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Time-resolved p-mode oscillations for subgiant HD 142091 with NEID at WIYN

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper shows that p-mode oscillations in the subgiant HD 142091 shift the averaged spectral line profile almost purely as a rigid Doppler shift, with shape-driven changes only a small residual.

desk verdict Genuinely new empirical result on p-mode line-profile behavior, with a solid shift-driven core and a plausible but GP-dependent amplitude gradient that needs a null simulation before the 10%/25% numbers are taken at face value. read the letter →

arxiv 2506.18989 v1 pith:U7XP6HOZ submitted 2025-06-23 astro-ph.SR astro-ph.EP

Jacob K. Luhn , Paul Robertson , Samuel Halverson , Arvind F. Gupta , Jared C. Siegel , Jason T. Wright , Eric B. Ford , Suvrath Mahadevan
show 17 more authors
Timothy R. Bedding Jaime A. Alvarado-Montes Chad F. Bender Jiayin Dong Fred Hearty Sarah E. Logsdon Andrew Monson Michael W. McElwain Joe P. Ninan Jayadev Rajagopal Arpita Roy Christian Schwab Gudmundur Stefansson Daniel J. Stevens Ryan C. Terrien Sharon Xuesong Wang Jinglin Zhao
This is my paper · ORCID
classification astro-ph.SRastro-ph.EP
keywords p-modeoscillationsradialvelocitiescross-correlationfunctionCCFbisectorlinedepthstellargranulationGaussianprocessshape-vs-shifttechnique
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

Low-degree p-mode oscillations, the acoustic pulsations that dominate short-timescale stellar noise in radial-velocity planet searches, have been assumed to distort spectral line shapes in ways that can be separated from the pure Doppler shift of a planet. This paper tests that assumption on a resolved, single-night time series of p-mode oscillations in the subgiant HD 142091, taken at 2-minute cadence with the NEID spectrograph. It finds that the oscillations appear primarily as pure translational Doppler shifts of the average line profile, measured by the cross-correlation function (CCF), with shape-driven variations only a higher-order effect. It further measures a height-dependent amplitude gradient: oscillation amplitudes are about 10% larger near the CCF core and 25% smaller near the wings, and deeper spectral lines show larger amplitudes. If true, this means p-mode oscillations will not lend themselves to removal by line-shape diagnostics, and existing exposure-time binning remains the appropriate mitigation for planet surveys.

What carries the argument

The load-bearing object is the cross-correlation function (CCF), the average stellar line profile built by cross-correlating each spectrum with a template mask. Its shape is probed two ways: seven horizontal flux slices yield bisector-velocity time series, and a line-by-line toolkit groups about 2662 lines into depth and wavelength bins of equal radial-velocity weight. Each time series is modeled with a two-component Gaussian process whose granulation and oscillation kernels and hyperparameters are fixed from prior work. Equation (1) fits every slice's oscillation as $A_i \mu^{\rm osc}_{\rm RV}(t)$, an amplitude-scaled version of the bulk oscillation component after subtracting the GP granulation component; the fitted $A_i$ values across CCF slices and across line-depth bins are what expose the 10%/25% amplitude gradient. SCALPELS is the negative control: it is shift-invariant by construction, and its failure to recover the known shape changes, with an injection-recovery test showing detection would require CCF noise near 24 ppm versus the observed 130 ppm, brackets how small the shape-driven component is.

What would settle it

A decisive test is a multi-night campaign on HD 142091 (or a similar subgiant) that resolves individual p-mode frequencies in the Fourier domain and measures the CCF core-to-wing amplitude ratio mode by mode; if the roughly 10% larger and 25% smaller gradient does not reproduce for resolved modes, the single-night decomposition has misattributed granulation power to the oscillations.

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Extended reading notes

Core claim

The central result is that, in a 6.5-hour NEID time series of HD 142091, the p-mode oscillations produce CCF residuals that are almost exactly what a pure Doppler shift of a template CCF would produce: the median ratio of residual RMS inside versus outside ±10 km/s is 1.069. When the time series is split into seven CCF flux slices, each slice's bisector velocity oscillation is well described as a constant scale factor times the bulk radial-velocity oscillation component after GP granulation subtraction. The scale factors are not all unity: the deepest (core) slice is about 10% larger than the bulk, the shallowest (wing) slice is about 25% smaller, a trend for which a quadratic is preferred at 7.2 sigma. A line-by-line analysis reproduces the same pattern, with deeper lines having larger oscillation amplitudes, while no phase lag, no change of timescale, and no wavelength dependence beyond line-depth differences are found. The paper reads the amplitude gradient as the atmospheric-height dependence of the oscillation velocity field, with the caveat that CCF slice position and line depth are only proxies for formation height.

Load-bearing premise

The result stands on the assumption that the two-component Gaussian-process decomposition, with kernels from earlier work, cleanly separates oscillations from granulation in a single roughly 6.5-hour night, so that the fitted $A_i$ factors measure true oscillation amplitudes rather than granulation leakage that varies across CCF slices or line depths.

Editorial extensions

If this is right

  • Exposure-time binning over an integer number of p-mode cycles, the standard mitigation for Sun-like radial-velocity surveys, should continue to work; shape-vs-shift algorithms will not recover additional p-mode signal from that observing strategy.
  • Radial-velocity measurements that weight deep line cores will see slightly larger p-mode oscillations than bulk RVs, while wing-weighted or shallow-line RVs will see smaller amplitudes, a systematic any sub-meter-per-second planet search should budget for.
  • Because low-degree modes dominate on this night, p-modes cannot be assumed to always produce CCF asymmetries; the instantaneous mode content decides whether shape-driven distortions are present.
  • Line-depth-dependent RV binning cannot suppress p-mode oscillations to near zero, since no collection of lines reduces oscillation RMS substantially; depth selection is therefore not a viable p-mode mitigation.

Reading between the lines

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

  • The measured core-to-wing amplitude gradient predicts that a Sun-as-a-star dataset, which averages over many more modes, should show a similar slice-dependent bisector amplitude; this is directly checkable with existing solar extreme-precision radial-velocity time series.
  • If the gradient is confirmed with resolved modes, bisector-slice amplitudes become a time-domain probe of line formation height that could complement spectral synthesis, potentially mapping atmospheric depth dependence for many stars at once.
  • The paper's negative SCALPELS result suggests that other shape-based activity indicators, such as bisector span or related metrics, will carry p-mode oscillations at reduced amplitude when weighted toward line wings, which could subtly affect activity-cycle measurements in some surveys.
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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 reports time-resolved NEID observations of p-mode oscillations in the subgiant HD 142091, taken on a single night at 2-minute cadence with 181 usable spectra. The authors analyze CCF residuals, CCF bisector velocities in seven flux slices, a SCALPELS decomposition, and line-by-line RVs grouped by line depth and wavelength. Their central claim is that the observed p-modes manifest primarily as pure Doppler shifts of the average line profile, with a higher-order amplitude gradient across the CCF: oscillation amplitudes are about 10% larger near the CCF core and 25% smaller in the wings, interpreted as larger oscillation velocities higher in the stellar atmosphere. A similar depth dependence is found in a line-by-line analysis, and no significant chromatic dependence remains after accounting for line-depth differences. The paper also shows that SCALPELS cannot detect the small shape-driven component at the achieved CCF noise level, and it validates this with an injection-recovery test.

Significance. If correct, the paper provides a rare time-domain, epoch-resolved characterization of how p-mode oscillations distort stellar line profiles, with direct implications for shape-vs-shift mitigation techniques in extreme-precision RV surveys. The raw CCF residual maps in Figures 2 and 3 give robust visual evidence that the dominant response is a translational shift, and the SCALPELS injection test is a well-designed, quantitative explanation of the non-detection. The paper is also commendably transparent about the chromatic analysis, showing that the marginal wavelength trend disappears once line depth is restricted. The main quantitative result that motivates the atmospheric-height interpretation — the 10%/25% amplitude gradient — is, however, derived through the authors' GP decomposition, and that decomposition's reliability in the gradient regime is not established by the validation presented. This makes the paper's central new physical conclusion conditional on a specific modeling assumption that needs further testing.

major comments (3)
  1. [4.1.2 and Appendix A] The amplitude gradient A_i in Eq. (1) is obtained by regressing each CCF-slice bisector time series on the bulk-RV oscillation posterior mean, where both the slice-wise granulation subtraction and the bulk oscillation mean come from a two-component GP with hyperparameters fixed from Luhn et al. (2023). Appendix A validates the decomposition on time series generated from that same GP model with a single bulk amplitude; it does not test the regime that matters for the gradient, namely slices whose true oscillation amplitudes differ from the fixed kernel amplitude, combined with per-slice granulation and a baseline of only about five oscillation cycles. The paper itself states that the granulation kernel has appreciable power inside the oscillation envelope and that granulation variability on oscillation timescales is partly attributed to the oscillation component. A slice-dependent leakage of granulation power into the oscillation posterior could therefore produce a spurious A_i trend. I request a null simulation: generate per-slice time series with a constant A_i (no gradient) plus per-slice granulation and realistic noise, run the same pipeline, and show the recovered A_i has no systematic gradient. Alternatively, a joint GP fit in which each slice's oscillation amplitude is a free parameter would avoid the two-step decomposition and directly test the gradient.
  2. [4.2.1] The line-depth amplitude trend uses the same two-step GP decomposition. The RMS trend shown in the bottom-right panel of Figure 6 is explicitly admitted to be not significant, and the significant 6.5-sigma amplitude-scale-factor trend in Figure 7 is obtained after subtracting the granulation posterior and regressing on the bulk oscillation component. Consequently, the same concern as in my first comment applies: if granulation leakage varies with line depth, the recovered scale factors could show a spurious trend. The paper should either validate the decomposition in the presence of a constant per-depth oscillation amplitude through simulation, or present an independent estimator of the per-depth oscillation amplitude (e.g., a direct fit of a shared oscillation signal with per-depth scale factors).
  3. [4.1.2] The sentence stating that 'our findings are consistent through either method' (with and without granulation subtraction) is presented as a robustness check, but it does not address the null hypothesis of no gradient, because the simpler regression still uses the same bulk-RV oscillation component and the same per-slice time series. This sentence should be clarified so that it is not read as evidence against decomposition-induced bias.
minor comments (5)
  1. [Header] The header lists 'Received Oct. 31, 2024; Accepted May 15, 2024', which is chronologically inconsistent; please verify the dates.
  2. [Section 2 and Abstract] The estimated p-mode period is given as '84 min.' in Section 2 but '80 min.' in the Abstract; the values should be reconciled.
  3. [Section 4.1.2] The description of the CCF flux bands ('including a gap between bands of 0.025') leaves ambiguous whether the gap is in units of flux or in velocity; please specify the units and the exact bin edges.
  4. [Section 4.2.1] There is a typo: 'the shallowest bin has has a slight negative trend' should read 'has a slight negative trend'.
  5. [References] The reference list begins with an entry '1997, ESA Special Publication...' with no author; as formatted, it will be difficult for readers to locate. Please check the journal's reference style for this item.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular reduction; the amplitude gradient is an empirical fit to independent per-slice observables, with only minor inherited model dependence from the authors' own GP kernels.

full rationale

The central claim that HD 142091's p-mode oscillations manifest primarily as Doppler shifts is supported by direct comparisons of observed CCF residuals to Doppler-shifted template CCFs (Figures 2-3) and by the SCALPELS injection-recovery test (Section 4.1.3); neither of these depends on the authors' GP model. The quantitative 10%/25% trend is obtained by regressing each CCF-slice or depth-bin time series against the bulk-RV oscillation component in Eq. (1), with the scale factors A_i as free parameters. The GP model from Luhn et al. (2023) and Gupta et al. (2022) supplies a common regression basis, but it does not contain or force the gradient, because the A_i are fit separately and independently for each slice. No equation in the paper reduces the conclusion to an input by construction. Appendix A is explicit that the granulation kernel has appreciable power inside the oscillation envelope and that simulations are drawn from the same model; this is a limitation on validation robustness, not a circular step. The score of 2 reflects the minor self-citation chain used to fix the GP hyperparameters and decomposition, but the central empirical finding remains self-contained and would stand or fall on the data and the fitted A_i values.

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

The central measured quantities are amplitude scale factors fitted to a common oscillation time series; no new physical entity is introduced. The physical interpretation relies on priors about GP decomposition and line formation height rather than on a derivation.

free parameters (4)
  • Amplitude scale factor A_i for each CCF flux bin (7 bins) = Fig. 4c: about 1.10 at CCF core, 0.75 at wings
    Fitted via MCMC in Eq. (1); the trend across bins is the central measured result.
  • Amplitude scale factor per line-depth bin (9 bins) = Fig. 7: increasing with line depth; trend reported at 6.5 sigma
    Fit of each depth bin's oscillation component to the bulk RV oscillation component.
  • Amplitude scale factor per wavelength bin (9 bins) = Fig. 9: weakly decreasing trend that vanishes when restricted to depth < 0.3
    Fit used to test chromatic dependence of p-mode amplitudes.
  • Two-component GP kernel hyperparameters (oscillation and granulation amplitudes/timescales) = Fixed from Luhn et al. (2023), values not restated in this preprint
    The paper conditions the time series on these fixed values; the decomposition and all amplitude trends depend on them.
assumptions (5)
  • domain assumption The two-component GP model with kernels from Luhn et al. (2023) and decomposition from Gupta et al. (2022) separates granulation and oscillation in a single 6.5-hour time series with gaps.
    Used in Sections 3 and 4 to define mu_osc_RV and to remove granulation; validated only on simulated data in Appendix A, which concedes granulation power at oscillation timescales leaks into the oscillation component.
  • domain assumption CCF flux level and continuum-normalized line depth are monotonic proxies for formation height in the stellar atmosphere.
    Authors interpret larger amplitudes near the CCF core and in deeper lines as larger velocities higher in the atmosphere, but explicitly state in Section 4.1.2 that CCF slices are not one-to-one with atmospheric height and recommend spectral synthesis follow-up.
  • domain assumption The observed p-mode oscillations are dominated by low angular degree modes (l=0,1,2) that produce pure shifts, per Telting and Schrijvers (1997).
    Used in Section 4.1.1 to explain why CCF residuals resemble pure Doppler shifts rather than shape changes.
  • domain assumption The K2 ESPRESSO mask and the NEID pipeline produce a CCF that faithfully represents the average stellar line profile.
    The entire CCF morphology and bisector analysis assumes the pipeline CCF is an unbiased tracer of the average line profile.
  • domain assumption Stellar parameters of HD 142091 (R=4.68 Rsun, Teff=4840 K, etc.) from Teng et al. (2023) are accurate enough that the predicted p-mode period near 84 minutes matches the observed oscillations.
    Used to design the observing cadence; the fact that oscillations are clearly resolved partially validates this assumption.

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Pith. "Pith review of Time-resolved p-mode oscillations for subgiant HD 142091 with NEID at WIYN." pith.science (2026). https://pith.science/paper/U7XP6HOZ

@misc{pith2026250618989,
  author       = {Pith},
  title        = {Pith review of: Time-resolved p-mode oscillations for subgiant HD 142091 with NEID at WIYN},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U7XP6HOZ}},
  note         = {Machine review of arXiv:2506.18989}
}
abstract

Detections of Earth-analog planets in radial velocity observations are limited by stellar astrophysical variability occurring on a variety of timescales. Current state-of-the-art methods to disentangle potential planet signals from intrinsic stellar signals assume that stellar signals introduce asymmetries to the line profiles that can therefore be separated from the pure translational Doppler shifts of planets. Here, we examine this assumption using a time series of resolved stellar p-mode oscillations in HD 142091 ($\kappa$ CrB), as observed on a single night with the NEID spectrograph at 2-minute cadence and with 25 cm/s precision. As an evolved subgiant star, this target has p-mode oscillations that are larger in amplitude (4-8 m/s) and occur on longer timescales (80 min.) than those of typical Sun-like stars of RV surveys, magnifying their corresponding effects on the stellar spectral profile. We show that for HD 142091, p-mode oscillations manifest primarily as pure Doppler shifts in the average line profile -- measured by the cross-correlation function (CCF) -- with "shape-driven" CCF variations as a higher-order effect. Specifically, we find that the amplitude of the shift varies across the CCF bisector, with 10% larger oscillation amplitudes closer to the core of the CCF, and 25% smaller oscillation amplitudes for bisector velocities derived near the wings; we attribute this trend to larger oscillation velocities higher in the stellar atmosphere. Using a line-by-line analysis, we verify that a similar trend is seen as a function of average line depth, with deeper lines showing larger oscillation amplitudes. Finally, we find no evidence that p-mode oscillations have a chromatic dependence across the NEID bandpass beyond that due to intrinsic line depth differences across the spectrum.

Figures

Figures reproduced from arXiv: 2506.18989 by the authors.

Figure 1
Figure 1. NEID RV time series of HD 142091 on April 25, 2022, in which we have resolved p-mode oscillations. Gaps in the time series result from a combination of scheduled in￾termediate calibrations, acquisition errors, and a VNC crash during the night, as described in the text. The blue line and ribbons shows a GP fit to the time series and the 1-, 2-, and 3-σ uncertainties using a two-component GP model that includes granul… view at source ↗
Figure 3
Figure 3. NEID time series with CCF residuals in 7 RV bins. The time series is color coded by RV for additional clarity. The insets show the mean CCF residual (solid, colored lines) compared to pure Doppler shifts (black and white dashed lines) within each velocity band. The CCF residuals closely match those of pure Doppler shifts, indicating primarily shift-driven, rather than shape-driven RV variations. median bisector velo… view at source ↗
Figure 4
Figure 4. Top left (a): Schematic of CCF slices used to compute the bisector velocities in Section 4.1.2. The CCF bisector is shown, magnified by a factor of 25. Bottom (b): Bisector velocity time series for each of the 7 CCF slices shown in panel (a). Points are color coded according to the mean CCF flux level in each slice as in panel (a). The structure is similar across all CCF slices, with a noticeable reduction in amplit… view at source ↗
Figures from the paper (7 more)
Figure 5
Figure 5. Figure 5: Results of a SCALPELS analysis (Collier Cameron et al. 2021) to disentangle “shape-driven” from “shift-driven” RV variations. The shape-driven velocities are consistent with a noise-dominated non-detection. pare the measured amplitudes to predictions from the￾oretical …
Figure 6
Figure 6. Figure 6: Analysis of line depth dependence of p-mode oscillations for HD 142091. Top left: Cumulative distribution function for the relative RV weight of each line as a function of line depth, with depth bins chosen to maintain constant total RV weight for each bin. Top right: …
Figure 7
Figure 7. Figure 7: Amplitude of oscillations (relative to the oscilla￾tion component of the bulk RV time series) for each depth bin time series in [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Similar to [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Top panel: Amplitude of oscillations (relative to the oscillation component of the bulk RV time series) for each wavelength bin time series in [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Five randomly selected representative samples demonstrating the GP decomposition procedure. Left columns show the recovered oscillation signal (dark bold colors) compared to the true synthesized signal (light faded colors) for each sample. The panels in the right colu…
Figure 11
Figure 11. Figure 11: Distribution of summary statistics for the GP decomposition analysis. For the decomposed oscillation (left) and granulation (right) signals, the top two rows show the distributions of the offset (top row) and slope (middle row) of a linear fit to the true synthesized …

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