rest of the paper uses this machinery to build instrument detection maps and to compare high- and low-resolution constraints.

What would settle it

Generate a NIRSpec-resolution mock observation from a known 3% Gaussian He I absorption using the WASP-69 stellar spectrum and Eq. 1, then fit it with the direct-convolution method: the paper predicts the recovered amplitude is $2.52\pm0.09\%$; a rerun with the same inputs that recovers 3% within errors would falsify the central claim. Alternatively, analyze one transit observed simultaneously with NIRPS and NIRSpec through Eq. 1 and check that the two instruments agree on the absorption amplitude while the direct-convolution NIRSpec fit is low by the predicted amount.

Watch

Extended reading notes

Core claim

The paper establishes that the transmission spectrum is defined by $A(\lambda,t)=1-\mathcal{R}_s(\mathcal{G}_R f_{\rm in})/\mathcal{R}_s(\mathcal{G}_R f_\star)$, so a model that instead smears a theoretical absorption profile and fits it to this ratio misestimates the signal. Assuming Gaussian stellar lines, planetary lines, and a Gaussian instrumental kernel, the deviation collapses to a single multiplicative factor $\kappa(\lambda)$ (Eq. A.5): at high resolution $\kappa\to 1$, for features much broader than the kernel $\kappa\to 1$, but in the intermediate regime of He I at NIRSpec resolution $\kappa$ departs from 1 and deep, narrow stellar lines like those of WASP-69 push the inferred peak absorption below the true value. Numerically, fitting a mock NIRSpec observation built from a true 3% peak, 0.7 Å FWHM Gaussian He I absorption with the star-biased (direct-convolution) method returns $2.52\pm0.09\%$, a 16% relative bias, while the same test with the shallower WASP-121 stellar lines gives a much smaller bias. The same treatment shows broadband molecular features are only mildly affected, because their intrinsic width exceeds both the kernel and the stellar lines. These results are independent of the thermospheric model and hold for low-resolution retrievals in general.

Load-bearing premise

The reported magnitude of the bias and the instrument rankings assume Turbospectrum stellar models for WASP-69 and WASP-121, a Gaussian instrument line-spread function, and a fixed 0.7 Å He I width, so those numbers would shift for other stars or instrumental profiles, even though the existence of the bias does not depend on them.

Editorial extensions

If this is right

  • Low-resolution He I retrievals should be computed from convolved flux spectra (Eq. 1) rather than by convolving the absorption profile; otherwise signals are underestimated for stars with deep narrow stellar lines.
  • The shortcut remains acceptable at high resolution (NIRPS-class) and for broadband molecular features, so the correction is needed mainly for narrow lines at JWST-class resolution.
  • NIRPS and NIRISS have nearly identical He I detection thresholds, while NIRSpec/G140H detects fainter targets; JWST does not broadly outperform ground-based HR for constraining temperature and mass-loss.
  • Low-resolution space-based data lose the velocity information needed to infer outflow dynamics near the planet, but their long, finely sampled time coverage makes them the primary tool for measuring outflow spatial extent and for establishing reliable baselines.
  • Choosing a baseline that already contains extended-outflow absorption biases the entire absorption time series; simultaneous space-based coverage is the practical remedy.

Reading between the lines

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

  • Published low-resolution He I retrievals that directly convolved the absorption profile may be systematically low for stars with deep narrow lines; re-running them through Eq. 1 could revise reported amplitudes and mass-loss rates without new observations (editorial inference).
  • The analytic $\kappa$ factor could serve as a fast archival correction: with a stellar model and line-spread function in hand, divide the naively convolved model by $\kappa$ instead of redoing the full forward model (editorial inference).
  • The same ratio-versus-convolution logic should apply to other narrow atomic lines observed at low resolution, so the paper's recommendation likely generalizes beyond He I to future JWST observations of alkali and metal lines (editorial inference).
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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 / 5 minor

Summary. The paper addresses how synthetic transmission spectra should be compared to He I observations. Its central methodological claim is that high-resolution theoretical absorption spectra must not be convolved directly with the instrumental profile; because observed transmission spectra are formed as the ratio of convolved in-transit and out-of-transit stellar fluxes (Eq. 1), direct convolution produces a resolution-dependent bias. The claim is supported by two independent routes: mock EvE observations fitted with the standard p-winds approach (Sects. 3.1 and 3.2) and a closed-form Gaussian model for the bias factor κ (Appendix A.1). The paper then uses the recommended methodology to compare NIRPS, JWST/NIRSpec, and JWST/NIRISS for detection (S/N_EW maps, Fig. 4) and characterization (weighted-distance metric, Fig. 5), concluding that NIRPS and NIRISS have comparable detection thresholds, NIRSpec performs best for faint targets, and HR/LR observations are complementary for dynamics versus outflow extent and baseline reconstruction. It closes with recommendations to use Eq. 1 and to account for baseline contamination (Sect. 5, Eq. 8).

Significance. If the central claim is accepted, this is an important correction for He I escape studies and, more broadly, for low-resolution retrievals of features narrower than the instrumental kernel: models must be evaluated as ratios of convolved fluxes, not by convolving model absorption spectra. The paper deserves credit for demonstrating the bias with both numerical mock observations and an analytic κ expression that agree, for including realistic stellar grids with POLDs and error bars, and for providing the GJ3090 b robustness check in Appendix B. The main weakness is that the quantitative bias magnitude (16%) and the instrument ranking depend on assumptions (a fixed Gaussian He I width of 0.7 Å; a Gaussian LSF) that the manuscript acknowledges but does not quantitatively bound. This does not threaten the existence of the bias, whose origin is mathematical, but it does limit the quantitative conclusions as currently stated.

major comments (3)
  1. [Sect. 3.2 and Sect. 4.1; Appendix B (Figs. B.1, B.2)] The headline 16% bias for WASP-69 (Sect. 3.2) and the detection-threshold maps (Sect. 4.1, Fig. 4) are computed with the He I FWHM fixed at 0.7 Å. Appendix B itself shows that the LR-to-HR amplitude conversion varies by up to a factor of 5 when the FWHM is treated as free (Fig. B.1), and that the maps change when the reference system is GJ3090 b with FWHM ~1.0 Å (Fig. B.2). The statement in Sect. 4.1 that "the conclusions drawn here ... remain valid" is an assertion rather than a demonstrated robustness result. Please provide a sensitivity run of the Sect. 3.2 recovery and of Fig. 4 over the FWHM range 0.5–1.5 Å, and ideally with non-Gaussian He I absorption profiles, reporting the resulting range on the 16% bias and on the relative NIRSpec/NIRISS/NIRPS thresholds.
  2. [Sect. 2.2 (Eq. 1) and Sect. 4.1] All mock observations assume a Gaussian instrument kernel, but the NIRISS versus NIRSpec comparison in Sect. 4.1 is driven by how the stellar Si I line blends with He I at σ_R ≈ 7 Å versus ≈ 2 Å. The real NIRISS/SOSS line spread function is structured and wavelength-dependent rather than a single Gaussian. Because the bias factor κ in Appendix A.1 (Eq. A.5) depends on the kernel shape and on the relative positions of the lines, a non-Gaussian LSF can alter both the bias magnitude and the NIRISS detection thresholds. Please test at least one asymmetric or empirical NIRISS LSF profile, or substantially soften the quantitative instrument-ranking conclusions and state this limitation explicitly.
  3. [Sect. 3.1 (Fig. 1)] The claim that the stronger LR retrieval bias "cannot be explained by POLDs" is supported only qualitatively. The comparison mixes two changes at once: the mock spectrum is generated with EvE's 3D stellar and geometric treatment and with Eq. 1, while the p-winds grid is fitted after direct convolution without the stellar spectrum, as the authors state. To isolate the convolution bias from residual POLD or 3D-model mismatch, I suggest repeating the LR fit with the same mock but with models computed from Eq. 1, as already done in Appendix B. This would make Fig. 1 a controlled test of the central claim.
minor comments (5)
  1. [Appendix A.1] Equations (A.4) and (A.5) contain notation that appears dimensionally inconsistent: Eq. (A.4) writes exp[-σ_i^2(λ-μ_i)^2/2] while the figure captions quote σ in Å, and the convolution amplitude factors in Eq. (A.5) contain σ^2/√(σ^2+σ_t^2) where the standard Gaussian convolution requires σ/√(σ^2+σ_t^2). Please correct these expressions, which are likely typographical but make the analytic derivation hard to follow.
  2. [Sect. 3.1] There is a typo in "We estimate the errros on the number of photons", and the instrument name is written inconsistently as both NIRSpec and NIRSPEC (including in Fig. 1 labels); please standardize.
  3. [Fig. 2] The black spectrum is labeled "No stellar spectrum"; this is the star-biased direct convolution discussed in the text, so consider renaming it "Direct convolution (star-biased)" for clarity.
  4. [Sect. 2.2 (Eq. 3)] The error scaling in Eq. (3) should state explicitly whether the reference S/N for WASP-69 is per single transit or per the coadded three-transit dataset used by Allart et al. (2025a), since the derived J-magnitude scaling depends on this choice.
  5. [Sect. 5 (Eqs. 6–8)] The derivation of the baseline bias assumes normalized continuum fluxes and a homogeneous stellar disk; please state these simplifications explicitly when introducing F⋆ = Σ F_i and Eq. (6).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central convolution-bias claim is derived analytically from the definition of the observed transmission spectrum and is not equivalent to a fitted input.

full rationale

The central claim is a mathematical consequence of Eq. 1, which defines the observed transmission spectrum as the ratio of convolved in-transit and out-of-transit stellar spectra. Appendix A.1 derives the bias factor kappa explicitly from this definition under stated Gaussian assumptions, with no fitted parameters. The 16% bias quoted in Sect. 3.2 is a calibration exercise in which a mock observation with known input (3% Gaussian, 0.7 A FWHM) is fit with the standard star-biased method; the recovered 2.52% is not a prediction defined in terms of its own fitted parameters but a demonstration of the bias. The detection and constraint maps in Sect. 4 are self-consistent simulations with fixed model assumptions; their quantitative conclusions depend on assumptions such as the 0.7 A FWHM and Gaussian LSF, which the paper explicitly acknowledges in the footnote in Sect. 2.1 and in Appendix B. That is a robustness limitation, not circularity. The authors use their own EvE code, but the main analytic result is verified independently in Appendix A.1, and the broadband molecular bias is cross-checked with SCARLET and a second stellar spectrum; no load-bearing self-citation chain is identifiable. No claim reduces by construction to its own inputs.

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

The central bias claim introduces no fitted parameters: it follows from Eq. 1 versus direct convolution and is verified analytically in Appendix A.1. The quantitative demonstrations and instrument comparisons, however, rest on hand-chosen model parameters (Sect. 2.1) and an assumed line width (0.7 Å) calibrated to reproduce the observed WASP-69 b signal or set to 'typical' values. No new physical entities are introduced; the four outflow scenarios (W, M, S, STR) are simplified parameterizations of previously published simulations.

free parameters (5)
  • Thermosphere temperature T = 9750 K
    Chosen to roughly reproduce the observed WASP-69 b He I amplitude (Sect. 2.1). Affects the width and amplitude of the absorption and thus the instrument comparison, but not the qualitative bias result.
  • Mass-loss rate dM/dt = 10^12 g/s
    Same as temperature: p-winds input chosen to reproduce the observed signal amplitude (Sect. 2.1).
  • H/He ratio = 90:10
    Fixed assumption (Sect. 2.1); a footnote states that the general conclusions do not depend on this choice.
  • He I absorption FWHM = 0.7 Å
    Assumed in the detection maps (Sect. 4.1) and in the bias quantification (Sect. 3.2). Appendix B shows the predicted HR amplitude from LR can vary by a factor of 5 with FWHM.
  • Outflow tail parameters (escape velocity and angle) = varied per scenario (4 cases)
    Parameterize the four outflow geometries in Sect. 4.3, chosen to approximate hydrodynamical simulations by MacLeod & Oklopčić (2022) and MacLeod et al. (2025).
assumptions (4)
  • domain assumption The instrument line spread function is Gaussian with width set by the resolving power R.
    Used throughout Sect. 2.2 to generate mock observations. Real LSFs (echelle, JWST grisms) deviate from Gaussian, which would modify the quantitative bias and detection thresholds.
  • standard math The observed transmission spectrum is exactly the ratio of convolved in-transit to out-of-transit fluxes (Eq. 1).
    This is the data-analysis convention the paper advocates. If real pipelines use a different reference construction (e.g., telluric-corrected masters), the bias formalism shifts.
  • domain assumption p-winds thermospheric profiles and the EvE 3D projection correctly represent the atmospheric structure of WASP-69 b.
    Sect. 2.1. The instrument comparison results depend on this atmospheric model, though the bias demonstration does not require it.
  • domain assumption Photon noise is Poissonian and the error scaling of Eq. 3 correctly propagates S/N.
    Sect. 2.2. NIRPS errors are calibrated on real GTO data and JWST errors via Pandexo, but the scaling assumes uncorrelated noise and ignores systematics.

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Cite this review

Pith. "Pith review of Toward a unified framework for helium observations and interpretation of atmospheric escape." pith.science (2026). https://pith.science/paper/QAVMN5YI

@misc{pith2026260810707,
  author       = {Pith},
  title        = {Pith review of: Toward a unified framework for helium observations and interpretation of atmospheric escape},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QAVMN5YI}},
  note         = {Machine review of arXiv:2608.10707}
}
read the original abstract

Atmospheric escape is considered a key process in shaping exoplanet demographics and evolution. The near-infrared metastable helium triplet (HeI at ~10833{\AA}) is one of the most powerful tracers of hydrodynamical outflows. In recent years, a large variety of instruments have been used to detect and characterize expanding upper atmospheres. High-resolution (HR) spectrographs, which can spectrally resolve the lines of the triplet, have been mostly used to probe the dynamics of atmospheric escape. More recently, JWST (low-resolution, LR) detected extended outflows that were missed by previous ground-based observations due to night-length constraints. Since the observed transmission spectrum is computed from the ratio of stellar spectra degraded by instrumental convolution, we demonstrate that directly convolving a theoretical transmission spectrum with the instrument's profile leads to inference biases as resolution decreases. This conclusion is particularly important for atmospheric escape studies but also holds in the more general context of LR atmospheric retrievals. Following the proper comparison methodology, we then investigated the complementarity of HR and LR observations for atmospheric escape measurements, comparing the capability of three instruments (NIRPS, JWST/NIRSpec, and JWST/NIRISS) to detect and characterize outflows. We find that NIRPS and JWST/NIRISS are mostly sensitive to the same helium signatures, while JWST/NIRSpec improves the detection limit of excess absorption for faint targets. Studying different outflow configurations, we show that LR space-based measurements cannot be used to infer the dynamics of the upper atmosphere close to the planet, and are best employed to measure the spatial extent of outflowing tails. We thus highlight the complementarity between HR and LR observations of atmospheric escape, the latter improving baseline reconstruction.

Figures

Figures reproduced from arXiv: 2608.10707 by the authors.

Figure 1
Figure 1. Left panels: synthetic averaged absorption spectra for NIRPS (top) and NIRSpec (bottom) generated with [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Absorption spectra at NIRSpec resolution. The blue and [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Detection maps of He i absorption for NIRISS/SOSS, NIRSpec/G140H, and NIRPS. The black lines denotes the 3σ limit on S/NEW, and we highlighted 3 specific cases with letters for which the observed absorption spectra are displayed in Fig. D.1. The dashed line reported on each JWST maps is the NIRPS 3σ threshold for comparison. On the NIRPS map we also show with the green, blue, and red arrows the J magnitude limit for… view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Atmospheric escape fitting constraints maps for NIRISS [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Simulated average absorption spectra in the planetary rest-frame for the four di [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Simulated excess absorption light curves in the stellar rest-frame for the four di [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Synthetic high-resolution average absorption spectra computed using various stellar reference spectra as observed with [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

Discussion (0). Continue with ORCID to comment.

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Reviewed August 12, 2026 · model on record in the stance chip above.