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REVIEW 4 major objections 5 minor 51 references

Biases in retrieving planetary signals in the presence of quasi-periodic stellar activity

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read When a low-mass planet's orbital period nears the star's rotation period, Gaussian-process activity correction can overestimate the planet's Doppler amplitude by 100% or more, with detection significance below 2 sigma even after two…

desk verdict A competent, useful simulation study quantifying GP-activity bias on small-planet RVs; the matched-kernel caveat is real but scoped, not fatal. read the letter →

arxiv 1908.02217 v1 pith:4ZWOTNGD submitted 2019-08-06 astro-ph.EP

M. Damasso , M. Pinamonti , G. Scandariato , A. Sozzetti This is my paper · ORCID
classification astro-ph.EP
keywords radialvelocitystellaractivityGaussianprocessregressionexoplanetmassesinjection-recoverysimulationsquasi-periodicvariabilityperiodogramanalysislow-massplanets
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 asks how accurately astronomers can weigh small planets when stellar activity contaminates radial-velocity measurements. Through 80,000 mock datasets spanning 16 scenarios, it shows that the standard quasi-periodic Gaussian-process correction systematically biases the retrieved planetary semi-amplitude: for active stars with the planetary period close to the rotation period, $K_b$ is overestimated by 100% or more even with two observing seasons, and the detection significance stays below 2 sigma in nearly all tested cases. The paper also finds that the activity timescale is often poorly recovered from one season of data, and that fitting the activity better does not automatically improve the planet mass. The result matters because these are exactly the regimes targeted by follow-up mass measurements of small transiting planets found by current and planned transit surveys.

What carries the argument

The load-bearing object is the quasi-periodic Gaussian process covariance kernel $$K(t,t') = $h^{2}$ \exp\left[-\frac{(t-t')^2}{2\tau_{\rm AR}^2} - \frac{\$sin^{2}$\left(\frac{\pi(t-t')}{P_{\rm rot}}\right)}{$2w^{2}$}\right] + \sigma_{\rm RV}^2(t)\delta_{t,t'},$$ where $h$ is the activity amplitude, $P_{\rm rot}$ the stellar rotation period, $w$ the periodic length scale, and $\tau_{\rm AR}$ the active-region evolutionary timescale. This kernel both generates the mock stellar activity, via random draws from the GP prior, and serves as the model used to fit it, so the experiment measures how well the recovery pipeline inverts the same stochastic process that created the data. Around that kernel the paper builds an injection-recovery Monte Carlo: 80,000 mock datasets in 16 scenarios, varying activity level, rotation period, activity timescale, period coincidence between planet and star, and one versus two observing seasons, analyzed with nested-sampling GP fits and three periodogram algorithms.

What would settle it

Re-run the Case III scenario (active star, $P_{\rm orb}\approx P_{\rm rot}$, $\tau_{\rm AR}\approx P_{\rm rot}$, two seasons) with the injected activity drawn from a physical rotating-spot or granulation model instead of sampled from the same GP kernel. If the median $K_b$ ratio then falls well below 2, the claimed 100% overestimate is partly an artifact of the matched kernel; if it stays near 2, the bias persists outside the paper's model-fidelity assumption.

Watch

Extended reading notes

Core claim

The paper's central claim is that when stellar activity is modeled as a quasi-periodic Gaussian process, the standard GP regression procedure used to remove it does not reliably retrieve a 1 m/s planetary signal. For the most difficult case, an active star whose rotation period nearly equals the planet's orbital period, the posterior median of the semi-amplitude runs about 1.9--2.1 times the injected value when the activity evolves on the rotation timescale, even with two seasons; with long-lived active regions the overestimate drops to about 20% but remains present. The detection significance is about 1.5--1.6 sigma across active-star cases, formally a non-detection. For quiet stars the median is generally close to the true value, and the 68.3rd percentile of the $K_b$ posterior often gives a more accurate estimate than the median. The paper frames these numbers as expected biases for transit-follow-up mass measurements and as a warning that better constraints on the activity model do not translate into better planetary amplitudes.

Load-bearing premise

The whole exercise assumes that a quasi-periodic Gaussian process kernel faithfully represents stellar activity, because the same kernel generates the mock signals and is then used to remove them; if real activity contains granulation, convection, or evolving spot geometries, the bias sizes could differ.

Editorial extensions

If this is right

  • For active stars with $P_{\rm orb}\approx P_{\rm rot}$ and $\tau_{\rm AR}\approx P_{\rm rot}$, fitted $K_b$ values will be roughly twice the true amplitude even with two seasons of data, so mass estimates in these systems are systematically high.
  • Across nearly all 16 scenarios, a 1 m/s planet is retrieved with significance below 2 sigma, meaning typical RV follow-up of small transiting planets will yield upper limits rather than detections.
  • In quiet-star cases with $P_{\rm orb}$ well separated from $P_{\rm rot}$ and two seasons of data, the 68.3rd percentile of the $K_b$ posterior is closer to the injected value than the median is, so posterior upper limits are more trustworthy than best-fit medians.
  • Adding a second season improves recovery of the activity timescale $\tau_{\rm AR}$ but does not cure the planet-amplitude bias; better activity modeling does not automatically produce better planet masses.
  • Blind periodogram searches recover the planetary period in fewer than about 5 percent of datasets, so periodogram peaks alone cannot establish the presence or amplitude of such planets.

Reading between the lines

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

  • Beyond the paper: because the activity model is given its best possible chance here, with the same kernel used for generation and fitting, real activity containing granulation, convection, or evolving spot geometries would likely make the planet-activity separation harder rather than easier, so the Case III overestimate may be a floor rather than a ceiling.
  • Beyond the paper: the same simulation machinery could be used to test whether adding photometry or spectroscopic activity indicators as extra GP inputs reduces the $K_b$ inflation; the paper sets up exactly such a controlled comparison without running it.
  • Beyond the paper: the strong $P_{\rm orb}\approx P_{\rm rot}$ bias implies that population-level mass-radius studies should flag or down-weight planets whose orbital period is close to the stellar rotation period, a survey-level consequence the paper does not draw.
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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 / 5 minor

Summary. This paper presents an extensive injection-recovery study of radial velocity (RV) time series containing a small planetary signal (K_b = 1 m/s) embedded in quasi-periodic stellar activity. The authors generate 80,000 mock datasets across 16 scenarios that vary activity amplitude (h = 3 and 15 m/s), rotation period, active-region lifetime (short vs. long tau_AR), the proximity of P_orb to P_rot, and one versus two observing seasons. They fit each dataset with a Keplerian plus a quasi-periodic Gaussian-process (GP) activity term using MultiNest and GEORGE, and quantify the recovered K_b, the significance K_b/sigma, the GP hyperparameters, and the fitted jitter. They also analyze a subset of datasets with GLS, BGLS, and FREDEC periodograms to measure completeness and reliability in recovering P_rot and P_orb. The main claims are that for K_b = 1 m/s the detection significance is generally below 2-sigma, that K_b is severely overestimated (by factors up to ~2) for active stars with P_orb ~ P_rot and short tau_AR, that the 68.3rd percentile of the K_b posterior is often closer to the injected value than the median, and that simple periodogram searches have very low completeness for the planet signal.

Significance. If the results hold, they provide a useful quantitative caution for the RV follow-up of small transiting planets, particularly in the context of TESS and PLATO targets. The study's strengths are its large simulation volume (5,000 datasets per scenario), the systematic coverage of activity regimes, the use of a realistic observing calendar, the explicit robustness checks in Appendix B and Sect. 3.2.2, and the comparison of three widely used periodogram tools. The paper also makes the simulated datasets available, which is a practical contribution. The main limitation is that the activity signal is both generated and fitted with the same quasi-periodic GP kernel, so the quoted bias magnitudes are matched-kernel estimates; the paper acknowledges this in Sect. 2.2 and Sect. 3.1, but the practical conclusions in Sect. 5 are stated more broadly. Subject to that caveat, the internal statistics are solid and the reported trends are clearly tabulated.

major comments (4)
  1. [Sect. 3.2, Case III and Table 4] The headline statement in Case III, 'The semi-amplitude Kb is overestimated by 100% or more when tau_AR is close to P_rot, even with two seasons of data,' is not fully supported by Table 4. In the active-star, P_orb~P_rot, short-tau_AR row, K_b,ratio,50% is 2.14+0.5-0.4 for one season, but 1.9+0.5-0.4 for two seasons, so the median two-season overestimate is about 90%, not 100% or more. Please rephrase the claim to state the median overestimate (or identify the percentile that exceeds 100%).
  2. [Sect. 2.5 and Sect. 3.1] The activity term is generated by drawing from the same quasi-periodic GP kernel (Eq. 1) used in the retrieval, so Table 4 measures bias under a matched-kernel, model-true protocol. The manuscript acknowledges in Sect. 2.2 that the quasi-periodic representation is 'not necessarily complete' and in Sect. 3.1 that the kernel choice is a working hypothesis, but the abstract and Sect. 5 draw practical conclusions about real surveys. Real activity includes granulation, convection, differential rotation, and complex spot evolution, all of which could change the bias magnitudes and the conclusions about GP effectiveness. I recommend either adding robustness simulations with a different activity generator (e.g., a spot-occultation model or a GP with a different kernel) or explicitly restricting the central claims to the exactly quasi-periodic case.
  3. [Sect. 3.2, 'Upper limits as defined by the 68th percentile'] Describing the 68.3rd percentile of the K_b posterior as a 'more accurate estimate' is problematic in the low-signal cases treated here, where the posterior is one-sided and this percentile is an upper limit rather than a point estimate. The comparison in Table 4 does not establish accuracy; a coverage statistic, such as the fraction of datasets for which the 68.3% credible interval contains the injected K_b, would be a more meaningful calibration. This issue affects one of the summary bullets in Sect. 5.
  4. [Table 3 and Sect. 3.2] The quantity K_b,50%/sigma_Kb^- is repeatedly called a 'detection significance,' but for one-sided posteriors that pile up near zero, the lower uncertainty sigma_Kb^- can be very small, making this ratio a poor proxy for significance relative to a null amplitude. The statement that detection significance 'stays below 2 sigma' should be justified or the quantity should be renamed, for example 'median-over-lower-uncertainty ratio.'
minor comments (5)
  1. [Sect. 4] The text '1 0000 RV mock datasets' should read '10 000 RV mock datasets.'
  2. [Table 5] The line 'median sigma_jit, no GP/sigma_jit, with GP' is duplicated within each observing-season block; remove the repeated rows.
  3. [Table 8] In the low-activity, short-tau_AR, two-season GLS row, the entry '30.9.4%' should be '30.9%.'
  4. [Section 1] There is a typo in the introduction: 'Zeng et al. 017b' should be 'Zeng et al. 2017b.'
  5. [Sections 2.1 and 2.5] The notation alternates between K_p (Sect. 2.1) and K_b (Sect. 2.5 onward) for the planetary semi-amplitude; please use a single symbol throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the matched-kernel protocol is an explicitly stated modeling assumption, and the bias results are conditional claims, not self-fulfilling by construction.

full rationale

The paper is an injection-recovery simulation study, and it repeatedly and explicitly conditions its conclusions on the quasi-periodic GP kernel. Mock activity is drawn from Eq. (1) using the GEORGE sample function, and the retrieval fits the same kernel, with the stated working hypothesis that this kernel choice is justified (Sect. 3.1). This is a model-fidelity assumption rather than a circular derivation: the bias statistics for Kb, tau_AR, sigma_jit, and the periodogram completeness/reliability figures are computed from the simulated data and are not imposed by the definitions of the injected parameters. The paper is also transparent that the quasi-periodic model is 'only one of several possible representations of the stellar component in RV time series' and that it lacks the detail of Dumusque (2016), including granulation and oscillations. The self-citations (Pinamonti et al. 2017, Damasso & Del Sordo 2017, Pinamonti et al. 2018) are used for conventions, prior choices, and algorithm comparisons, but the central bias map does not rest on accepting an unverified self-cited theorem. No step in the derivation chain reduces by construction to its own inputs, so the honest finding is no significant circularity.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The central results are conditional on the simulation and prior parameter choices, which are listed here. The main domain assumption is that quasi-periodic GP activity (Eq. 1) is an adequate model of stellar RV noise; the paper flags this as a working hypothesis. No new physical entities are introduced.

free parameters (8)
  • Planet semi-amplitude Kb = 1 m/s fixed; 2 and 3 m/s in exploratory subsets
    Choice of a challenging low-mass planet signal; the entire bias assessment depends on this amplitude.
  • Stellar activity amplitude h = 3 m/s (low activity), 15 m/s (high activity)
    Representative values from literature; defines the signal-to-noise of activity relative to the planet.
  • RV uncertainty sigma_RV = N(2, 0.3^2) m/s
    Adopted average precision and scatter for HARPS/HARPS-N-like follow-up observations.
  • Stellar rotation period P_rot = 20 d (low activity), 10 d (active)
    Chosen to represent active fast rotators and quiet slow rotators.
  • Active region timescale tau_AR = P_rot (short) and 10*P_rot (long)
    Explored extreme regimes; recovery of tau_AR is scenario dependent.
  • Number of epochs per season = 40 (one season) or 80 (two seasons)
    Sampling size affects detection significance and periodogram behavior.
  • P_orb prior width in MC fit = P_sim +/- 0.5 d
    Assumes period is known from transits; restricts the fit and affects Kb recovery.
  • tau_AR prior in MC fit = U(0,1000) d linear; log-uniform variant explored
    Prior choice changes tau_AR accuracy but not Kb accuracy, as shown in Sect. 3.2.2.
assumptions (5)
  • domain assumption The quasi-periodic GP kernel (Eq. 1) is a realistic representation of stellar activity in RV time series.
    Stated in Sect. 2.2 as 'a realistic, even if not necessarily complete, representation' and used as the working hypothesis for recovery in Sect. 3.1.
  • domain assumption Planets in the simulation are on circular orbits.
    Sect. 2.1: 'We simulated planets on circular orbits, which is a reasonable assumption for low-mass planets with low P_orb.'
  • domain assumption Data are collected with a single instrument and noise is Gaussian with known sigma_RV, plus an optional fitted jitter.
    Sect. 2.3 and Sect. 3.1; multi-instrument offsets are not modeled.
  • standard math Bayesian sampling with MultiNest over the parameter space yields correct posterior estimates.
    Sect. 3.1 uses publicly available MultiNest and GEORGE; no formal verification, but the tools are standard in exoplanet analyses.
  • domain assumption The orbital period is known from transits to within 0.5 days and P_rot to within 5 days for the MC analysis.
    Table 2 priors: P_orb U(P_sim-0.5, P_sim+0.5) and P_rot U(P_rot_sim-5, P_rot_sim+5); this is optimistic for non-transiting planets.

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Pith. "Pith review of Biases in retrieving planetary signals in the presence of quasi-periodic stellar activity." pith.science (2026). https://pith.science/paper/4ZWOTNGD

@misc{pith2026190802217,
  author       = {Pith},
  title        = {Pith review of: Biases in retrieving planetary signals in the presence of quasi-periodic stellar activity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4ZWOTNGD}},
  note         = {Machine review of arXiv:1908.02217}
}
abstract

Gaussian process regression is a widespread tool used to mitigate stellar correlated noise in radial velocity time series. It is particularly useful to search for and determine the properties of signals induced by small-size, low-mass planets ($R_p<4R_{\rm \oplus}$, $m_p<10M_{\rm \oplus}$). By using extensive simulations based on a quasi-periodic representation of the stellar activity component, we investigate the ability in retrieving the planetary parameters in 16 different realistic scenarios. We analyse systems composed by one planet and host stars having different levels of activity, focusing on the challenging case represented by low-mass planets, with Doppler semi-amplitudes in the range 1-3 $m s^{-1}$. We consider many different configurations for the quasi-periodic stellar activity component, as well as different combinations of the observing epochs. We use commonly-employed analysis tools to search for and characterize the planetary signals in the datasets. The goal of our injection-recovery statistical analysis is twofold. First, we focus on the problem of planet mass determination. Then, we analyse in a statistical way periodograms obtained with three different algorithms, in order to explore some of their general properties, as the completeness and reliability in retrieving the injected planetary and stellar activity signals with low false alarm probabilities. This work is intended to provide some understanding of the biases introduced in the planet parameters inferred from the analysis of radial velocity time series that contain correlated signals due to stellar activity. It also aims to motivate the use and encourage the improvement of extensive simulations for planning spectroscopic follow-up observations.

Figures

Figures reproduced from arXiv: 1908.02217 by the authors.

Figure 1
Figure 1. Examples of mock RV datasets used in this work.Upper panel: Two seasons of data for low-activity stars with Prot , Porb and τAR Prot. Lower panel: Two seasons of data for active stars with Prot ∼ Porb and τAR ∼ Prot. 3 MONTE CARLO FITTING ANALYSIS We first analysed the simulated datasets described in Sect. 2 from the perspective of someone interested in determining the mass of a transiting planet, within the framewo… view at source ↗
Figure 2
Figure 2. Posterior distributions for τAR,ratio= τAR,retrieved τAR,injected for the scenario represented by a star with low and quickly variable ac￾tivity (short τAR) hosting a planet with Porb ∼ Prot. The upper and lower panels show the results for one and two seasons of data, respectively. The vertical lines indicate the median of each dis￾tribution. The inset plots are a zoomed in view around τAR,ratio=1 in log-scale, show… view at source ↗
Figure 3
Figure 3. Posterior distributions for the planet detection signif￾icance Kb, 50%/σ − Kb concerning the scenario represented by a star with low and slowly variable activity (long τAR) hosting a planet with Porb , Prot. The upper plot shows the result for one season of data, and the lower plot that for two seasons of observations. The vertical lines indicate the median of each distribution. Case III. Active star; Porb ∼ Prot. T… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Distributions of the posterior Kb,ratio 50%= Kb,retrieved Kb,injected for the scenario represented by a star with low and stable activity (long τAR) hosting a planet with Porb ,Prot. The results refer to the case of datasets composed by one semester of observations. Th…
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 5
Figure 5. Figure 5: Posterior distributions of the quantity Kb, 50%/σ − Kb for the scenario represented by a star with low and stable activity (long τAR) hosting a planet with Porb , Prot. The results refer to the case of datasets composed by one semester of observations. The injected pla…
Figure 7
Figure 7. Figure 7: Distributions of the retrieved/simulated period ratios (the injected periods correspond to Prot) for the cases of active stars, Prot ,Porb, short τAR, Nepochs,s1 (solid black line) and Nepochs,s2 (dashed red line) data. Here we show the results for GLS and BGLS algorit…
Figure 8
Figure 8. Figure 8: Examples of mock RV datasets spanning two seasons for which the injected active regions evolutionary time scale τAR is close to the stellar rotation period. (Plot a: Low-activity star with Prot , Porb. Plot b: active star with Prot ∼ Porb). When searched for periodicit…

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Works this paper leans on

51 extracted references · 10 canonical work pages

  1. [1]

    Affer L., et al., 2016, @doi [ ] 10.1051/0004-6361/201628690 , http://adsabs.harvard.edu/abs/2016A

    doi:10.1051/0004-6361/201628690 2016

  2. [2]

    C., 2017, Transit-Timing and Duration Variations for the Discovery and Characterization of Exoplanets

    Agol E., Fabrycky D. C., 2017, Transit-Timing and Duration Variations for the Discovery and Characterization of Exoplanets . p. 7, @doi 10.1007/978-3-319-30648-3_7-1

    work page doi:10.1007/978-3-319-30648-3_7-1 2017

  3. [3]

    W., O'Neil M., 2014

    Ambikasaran S., Foreman-Mackey D., Greengard L., Hogg D. W., O'Neil M., 2014

    work page 2014

  4. [4]

    Artigau \'E ., et al., 2014, in Ground-based and Airborne Instrumentation for Astronomy V. p. 914715 ( @eprint arXiv 1406.6992 ), @doi 10.1117/12.2055663

    arXiv 2014

  5. [5]

    V., 2013, @doi [Astronomy and Computing] 10.1016/j.ascom.2013.11.003 , http://adsabs.harvard.edu/abs/2013A

    Baluev R. V., 2013, @doi [Astronomy and Computing] 10.1016/j.ascom.2013.11.003 , http://adsabs.harvard.edu/abs/2013A

    work page doi:10.1016/j.ascom.2013.11.003 2013

  6. [6]

    V., 2018, @doi [ ] 10.3847/1538-4365/aae3e9 , https://ui.adsabs.harvard.edu/abs/2018ApJS..239....2B 239, 2

    Barclay T., Pepper J., Quintana E. V., 2018, @doi [ ] 10.3847/1538-4365/aae3e9 , https://ui.adsabs.harvard.edu/abs/2018ApJS..239....2B 239, 2

    doi:10.3847/1538-4365/aae3e9 2018

  7. [7]

    Online at <A href=``href=''>https://pos.sissa.it/cgi-bin/reader/conf.cgi?confid=269</A>, id.69

    Benatti S., et al., 2016, in Frontier Research in Astrophysics II, held 23-28 May, 2016 in Mondello (Palermo), Italy (FRAPWS2016). Online at <A href=``href=''>https://pos.sissa.it/cgi-bin/reader/conf.cgi?confid=269</A>, id.69. p. 69

    work page 2016

  8. [8]

    A., Huber D., Gaidos E., van Saders J

    Berger T. A., Huber D., Gaidos E., van Saders J. L., 2018, @doi [ ] 10.3847/1538-4357/aada83 , https://ui.adsabs.harvard.edu/abs/2018ApJ...866...99B 866, 99

    doi:10.3847/1538-4357/aada83 2018

Show all 51 references
  1. [9]

    Buchner J., et al., 2014, @doi [ ] 10.1051/0004-6361/201322971 , http://adsabs.harvard.edu/abs/2014A

    2014 doi

  2. [10]

    Damasso M., Del Sordo F., 2017, @doi [ ] 10.1051/0004-6361/201630050 , http://adsabs.harvard.edu/abs/2017A

    2017 doi

  3. [11]

    Damasso M., et al., 2018, @doi [ ] 10.1051/0004-6361/201732459 , 615, A69

    2018 doi

  4. [12]

    Damasso M., et al., 2019, @doi [ ] 10.1051/0004-6361/201834671 , 624, A38

    2019 doi

  5. [13]

    Dumusque X., 2016, @doi [ ] 10.1051/0004-6361/201628672 , http://adsabs.harvard.edu/abs/2016A

    2016 doi

  6. [14]

    Dumusque X., et al., 2017, @doi [ ] 10.1051/0004-6361/201628671 , http://adsabs.harvard.edu/abs/2017A

    2017 doi

  7. [15]

    Feng F., Tuomi M., Jones H. R. A., Butler R. P., Vogt S., 2016, @doi [ ] 10.1093/mnras/stw1478 , http://adsabs.harvard.edu/abs/2016MNRAS.461.2440F 461, 2440

    2016 doi

  8. [16]

    T., Hobson M

    Feroz F., Balan S. T., Hobson M. P., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18962.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.415.3462F 415, 3462

    2011

  9. [17]

    P., Cameron E., Pettitt A

    Feroz F., Hobson M. P., Cameron E., Pettitt A. N., 2013, preprint, http://adsabs.harvard.edu/abs/2013arXiv1306.2144F ( @eprint arXiv 1306.2144 )

    2013 arXiv

  10. [18]

    A., et al., 2016, @doi [ ] 10.1088/1538-3873/128/964/066001 , http://adsabs.harvard.edu/abs/2016PASP..128f6001F 128, 066001

    Fischer D. A., et al., 2016, @doi [ ] 10.1088/1538-3873/128/964/066001 , http://adsabs.harvard.edu/abs/2016PASP..128f6001F 128, 066001

    2016 doi

  11. [19]

    J., Petigura E

    Fulton B. J., Petigura E. A., 2018, @doi [ ] 10.3847/1538-3881/aae828 , https://ui.adsabs.harvard.edu/abs/2018AJ....156..264F 156, 264

    2018 doi

  12. [20]

    J., et al., 2017, @doi [ ] 10.3847/1538-3881/aa80eb , http://adsabs.harvard.edu/abs/2017AJ....154..109F 154, 109

    Fulton B. J., et al., 2017, @doi [ ] 10.3847/1538-3881/aa80eb , http://adsabs.harvard.edu/abs/2017AJ....154..109F 154, 109

    2017 doi

  13. [21]

    D., et al., 2018, @doi [ ] 10.3847/1538-3881/aab8f3 , http://adsabs.harvard.edu/abs/2018AJ....155..203H 155, 203

    Haywood R. D., et al., 2018, @doi [ ] 10.3847/1538-3881/aab8f3 , http://adsabs.harvard.edu/abs/2018AJ....155..203H 155, 203

    2018 doi

  14. [22]

    Jurgenson C., Fischer D., McCracken T., Sawyer D., Szymkowiak A., Davis A., Muller G., Santoro F., 2016, in Ground-based and Airborne Instrumentation for Astronomy VI. p. 99086T ( @eprint arXiv 1606.04413 ), @doi 10.1117/12.2233002

    2016 arXiv

  15. [23]

    D., Fortney J

    Lopez E. D., Fortney J. J., 2014, The Astrophysical Journal, 792, 1

    2014

  16. [24]

    Mahadevan S., et al., 2014, in Ground-based and Airborne Instrumentation for Astronomy V. p. 91471G, @doi 10.1117/12.2056417

    2014 doi

  17. [25]

    Malavolta L., et al., 2018, @doi [ ] 10.3847/1538-3881/aaa5b5 , http://adsabs.harvard.edu/abs/2018AJ....155..107M 155, 107

    2018 doi

  18. [26]

    Marconi A., et al., 2016, in Ground-based and Airborne Instrumentation for Astronomy VI. p. 990823 ( @eprint arXiv 1609.00497 ), @doi 10.1117/12.2231653

    2016 arXiv

  19. [27]

    S., et al., 2018, @doi [ ] 10.1051/0004-6361/201732143 , http://adsabs.harvard.edu/abs/2018A

    Mascare \ n o A. S., et al., 2018, @doi [ ] 10.1051/0004-6361/201732143 , http://adsabs.harvard.edu/abs/2018A

    2018 doi

  20. [28]

    P., Correia C

    Mortier A., Faria J. P., Correia C. M., Santerne A., Santos N. C., 2015, @doi [ ] 10.1051/0004-6361/201424908 , http://adsabs.harvard.edu/abs/2015A

    2015 doi

  21. [29]

    E., Ford E

    Nelson B. E., Ford E. B., Buchner J., Cloutier R., D \' az R. F., Faria J. P., Rajpaul V. M., Rukdee S., 2018, preprint, http://adsabs.harvard.edu/abs/2018arXiv180604683N ( @eprint arXiv 1806.04683 )

    2018 arXiv

  22. [30]

    E., 2019, @doi [Annual Review of Earth and Planetary Sciences] 10.1146/annurev-earth-053018-060246 , https://ui.adsabs.harvard.edu/abs/2019AREPS..47...67O 47, 67

    Owen J. E., 2019, @doi [Annual Review of Earth and Planetary Sciences] 10.1146/annurev-earth-053018-060246 , https://ui.adsabs.harvard.edu/abs/2019AREPS..47...67O 47, 67

    2019 doi

  23. [31]

    E., Wu Y., 2013, @doi [ ] 10.1088/0004-637X/775/2/105 , http://adsabs.harvard.edu/abs/2013ApJ...775..105O 775, 105

    Owen J. E., Wu Y., 2013, @doi [ ] 10.1088/0004-637X/775/2/105 , http://adsabs.harvard.edu/abs/2013ApJ...775..105O 775, 105

    2013 doi

  24. [32]

    Pepe F., et al., 2014, @doi [Astronomische Nachrichten] 10.1002/asna.201312004 , 335, 8

    2014 doi

  25. [33]

    Perger M., et al., 2017, @doi [ ] 10.1051/0004-6361/201731307 , http://adsabs.harvard.edu/abs/2017A

    2017 doi

  26. [34]

    S., Damasso M., 2017, @doi [ ] 10.1093/mnras/stx664 , http://adsabs.harvard.edu/abs/2017MNRAS.468.3775P 468, 3775

    Pinamonti M., Sozzetti A., Bonomo A. S., Damasso M., 2017, @doi [ ] 10.1093/mnras/stx664 , http://adsabs.harvard.edu/abs/2017MNRAS.468.3775P 468, 3775

    2017 doi

  27. [35]

    Pinamonti M., et al., 2018, @doi [ ] 10.1051/0004-6361/201732535 , http://adsabs.harvard.edu/abs/2018A

    2018 doi

  28. [36]

    Quirrenbach A., et al., 2016, in Ground-based and Airborne Instrumentation for Astronomy VI. p. 990812, @doi 10.1117/12.2231880

    2016 doi

  29. [37]

    Rauer H., et al., 2014, @doi [Experimental Astronomy] 10.1007/s10686-014-9383-4 , http://adsabs.harvard.edu/abs/2014ExA....38..249R 38, 249

    2014 doi

  30. [38]

    C., Mayor M., Naef D., Pepe F., Queloz D., Udry S., Blecha A., 2000, , http://adsabs.harvard.edu/abs/2000A

    Santos N. C., Mayor M., Naef D., Pepe F., Queloz D., Udry S., Blecha A., 2000, , http://adsabs.harvard.edu/abs/2000A

    2000

  31. [39]

    Schwab C., et al., 2016, in Ground-based and Airborne Instrumentation for Astronomy VI. p. 99087H, @doi 10.1117/12.2234411

    2016 doi

  32. [40]

    P., 2016, @doi [ ] 10.1093/mnras/stw1936 , http://adsabs.harvard.edu/abs/2016MNRAS.463.1844S 463, 1844

    Stelzer B., Damasso M., Scholz A., Matt S. P., 2016, @doi [ ] 10.1093/mnras/stw1936 , http://adsabs.harvard.edu/abs/2016MNRAS.463.1844S 463, 1844

    2016 doi

  33. [41]

    I., Esposito M., 2017, @doi [ ] 10.1093/mnras/stx771 , http://adsabs.harvard.edu/abs/2017MNRAS.468.4772S 468, 4772

    Su \'a rez Mascare \ n o A., Rebolo R., Gonz \'a lez Hern \'a ndez J. I., Esposito M., 2017, @doi [ ] 10.1093/mnras/stx771 , http://adsabs.harvard.edu/abs/2017MNRAS.468.4772S 468, 4772

    2017 doi

  34. [42]

    Van Eylen V., et al., 2019, @doi [ ] 10.3847/1538-3881/aaf22f , https://ui.adsabs.harvard.edu/abs/2019AJ....157...61V 157, 61

    2019 doi

  35. [43]

    A., Ciardi D

    Vanderburg A., Plavchan P., Johnson J. A., Ciardi D. R., Swift J., Kane S. R., 2016, @doi [ ] 10.1093/mnras/stw863 , http://adsabs.harvard.edu/abs/2016MNRAS.459.3565V 459, 3565

    2016 doi

  36. [44]

    M., Marcy G

    Weiss L. M., Marcy G. W., 2014, @doi [ ] 10.1088/2041-8205/783/1/L6 , http://adsabs.harvard.edu/abs/2014ApJ...783L...6W 783, L6

    2014 doi

  37. [45]

    Wildi F., et al., 2017, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. p. 1040018, @doi 10.1117/12.2275660

    2017 doi

  38. [46]

    Wu Y., 2019, @doi [ ] 10.3847/1538-4357/ab06f8 , https://ui.adsabs.harvard.edu/abs/2019ApJ...874...91W 874, 91

    2019 doi

  39. [47]

    Zechmeister M., K \"u rster M., 2009, @doi [ ] 10.1051/0004-6361:200811296 , http://adsabs.harvard.edu/abs/2009A

    2009 doi

  40. [48]

    Zeng L., et al., 2017, in Lunar and Planetary Science Conference. p. 1576

    2017

  41. [49]

    Zeng L., et al., 2019, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2019arXiv190604253Z

    2019

  42. [50]

    B., Sasselov D

    Zeng L., Jacobsen S. B., Sasselov D. D., 2017b, Research Notes of the AAS, 1, 32

  43. [51]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.