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Homogeneous planet masses I: Reanalysis of archival HARPS radial velocities

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A homogeneous reanalysis of 6428 archival HARPS radial velocities for 87 small exoplanets shows that the choice of eccentricity prior and of Gaussian-process activity model can substantially change the derived radial-velocity amplitude…

desk verdict A useful homogeneous HARPS reanalysis with a solid 12-model comparison, but the adopted best-model K values rest on an unvalidated GP activity separation and a heuristic selection rule. read the letter →

arxiv 2411.12723 v1 pith:3FJ5DNVR submitted 2024-11-19 astro-ph.EP astro-ph.IMastro-ph.SR

H. L. M. Osborne , L. D. Nielsen , V. Van Eylen , O. Barragán This is my paper · ORCID
classification astro-ph.EPastro-ph.IMastro-ph.SR
keywords exoplanetmassesradialvelocitiesHARPSstellaractivityGaussianprocessesorbitaleccentricitymass-radiusrelationhomogeneousanalysis
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 argues that the masses of small exoplanets are not as settled as the literature suggests, because nearly every published mass comes from a different modelling recipe. To make the comparison fair, the authors re-fit all publicly available HARPS radial velocities for 87 small planets around 44 stars—6428 measurements in total—with one pipeline and 12 explicitly varied models. They find that modelling the orbit as eccentric with a wide uniform prior can push the RV amplitude up, in some cases by a factor of about three, while a physically motivated beta-distribution prior on eccentricity keeps results close to the circular-orbit case. They also find that adding a Gaussian process to absorb stellar activity changes the amplitude, and that the most consistent results come from multi-dimensional GPs that also fit activity indicators rather than a GP on the RVs alone. If this is right, the community gets the first large homogeneous small-planet RV amplitude catalogue, and demographics conclusions drawn from heterogeneous mass lists will need to be re-examined.

What carries the argument

The argument is carried by a deliberately rigid comparison grid: every system is fitted with the same twelve models, so model choice is the only free variable. The central components are the quasi-periodic Gaussian Process kernel, which models stellar activity as a periodic, evolving signal with a period prior set from the stellar rotation period; the $\sqrt{e}\sin\omega_*$ and $\sqrt{e}\cos\omega_*$ parameterisation of eccentricity, which avoids truncation at zero; and the $\beta$-distribution prior on eccentricity derived from transit populations, which prevents the unphysically large eccentricities that a uniform prior allows. The grid spans no-GP, 1D, 2D and 3D GPs, circular/eccentric/$\beta$-distribution orbits, and no/linear/quadratic long-term trends, yielding the $K$ comparisons that ground every conclusion.

What would settle it

Compare the homogeneous RV amplitudes for a subset of the 87 planets against independent mass constraints from transit-timing variations or joint RV-plus-photometry fits: if the differences trace the presence of the GP or the eccentricity prior, rather than random scatter, the separation assumption is falsified. A sharper test is an injection-recovery experiment on the same HARPS time series with synthetic planets of known amplitude, checking whether each of the twelve models recovers the injected $K$ without bias.

Watch

Extended reading notes

Core claim

The central claim is that, for the same archival data, the extracted RV amplitude $K$—and hence the planet mass $m\sin i$—depends systematically on how the fit is configured. Running twelve models that vary the treatment of eccentricity (fixed circular, uniform prior, $\beta$-distribution prior), the addition of long-term trends, and the dimensionality of a quasi-periodic Gaussian process used to mitigate stellar activity, the authors show that a uniform prior on eccentricity produces inflated, spuriously high eccentricities and higher $K$ values, whereas a $\beta$-distribution prior yields amplitudes consistent with circular fits. Adding a 1D GP to the RVs alone gives the least consistent amplitudes, while 2D and 3D GPs that jointly fit the FWHM and bisector-span activity indicators behave more consistently. Long-term linear or quadratic trends have little effect. The authors release the $K$ amplitude for every planet under all twelve models and define a 'best' model per target, and they recommend against unconstrained eccentric orbits and in favour of activity-anchored multi-dimensional GPs for large surveys.

Load-bearing premise

The catalogue's amplitudes assume that the quasi-periodic Gaussian process, with rotation-period priors fixed by a single activity-rotation relation, cleanly separates stellar activity from the planetary signal for all 44 stars; the model failures on TOI-269, TOI-4399 and HD 3167 show this separation is not guaranteed.

Editorial extensions

If this is right

  • The public catalogue of RV amplitudes under all twelve models lets other groups test how their own modelling choices move planet masses, without redoing the data reduction.
  • Demographics studies that mix masses from different pipelines carry a model-dependent scatter; a homogeneous sample should tighten the observed mass-radius relation if the systematics are real.
  • The recommendation to use a beta-distribution prior on eccentricity, or to fix circular orbits, implies that many published eccentric single-planet fits for small planets may be fitting noise.
  • Multi-dimensional GPs fitted to RVs plus activity indicators should become the default for active stars, since the 1D GP is the least consistent with the other models.
  • The three systems where no model produced a good fit (TOI-269, TOI-4399, HD 3167) show that some published masses rest on data or modelling beyond what a homogeneous pipeline can reproduce.

Reading between the lines

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

  • If the eccentricity-prior effect is general, then published mass-radius relations assembled from heterogeneous eccentric fits may be biased toward higher masses for low-mass planets; a test is to re-fit a literature sample with a beta-distribution prior and compare the mass-radius scatter.
  • The GP period priors derived from a single activity-rotation relation are a likely weak point: for stars where measured rotation periods are available, replacing that prior with direct rotation-period measurements should reduce activity-signal leakage, and this can be tested on the same data.
  • The same twelve-model grid could be applied to a control sample of planets with masses independently measured by transit-timing variations; agreement would validate the homogeneous catalogue, while disagreement would localise which model component is biased.
  • Because the paper uses only HARPS data, the catalogue cannot test instrument systematics; combining with data from other precision spectrographs for a subset would show whether the homogeneous results are instrument-independent.
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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

4 major / 6 minor

Summary. The paper re-analyzes public HARPS radial velocities for 87 small exoplanets (R < 4 R⊕; the abstract also says 85) using the Pyaneti toolkit. For each target it runs 12 models that vary the Gaussian Process dimension (none, 1D, 2D, 3D), the eccentricity treatment (circular, uniform prior, beta prior), and the inclusion of long-term trends. It compares models with AIC/BIC, defines a 'best' model via a hybrid rule, and provides tables of K, eccentricity, period, and m sin i for the adopted models, with the full model grid available online. The authors report that the eccentricity prior can change K by factors up to ~3 in some cases, that the 1D GP gives systematically different K from the 2D/3D GPs, and that long-term trends have a small average effect. They present this as a homogeneous K catalog for demographics studies and give best-practice recommendations.

Significance. If the catalog K values are unbiased, this would be a valuable resource: the first large homogeneous small-planet RV-amplitude catalog from HARPS, directly useful for mass-radius demographics. The paper's strengths are its transparent MCMC setup, explicit prior tables, a 12-model grid applied uniformly, public data products, and honest caveats about archival data. The qualitative conclusions about model-choice sensitivity (eccentricity prior, GP dimension, trend inclusion) appear robust because they are based on differences between models applied to the same data. However, the absolute K values intended for demographics use rest on an unvalidated GP activity-separation assumption, and the sample definition has internal inconsistencies that must be resolved before the catalog can be used as stated.

major comments (4)
  1. [§6.4, Table B.1] The final adopted K values are not the AIC-selected models. The rule in §6.4 selects the lowest-AIC model among the RV-only models (a–f), then substitutes model n (3D GP + beta eccentricity) whenever the 1D GP wins and model e (no GP + beta eccentricity) whenever a no-GP model wins. Models e and n are never directly compared with the alternatives on the same likelihood, and model n uses FWHM/BIS as additional data dimensions so AIC is not comparable. No injection-recovery or cross-validation is presented to show that this substitution recovers unbiased K. The comparison to NASA Archive values in Fig. 5 is not a validation because those values are heterogeneous, use different data, and are not ground truth. Since Table B.1 is the primary catalog deliverable, this selection rule needs an explicit validation test, or the AIC-selected model should be listed as primary and the substituted model clearly labeled as a secondary choice.
  2. [§5, §6] The GP activity separation is assumed rather than tested. The quasi-periodic kernel in Eq. (1) is applied to all 44 stars with P_GP priors derived from Mamajek & Hillenbrand (2008), but the manuscript contains no injection-recovery tests, no comparison with photometric rotation periods, and no leave-one-out checks to show that the GP does not absorb part of the planet signal or leave activity unmodelled. Section 6 itself shows the framework is fragile: TOI-269, TOI-4399, and HD 3167 could not be modelled well. For a catalog intended for demographic studies, at least a representative subsample of stars should be tested by injecting planetary signals of known K and comparing recovered K; otherwise the absolute K values in Table B.1 are not demonstrably unbiased.
  3. [§6.4] The model-comparison counts are internally inconsistent. The text states that the lowest-AIC RV-only model is f for 78 planets and g for 11, which sums to 89, exceeding the stated sample of 87 small planets. The 3D GP counts are 80 (k) + 18 (n) + 15 (m, mislabelled as n in the text) = 113, equal to the total number of planets orbiting the target stars including the 26 non-small planets that §2 says are excluded from the model comparison. The counts must be reconciled with the actual sample and the exclusion statement clarified, because these counts describe the basis for the best-model selection.
  4. [§2, §6, Table C.1, Table B.1] The sample definition is not reproducible from the tables. Section 2 says the final sample is 87 small planets orbiting 44 stars, but Table C.1 lists 49 stars and omits TOI-4399 (discussed in §6) while including stars with no entry in Table B.1 (for example, HD 18599, HD 15337, HIP 94235). Furthermore, §6 states that removing three targets leaves 83 small planets, which is only consistent if those three stars contribute four small planets total; this is not stated. The tables need to be made consistent and the exact per-star planet counts presented.
minor comments (6)
  1. [§6.4] The phrase '15 the uniform eccentric model (n)' should refer to model m, not n; model n is the 3D GP with beta-distributed eccentricity.
  2. [§6.5] The worked example alternates between 'TIC 9870809' and 'TIC 98720809'; the latter matches Table C.1 and should be used consistently.
  3. [Table 2, §5] The λ_e prior is listed as U[1,160] for all stars, but the text says the maximum λ_e is twice the stellar-type-dependent P_GP maximum (20–60 days depending on temperature). Clarify whether the prior is global or per-star, and reconcile the numbers.
  4. [§6.4] The statement that the 3D GP model 'will always have a lower value of AIC compared to the 2D GP case because it has more data points' is incorrect and contradicts the same paragraph's caveat that AIC cannot be compared across different data sets; AIC depends on the likelihood, not simply on the number of points.
  5. [Abstract, §2] The number of small planets is given as 85 in one version of the abstract and 87 in the body; please harmonize all sample-size statements.
  6. [Appendix A.1, §3] The manual RV cuts for TIC 173103335, TIC 220479565, TIC 260004324, and TIC 56815340 are described only in the appendix; please state in the main text how many points were removed per target and confirm that the exact cut thresholds do not affect the final K values.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the K amplitudes are direct MCMC fits to public HARPS data, and the model comparisons are empirical differences between fits rather than predictions derived from the fits.

full rationale

The paper's central output is a set of RV amplitudes obtained by fitting twelve Keplerian/GP models to archival HARPS data. Each K value is a free parameter sampled by MCMC from public data, with priors on period and transit time taken from the NASA Exoplanet Archive and eccentricity either fixed, uniform, or drawn from a beta distribution. There is no step in which a fitted parameter is renamed as a prediction, and no quantity used in the analysis is defined in terms of the reported K values. The model-comparison findings (e.g., that a uniform eccentricity prior inflates K, or that 1D GPs behave differently from 2D/3D GPs) are descriptive comparisons of independent MCMC fits, not conclusions forced by the choice of model. Self-citations to Pyaneti and to Barragán et al. provide the fitting toolkit and the multidimensional-GP formalism, but these are code/methodology citations rather than load-bearing uniqueness claims; the paper tests alternative priors and GP dimensionalities. The beta eccentricity prior of Van Eylen et al. (2019) is an external empirical prior from transit observations and is explicitly compared against circular and uniform-eccentricity models. The paper's own caveats about GP prior sensitivity, unvalidated activity separation, and the heuristic best-model selection in Section 6.4 are robustness concerns, not circular reasoning. No reduction of the derived amplitudes to the model inputs or to a self-citation chain is present.

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

The catalog rests on standard RV modeling assumptions and several external priors; no new physical entities are introduced. The most consequential choices are the beta eccentricity prior, the GP kernel, and the archive ephemerides used as priors.

free parameters (5)
  • Per-planet RV amplitude K (87 values) = e.g., TIC 98720809 b: K=2.01+0.37/-0.36 m/s (best model n, Appendix B)
    The central output of the fits; used to compute m sin i and compared across 12 models.
  • Eccentricity per planet = e.g., TIC 98720809 b: e=0.039+0.036/-0.024
    Fitted with circular, uniform, or beta-distribution priors; the choice drives the paper's main finding.
  • GP hyperparameters (A0-A5, P_GP, lambda_e, lambda_p) = posterior medians, e.g., P_GP=19.29 days for TIC 98720809
    Define the quasi-periodic activity model; priors are set from stellar rotation estimates and values are fitted per system.
  • Jitter terms per instrument or channel = e.g., HARPS_post jitter=1.81 m/s for TIC 98720809
    White-noise excess fitted for each channel; affects uncertainties and K.
  • Long-term trend coefficients (models b and c) = linear and quadratic coefficients, typically below 0.2 m/s/day
    Fitted in trend models and found to have small impact on K.
assumptions (5)
  • domain assumption HARPS RVs trace the Keplerian reflex motion of the star plus smooth stellar activity and white noise.
    Foundational likelihood model used throughout Section 5; no independent validation of the noise model per target.
  • domain assumption The quasi-periodic GP kernel (Eq. 1) adequately represents stellar activity for all stars.
    GP hyperparameters are fit, but kernel adequacy is assumed; Section 7 notes that cadence gaps make GP fitting harder.
  • domain assumption The beta-distribution eccentricity prior from Van Eylen et al. (2019) is appropriate for small transiting planets.
    Used to define models e and n and chosen for the best catalog in Sections 5 and 6.4.
  • domain assumption Published orbital periods and transit times from the NASA Exoplanet Archive are accurate Gaussian priors.
    Period and T0 priors are taken from the archive in Section 5; incorrect ephemerides would bias K.
  • domain assumption The Mamajek & Hillenbrand (2008) rotation-period relation gives valid GP period priors.
    Used to cap the GP period based on stellar temperature in Section 5.

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Cite this review

Pith. "Pith review of Homogeneous planet masses I: Reanalysis of archival HARPS radial velocities." pith.science (2026). https://pith.science/paper/3FJ5DNVR

@misc{pith2026241112723,
  author       = {Pith},
  title        = {Pith review of: Homogeneous planet masses I: Reanalysis of archival HARPS radial velocities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3FJ5DNVR}},
  note         = {Machine review of arXiv:2411.12723}
}
read the original abstract

Empirical exoplanet mass-radius relations have been used to study the demographics and compositions of small exoplanets for many years. However, the heterogeneous nature of these measurements hinders robust statistical analysis of this population, particularly with regard to the masses of planets. For this reason, we perform a homogeneous and consistent re-analysis of the radial velocity (RV) observations of 85 small exoplanets using publicly available HARPS RV data and the fitting toolkit Pyaneti. For the entire sample, we run 12 different models to investigate the impact of modelling choices, including the use of multi-dimensional Gaussian Processes (GPs) to mitigate stellar activity. We find that the way orbital eccentricity is modelled can significantly impact the RV amplitude found in some cases. We also find that the addition of a GP to mitigate stellar activity does impact the RV amplitude found - though if the GP is modelled on activity indicators as well as the RVs the results are more robust. The RV amplitude found for every planet in our sample using all the models is made available for other groups to perform demographics studies. Finally, we provide a list of recommendations for the RV community moving forward.

Figures

Figures reproduced from arXiv: 2411.12723 by the authors.

Figure 1
Figure 1. Histograms showing the properties of the stars and plan [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Impact of long-term trends. Panel a) Comparison of the RV amplitude found for each target for three di [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Impact of orbital eccentricity. Panel a) Comparison of the RV amplitude found for each target for three di [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Impact of GP dimension. Panel a) Comparison of the RV amplitude found for each target for four di [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Panel a: The RV amplitude found with the best model for each planet in our sample, blue stars, compared to the default [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Best-fitting 2-planet orbital model for TIC 9870809. HARPS data shown by blue circles as a function of time. The best-fitting [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: The phase-folded RV data from HARPS (blue circles) [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: The phase-folded RV data from HARPS (blue circles) [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: The no GP beta distribution on eccentricity model for TIC 9870809. HARPS data shown by blue circles as a function of [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: The phase-folded RV data from HARPS (blue circles) [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: The phase-folded RV data from HARPS (blue circles) [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]

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