Pith. sign in

REVIEW 3 major objections 4 minor 225 references

A planet-formation model not tuned to any survey overproduces detectable planets by ~70% and yields orbits too close and too circular, the paper finds.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-04 18:45 UTC pith:PXV5PITD

load-bearing objection The first genuinely quantitative benchmark of the Gen III Bern model against HARPS/Coralie, with honest Monte Carlo machinery and a specific discrepancy list; the eccentricity deficit is the softest headline number because it compares against RV fits the paper itself shows are biased. the 3 major comments →

arxiv 2509.09762 v1 pith:PXV5PITD submitted 2025-09-11 astro-ph.EP

The New Generation Planetary Population Synthesis (NGPPS). VII. Statistical comparison with the HARPS/Coralie survey

Alexandre Emsenhuber , Christoph Mordasini , Michel Mayor , Maxime Marmier , St\'ephane Udry , Remo Burn , Martin Schlecker , Lokesh Mishra
show 3 more authors
Yann Alibert Willy Benz Erik Asphaug
This is my paper
classification astro-ph.EP
keywords planet formationpopulation synthesiscore accretionradial velocity surveyHARPS/Coralieplanetary mass functioneccentricity distributionplanetary migration
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper asks whether a modern planet-formation simulation can reproduce, as a statistical population, the planets found by the HARPS/Coralie radial-velocity survey. The authors run their nominal Generation III Bern model—not tuned to any particular survey—apply the survey's detection completeness to the synthetic planets, and compare 1000 mock observations with the actual 169 detected planets. The central finding is that the model captures several qualitative features (two planet groups, bimodal mass function, multiplicity near 1.6, some correlations) but fails quantitatively: too many planets, a too-deep desert, too many giants, too-low eccentricities, a too-weak metallicity effect, and planets too close to their stars. The paper then shows that modest parameter changes (larger planetesimals, slightly weaker migration, slower disc-limited gas accretion) nearly fix the mass function, but the orbital distances and dynamical excitation remain wrong, implying missing physical processes rather than simple parameter tuning.

Core claim

The paper's claim, stated on its own terms, is that the nominal generation-III Bern model population, once passed through the HARPS/Coralie detection bias, is statistically inconsistent with the observed sample: it predicts 290 planets where 169 are found (~70% excess), a planetary desert between about 20 and 200 Earth masses that is ~60% too empty, a ~40% relative excess of giant planets, a median eccentricity of 0.07 versus an observed 0.15, a too-weak dependence of planet occurrence on stellar metallicity, and planets systematically closer to their stars. Extending the N-body integration to 100 Myr does not cure the dynamical discrepancies. The authors then construct an adjusted populatio

What carries the argument

The load-bearing object is the Generation III Bern model: a global population-synthesis code in which protoplanets grow by core accretion from planetesimals, accrete gas, migrate under type I and type II disc migration, and interact dynamically through N-body physics, all starting from observationally motivated disc initial conditions. The comparison mechanism is the survey's mean completeness map—a detection-probability grid in minimum mass and period built by injecting circular-orbit signals into the actual HARPS/Coralie data—applied uniformly to every synthetic planet, with 1000 Monte-Carlo mock observations used to build confidence intervals and run KS tests on mass, period, mass-period,

Load-bearing premise

The load-bearing premise is that the survey's mean completeness map—an average detection probability derived from the same unpublished survey and applied uniformly to all 822 stars and all synthetic planets—accurately represents what HARPS/Coralie would detect, including for eccentric orbits and varied system architectures.

What would settle it

Recompute the comparison star-by-star: inject each synthetic planet into the actual HARPS/Coralie noise and detection pipeline rather than using the averaged circular-orbit completeness map. If the corrected count drops from 290 toward 169 and the eccentricity distributions agree, the missing-physics conclusion weakens; if the 70% excess and the median-eccentricity gap persist, the conclusion stands.

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

Share X Bluesky LinkedIn Reddit HN

If this is right

  • The too-deep desert and the over-massive giants both point to the same model element: disc-limited gas accretion rates that are too high in the detached phase.
  • Because the period-ratio distribution changes little when the N-body integration is extended to 100 Myr, late dynamical instabilities are not what breaks 2:1 resonances in the RV-accessible regime.
  • Migration strength is tightly constrained: reducing it enough to push planets outward would overproduce giant planets relative to sub-Neptunes.
  • The optimised population gets total planet count and mass distribution nearly right but leaves distances and eccentricities wrong, so the deficit is in missing processes, not just parameter values.
  • The metallicity correlation is reproduced in shape but too weak, and the model cannot form enough metal-poor, close-packed sub-Neptune systems of the kind observed.

Where Pith is reading between the lines

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

  • A star-by-star completeness calculation that also accounts for eccentricity and per-star noise could shrink or shift the claimed 70% excess and factor-two eccentricity gap; the averaged circular-orbit map smooths over exactly the regions where the model's overabundance might be concentrated.
  • If wide initial orbits are indeed the missing ingredient, then a synthesis with a larger initial disc radius or with pebble accretion should populate the 300–3000 day giant-planet region without also filling the desert; that is a directly testable prediction.
  • The model's failure to produce hot Jupiters through disc migration while finding ~20 planets destined to hit the star on eccentric orbits suggests that adding tidal circularisation could close the hot-Jupiter gap without invoking new initial conditions.
  • The same biasing-the-synthesis approach could be applied to transit surveys, testing whether the missing-physics conclusion is detection-technique-specific or fundamental to the formation model.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper compares synthetic planet populations from the Bern model (NG76 and NG76longshot) against the HARPS/Coralie RV survey of Mayor et al. (2011), updated to 2015. A synthetic detection bias based on the M11 completeness map is applied to the synthetic populations, and 1000 mock observations of 822-star samples are compared to the observed sample via KS tests. The nominal population reproduces several qualitative features: the bimodal mass function, close-in sub-Neptunes versus distant giants, mean multiplicity ~1.6, period-ratio pile-ups, and broad metallicity and eccentricity trends. The headline discrepancies are a ~70% overproduction of detectable planets, a planetary desert too deep by ~60%, a ~40% relative excess of giants, median eccentricity 0.07 versus 0.15, and planets too close to their stars. A tuned population NG192 (2 km planetesimals, reduced migration, modified gas accretion) nearly matches the mass function (KS distance 1.35 vs 1.36) but still fails on orbital distances and has even lower eccentricities. The paper concludes that missing physics, such as wider formation orbits, eccentricity excitation, and slower gas accretion, is needed.

Significance. The nominal population was not tuned to the HARPS/Coralie survey, so this is a valuable, independent stress test of the Bern model. The Monte Carlo mock-observation procedure is clearly described, and the internally consistent discrepancy list provides a concrete benchmark for the community. The tuned-population experiment illustrates parameter degeneracies and correctly identifies that no single parameter change fixes both the mass and period distributions. The main weaknesses are that all quantitative claims inherit the assumptions of a single mean completeness map, and that the eccentricity comparison uses catalog eccentricities that are affected by RV fitting bias, as the paper itself partly notes.

major comments (3)
  1. [Sec. 3.7.1, Fig. 10; Abstract] The factor-of-two eccentricity deficit is a headline discrepancy, but the comparison is not apples-to-apples. The synthetic eccentricities are noiseless model values, while the HARPS/Coralie eccentricities come from noisy, sparsely sampled RV fits. The paper cites Zakamska et al. (2011), who found that about 38 percent of RV planets have e<0.05 versus 17 percent in standard catalogs. Since the synthetic median is 0.07, an end-to-end test that injects synthetic RV signals and recovers eccentricities with the same pipeline could substantially reduce or even reverse the claimed deficit. This matters because the dynamically cold conclusion is used as evidence for missing eccentricity-excitation physics. Please provide such a test or re-derive the observed eccentricity distribution with an upper-limit-aware method.
  2. [Sec. 2.3, Fig. 3; Sec. 3] All quantitative discrepancy percentages (70 percent overproduction, 60 percent desert depth, 40 percent giant excess, and the eccentricity comparison) are computed under one mean detection-completeness map that, as stated in Sec. 2.3, ignores eccentricity and system architecture and is averaged over stars. The map is also from the same unpublished M11 analysis used as the observed sample. The quantitative claims would be much more robust with a sensitivity analysis, e.g., applying an eccentricity-aware or star-by-star completeness correction and checking how much the percentages change. Without this, the direction and magnitude of some discrepancies could plausibly change.
  3. [Sec. 4, Fig. 13] The claim that NG192 nearly matches the observed mass function is based on a KS distance of 1.35 at the 95 percent level versus a threshold of 1.36. Because NG192's parameters were selected using the HARPS/Coralie mass distribution, this near-threshold value is a fitting residual, not an independent validation. The paper's main conclusion that mass and period cannot be simultaneously matched is still valid, but the near-threshold wording risks overinterpretation. A holdout split or a clear statement that this is a posterior fit would be more appropriate.
minor comments (4)
  1. [Fig. C.1 caption] The caption says the median is indicated by the vertical dashed red line and then refers to the synthetic value also as the vertical dashed red line. This is ambiguous; please clarify which line is which.
  2. [Sec. 3.1] The phrase 'we detect 290+30-28 planets' could be misread as an actual detection. Consider writing 'the mock observations yield...' or 'the biased synthetic sample contains...'.
  3. [Sec. 3.2] The desert-depth metric uses the 20-200 M_earth range chosen from the synthetic cumulative distribution. Since the observed desert may be located elsewhere, please report the sensitivity of the 57-60 percent number to the adopted mass boundaries.
  4. [Sec. 2.3] Given that the quantitative claims rest on the M11 completeness map, please make that map available in machine-readable form, since the M11 survey paper remains unpublished.

Circularity Check

1 steps flagged

Mostly independent comparison; one minor in-sample fit is presented as a validation but is transparently labeled an optimization.

specific steps
  1. fitted input called prediction [Sect. 4, paragraphs 3-5 (Fig. 13 discussion)]
    "our goal is find a combination of parameters that best reproduces the total number of planets and their mass distribution (that is, not their location). ... The planetary mass function of the new population better reproduces the observed population overall. ... the KS distance at the 95 % of the random observation is 1.35 compared to a limit value of 1.36, which means that we are just below the rejection threshold"

    NG192's parameters (planetesimal radius, migration efficiency, gas-accretion cap) are explicitly chosen to reproduce the HARPS/Coralie planet number and mass function. Reporting that the resulting mass function is close (KS 1.35 vs 1.36) is a goodness-of-fit statement with the same quantity as the fitting objective, so it is in-sample validation rather than an out-of-sample prediction. The paper does not disguise this, and its main nominal-population comparison (NG76) is independent, so the circularity is minor and secondary.

full rationale

The central nominal comparison (Sect. 3) uses NG76/NG76longshot, populations from prior NGPPS papers whose parameters were not adjusted to the HARPS/Coralie sample; the paper explicitly stresses this (Sect. 3.1). The M11 detection map (Sect. 2.3) is an empirical injection-recovery completeness function for the same survey, and applying it to synthetic planets is the standard way to form a mock observation, not a fit of the model to the observed planet properties. The paper's main discrepancy percentages are therefore independent predictions of the model. The only place where a fitted result is used as if it were a validation is the NG192 mass-function comparison (Sect. 4): the population is optimized to match the number and mass distribution, so its KS distance of 1.35 is a fit diagnostic. This does not infect the central claim because the paper uses NG192's remaining period/eccentricity failures to argue for missing physics, and those were not fitting targets. Two limitations are acknowledged in-text but are correctness, not circularity, concerns: the M11 map is an average that ignores eccentricity and system architecture (Sect. 2.3), and RV eccentricities are prone to overestimation (Sect. 3.7.1), so the factor-of-two eccentricity discrepancy may be partly an artifact of comparing noiseless synthetic eccentricities to catalog fitted eccentricities. Footnote 1 also notes M11 is not refereed. These caveats lower confidence in the quantitative percentages but do not make the derivation circular.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The nominal comparison relies on the survey completeness map, the synthetic initial conditions, and the model structure. The NG192 optimization adds three fitted parameters. No invented entities are introduced. The main load-bearing assumptions are stated transparently in the text.

free parameters (4)
  • R_plan, planetesimal radius in NG192 = 2 km (nominal 300 m)
    Selected in Sect. 4/Appendix B.1 to reduce giant formation efficiency and re-balance sub-Neptune vs giant numbers against the HARPS/Coralie sample.
  • Migration efficiency multiplier in NG192 = 7/8 of nominal Type I and II rates
    Selected in Sect. 4/Appendix B.2; a modest reduction improves the mass function, while stronger reduction overproduces giants.
  • Disc-limited gas accretion cap in NG192 = min(Bondi rate, radial gas flow)
    Adopted to slow detached-phase gas accretion and better match giant planet masses (Sect. 4).
  • Detection probability floor in synthetic bias = 1%
    Planets with detection probability below 1% are unconditionally treated as undetected (Sect. 2.3); a hand-set cutoff that trims the long-period tail.
axioms (4)
  • domain assumption The M11 mean detection probability map is a valid per-star completeness function for all 822 HARPS/Coralie stars.
    The map is an average over survey stars and ignores per-star noise, eccentricity, and system architecture (Sect. 2.3); all quantitative comparisons in Sects. 3.1-3.7 rest on it.
  • domain assumption Synthetic initial conditions (disc masses, sizes, lifetimes, dust-to-gas from [Fe/H], M*=1 Msun) are representative of the HARPS sample.
    Sample mean stellar mass 0.91 Msun and mean [Fe/H] -0.11 differ from the synthetic assumptions; the authors argue via Burn et al. (2021) that the mass offset cannot fully explain the mismatch (Sect. 2.4, 3.2).
  • domain assumption The Bern model's existing processes (core accretion, migration, N-body, disc evolution) are the right framework, so residual mismatch indicates missing physics rather than wrong model structure.
    The main conclusion (Sect. 5) that wider orbits, eccentricity excitation, and slower gas accretion are needed presumes the model's structural choices are not the cause.
  • standard math KS test procedures (1D and 2D) applied to correlated, multiplicities-containing samples behave as implemented.
    The paper uses Monte Carlo calibration for significance (Hope 1968; Press et al. 2002), but the 2D test with 169 observed planets drawn from systems with correlated planets may have non-standard properties (Sect. 2.5).

pith-pipeline@v1.3.0-alltime-deepseek · 42393 in / 18131 out tokens · 186563 ms · 2026-08-04T18:45:16.683313+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of The New Generation Planetary Population Synthesis (NGPPS). VII. Statistical comparison with the HARPS/Coralie survey." pith.science (2026). https://pith.science/paper/PXV5PITD

@misc{pith2026250909762,
  author       = {Pith},
  title        = {Pith review of: The New Generation Planetary Population Synthesis (NGPPS). VII. Statistical comparison with the HARPS/Coralie survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PXV5PITD}},
  note         = {Machine review of arXiv:2509.09762}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

We seek to quantify the fidelity with which modern population syntheses reproduce observations in view of their use as predictive tools. We compared synthetic populations from the Generation 3 Bern Model of Planet Formation and Evolution (core accretion, solar-type host stars) and the HARPS/Coralie radial velocity sample. We biased the synthetic planet population according to the completeness of the observed data and performed quantitative statistical comparisons and systematically identified agreements and differences. Our nominal population reproduces many of the main features of the HARPS planets: two main groups of planets (close-in sub-Neptunes and distant giants), a bimodal mass function with a less populated `desert', an observed mean multiplicity of about 1.6, and several key correlations. The remaining discrepancies point to areas that are not fully captured in the model. For instance, we find that the synthetic population has 1) in absolute terms too many planets by ~70%, 2) a `desert' that is too deep by ~60%, 3) a relative excess of giant planets by ~40%, 4) planet eccentricities that are on average too low by a factor of about two (median of 0.07 versus 0.15), and 5) a metallicity effect that is too weak. Finally, the synthetic planets are overall too close to the star compared to the HARPS sample. The differences allowed us to find model parameters that better reproduce the observed planet masses, for which we computed additional synthetic populations. We find that physical processes appear to be missing and that planets may originate on wider orbits than our model predicts. Mechanisms leading to higher eccentricities and slower disc-limited gas accretion also seem necessary. We advocate that theoretical models should make a quantitative comparison between the many current and future large surveys to better understand the origins of planetary systems. (Abridged.)

Figures

Figures reproduced from arXiv: 2509.09762 by Alexandre Emsenhuber, Christoph Mordasini, Erik Asphaug, Lokesh Mishra, Martin Schlecker, Maxime Marmier, Michel Mayor, Remo Burn, St\'ephane Udry, Willy Benz, Yann Alibert.

Figure 1
Figure 1. Figure 1: Mass-distance of the (unbiased) NG76longshot population at an age of 5 Gyr. Each point corresponds to one synthetic planet. The population consists of 1000 synthetic planetary systems. Colours repre￾sent the bulk composition: red are planet where the H/He mass is larger than the mass of solids (the core mass). Open circles are planets con￾taining H/He but with a core mass higher than the envelope mass. Fil… view at source ↗
Figure 2
Figure 2. Figure 2: Unbiased mass function for planets within a < 3 au at three moments in time. The blue solid line shows the distribution at 100 Myr (NG76longshot) while the black dotted one is at 20 Myr (NG76). The red dashed line additionally shows the situation at the moment when the gas disc disappears in the systems (corresponding to 3 Myr on average). The reduction of the number of low-mass (proto)planets of mainly (s… view at source ↗
Figure 4
Figure 4. Figure 4: Histograms of the number of detected planets per system in both the HARPS/Coralie surveys (red) and the biased synthetic population (black). The histogram of the synthetic system is based on 1000 samples of the synthetic population, with the solid line denoting the median while grey band shows the 95 % confidence interval. The left panel compares the absolute number of systems, while the right panel compar… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of the planet masses between the synthetic biased population (NG76, black) and the HARPS/Coralie sample (red). The data of the synthetic population is based on 1000 Monte Carlo synthetic observations, with the bold line showing the median of these, the grey region showing the 95 % confidence interval, and the thin black lines indicating ten individual realisations. Left: Kernel density estimate … view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of the planet periods between the biased syn￾thetic population (NG76, black and blue lines) and the observed HARPS/Coralie sample. The curves have the same meaning as in the left panel of [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Left: Comparison of the minimum mass (M sin i) versus orbital period of the biased synthetic 100-embryo population (NG76, black circles) and the actual HARPS/Coralie survey (red crosses; M11). Both samples have 822 systems. Right: Same but only for the synthetic population and with different colours and symbols depending on planet properties. Red circles indicate planets whose H/He envelope content is larg… view at source ↗
Figure 8
Figure 8. Figure 8: Stellar metallicity versus planet mass (M sin i) for the biased 100-embryo population (NG76, in black) and the HARPS/Coralie sur￾vey (M11, in red). The scatter plot shows one random observation of the synthetic population against the HARPS/Coralie planets. The lines show the medians in the respective range. For the synthetic planets, the values are obtained over 1000 simulated observations of the underlyin… view at source ↗
Figure 9
Figure 9. Figure 9: Cumulative distribution (left) and histogram (right) of the period ratios of adjacent planets for the biased 100-embryo population (NG76, in black), the same population but with N-body interactions extended to 100 Myr (NG76longshot, in blue), and the HARPS/Coralie surveys (M11, in red). For the synthetic planets, the values are obtained over 1000 simulated observations of the underlying population and show… view at source ↗
Figure 10
Figure 10. Figure 10: Cumulative distribution (left) and histogram (right) of the planets’ eccentricities for the biased 100-embryo population (NG76, in black), the same population but with N-body interactions extended to 100 Myr (NG76longshot, in blue), and the HARPS/Coralie surveys (M11, in red). For the synthetic planets, the values are obtained over 1000 simulated observations of the underlying population and showing the 9… view at source ↗
Figure 11
Figure 11. Figure 11: Eccentricity as a function of planet mass (M sin i) for the bi￾ased 100-embryo population (NG76, in black) and the HARPS/Coralie surveys (M11, in red). The scatter plot shows one random observation of the synthetic population against the HARPS/Coralie planets. The lines show the medians in the respective range, but one should note the low number of planets in the bins on the right. For the synthetic plane… view at source ↗
Figure 12
Figure 12. Figure 12: Histogram of the number of planets per systems in both the HARPS/Coralie surveys (red) and the optimised synthetic population NG192 (green). ��� ��� ��� ��� ��� ����� �� � ��� ��� ��� ��� ��� ��� ������������������� ����������� ����� [PITH_FULL_IMAGE:figures/full_fig_p013_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Cumulative distribution of the planet masses in the optimised synthetic population NG192 (in green), the canonical 100 embryo pop￾ulation (in black), and the observed population (in red). The resulting population, which we refer to as NG192, contains 1000 systems and has been analysed in the same manner as the original one presented in the previous section. The histogram of the number of observed planets … view at source ↗
Figure 14
Figure 14. Figure 14: Mass-period diagram comparing one random mock observation of the optimised synthetic population NG192 (in green) and the actual HARPS/Coralie planets (in red). multiplicity is a good match with the combined HARPS/Coralie sample, although the synthetic populations shows too many sys￾tems with two planets. Unlike the nominal population, however, this population can produce one system with 6 observed planets… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

225 extracted references · 4 linked inside Pith

  1. [1]

    2019, Geosciences, 9, 105

    Adibekyan , V. 2019, Geosciences, 9, 105

    2019

  2. [2]

    Z., Figueira , P., Santos , N

    Adibekyan , V. Z., Figueira , P., Santos , N. C., et al. 2013, , 554, A44

    2013

  3. [3]

    M., et al

    Ahrer , E., Queloz , D., Rajpaul , V. M., et al. 2021, , 503, 1248

    2021

  4. [4]

    2013, , 558, A109

    Alibert , Y., Carron , F., Fortier , A., et al. 2013, , 558, A109

    2013

  5. [5]

    2004, , 417, L25

    Alibert , Y., Mordasini , C., & Benz , W. 2004, , 417, L25

    2004

  6. [6]

    2011, , 526, A63

    Alibert , Y., Mordasini , C., & Benz , W. 2011, , 526, A63

    2011

  7. [7]

    2005, , 434, 343

    Alibert , Y., Mordasini , C., Benz , W., & Winisdoerffer , C. 2005, , 434, 343

    2005

  8. [8]

    2018, , 2, 873

    Alibert , Y., Venturini , J., Helled , R., et al. 2018, , 2, 873

    2018

  9. [9]

    T., Sousa , S

    Andreasen , D. T., Sousa , S. G., Tsantaki , M., et al. 2017, , 600, A69

    2017

  10. [10]

    M., Terrell , M., Tripathi , A., et al

    Andrews , S. M., Terrell , M., Tripathi , A., et al. 2018, , 865, 157

    2018

  11. [11]

    M., Wilner , D

    Andrews , S. M., Wilner , D. J., Hughes , A. M., Qi , C., & Dullemond , C. P. 2010, , 723, 1241

    2010

  12. [12]

    P., Trapman , L., et al

    Ansdell , M., Williams , J. P., Trapman , L., et al. 2018, , 859, 21

    2018

  13. [13]

    & Bai , X.-N

    Aoyama , Y. & Bai , X.-N. 2023, , 946, 5

    2023

  14. [14]

    Ayliffe , B. A. & Bate , M. R. 2012, , 427, 2597

    2012

  15. [15]

    2018, , 615, A175

    Barbato , D., Sozzetti , A., Desidera , S., et al. 2018, , 615, A175

    2018

  16. [16]

    2016, , 205, 77

    Baruteau , C., Bai , X., Mordasini , C., & Molli \`e re , P. 2016, , 205, 77

    2016

  17. [17]

    2011, , 528, A2

    Ben \' tez-Llambay , P., Masset , F., & Beaug \'e , C. 2011, , 528, A2

    2011

  18. [18]

    P., Ranc , C., & Fernandes , R

    Bennett , D. P., Ranc , C., & Fernandes , R. B. 2021, , 162, 243

    2021

  19. [19]

    2014, in Protostars and Planets VI, ed

    Benz , W., Ida , S., Alibert , Y., Lin , D., & Mordasini , C. 2014, in Protostars and Planets VI, ed. H. Beuther , R. S. Klessen , C. P. Dullemond , & T. Henning , 691

    2014

  20. [20]

    & Ivanova , A

    Bertaux , J.-L. & Ivanova , A. 2022, , 512, 5552

    2022

  21. [21]

    2020, , 643, A66

    Bitsch , B., Trifonov , T., & Izidoro , A. 2020, , 643, A66

    2020

  22. [22]

    Bodenheimer , P., Hubickyj , O., & Lissauer , J. J. 2000, , 143, 2

    2000

  23. [23]

    & Pollack , J

    Bodenheimer , P. & Pollack , J. B. 1986, , 67, 391

    1986

  24. [24]

    2009, , 496, 527

    Bouchy , F., Mayor , M., Lovis , C., et al. 2009, , 496, 527

    2009

  25. [25]

    2024, Nature Astronomy, 8, 463

    Burn , R., Mordasini , C., Mishra , L., et al. 2024, Nature Astronomy, 8, 463

    2024

  26. [26]

    2021, , 656, A72

    Burn , R., Schlecker , M., Mordasini , C., et al. 2021, , 656, A72

    2021

  27. [27]

    P., Marcy , G

    Butler , R. P., Marcy , G. W., Vogt , S. S., et al. 2003, , 582, 455

    2003

  28. [28]

    P., Marcy , G

    Butler , R. P., Marcy , G. W., Vogt , S. S., et al. 2002, , 578, 565

    2002

  29. [29]

    P., Tinney , C

    Butler , R. P., Tinney , C. G., Marcy , G. W., et al. 2001, , 555, 410

    2001

  30. [30]

    P., Vogt , S

    Butler , R. P., Vogt , S. S., Marcy , G. W., et al. 2000, , 545, 504

    2000

  31. [31]

    P., Wright , J

    Butler , R. P., Wright , J. T., Marcy , G. W., et al. 2006, , 646, 505

    2006

  32. [32]

    N., & Davies , M

    Carrera , D., Raymond , S. N., & Davies , M. B. 2019, , 629, L7

    2019

  33. [33]

    D., Butler , R

    Carter , B. D., Butler , R. P., Tinney , C. G., et al. 2003, , 593, L43

    2003

  34. [34]

    2006, , 180, 496

    Chambers , J. 2006, , 180, 496

    2006

  35. [35]

    2018, , 865, 30

    Chambers , J. 2018, , 865, 30

    2018

  36. [36]

    Chambers , J. E. 1999, , 304, 793

    1999

  37. [37]

    2022, , 514, 3844

    Charalambous , C., Teyssandier , J., & Libert , A.-S. 2022, , 514, 3844

    2022

  38. [38]

    B., Matsumura , S., & Rasio , F

    Chatterjee , S., Ford , E. B., Matsumura , S., & Rasio , F. A. 2008, , 686, 580

    2008

  39. [39]

    2018, , 614, A16

    Cheetham , A., S \'e gransan , D., Peretti , S., et al. 2018, , 614, A16

    2018

  40. [40]

    O., Nesvorn \'y , D., & Flock , M

    Chrenko , O., Chametla , R. O., Nesvorn \'y , D., & Flock , M. 2022, , 666, A63

    2022

  41. [41]

    J., Gendrin , A., & Sotomayor , M

    Clarke , C. J., Gendrin , A., & Sotomayor , M. 2001, , 328, 485

    2001

  42. [42]

    Coleman , G. A. L. & Nelson , R. P. 2014, , 445, 479

    2014

  43. [43]

    Correia , A. C. M., Udry , S., Mayor , M., et al. 2009, , 496, 521

    2009

  44. [44]

    G., Harris , R

    Cox , E. G., Harris , R. J., Looney , L. W., et al. 2017, , 851, 83

    2017

  45. [45]

    2006, , 181, 587

    Crida , A., Morbidelli , A., & Masset , F. 2006, , 181, 587

    2006

  46. [46]

    P., Hunziker , S., et al

    Cugno , G., Quanz , S. P., Hunziker , S., et al. 2019, , 622, A156

    2019

  47. [47]

    2022, Nature Astronomy [ [arXiv] 2204.00633 ]

    Currie , T., Lawson , K., Schneider , G., et al. 2022, Nature Astronomy [ [arXiv] 2204.00633 ]

    Pith/arXiv arXiv 2022

  48. [48]

    Dawson , R. I. & Johnson , J. A. 2018, , 56, 175

    2018

  49. [49]

    Z., et al

    Delgado Mena , E., Tsantaki , M., Adibekyan , V. Z., et al. 2017, , 606, A94

    2017

  50. [50]

    B., S \'e gransan , D., Dumusque , X., et al

    Delisle , J. B., S \'e gransan , D., Dumusque , X., et al. 2018, , 614, A133

    2018

  51. [51]

    & Barbieri , M

    Desidera , S. & Barbieri , M. 2007, , 462, 345

    2007

  52. [52]

    F., S \'e gransan , D., Udry , S., et al

    D \' az , R. F., S \'e gransan , D., Udry , S., et al. 2016, , 585, A134

    2016

  53. [53]

    M., Mordasini , C., Klahr , H., Alibert , Y., & Henning , T

    Dittkrist , K. M., Mordasini , C., Klahr , H., Alibert , Y., & Henning , T. 2014, , 567, A121

    2014

  54. [54]

    2011, , 535, A55

    Dumusque , X., Lovis , C., S \'e gransan , D., et al. 2011, , 535, A55

    2011

  55. [55]

    2006, , 447, 1159

    Eggenberger , A., Mayor , M., Naef , D., et al. 2006, , 447, 1159

    2006

  56. [56]

    2023, European Physical Journal Plus, 138, 181

    Emsenhuber , A., Mordasini , C., & Burn , R. 2023, European Physical Journal Plus, 138, 181

    2023

  57. [57]

    2021 a , , 656, A69

    Emsenhuber , A., Mordasini , C., Burn , R., et al. 2021 a , , 656, A69

    2021

  58. [58]

    2021 b , , 656, A70

    Emsenhuber , A., Mordasini , C., Burn , R., et al. 2021 b , , 656, A70

    2021

  59. [59]

    L., Bennett , D

    ExoPAG Science Interest Group , Christiansen , J. L., Bennett , D. P., et al. 2023, arXiv e-prints, arXiv:2304.12442

    Pith/arXiv arXiv 2023

  60. [60]

    C., Lissauer , J

    Fabrycky , D. C., Lissauer , J. J., Ragozzine , D., et al. 2014, , 790, 146

    2014

  61. [61]

    P., Vogt , S

    Feng , F., Butler , R. P., Vogt , S. S., et al. 2022, , 262, 21

    2022

  62. [62]

    2008, , 101, 171

    Ferraz-Mello , S., Rodr \' guez , A., & Hussmann , H. 2008, , 101, 171

    2008

  63. [63]

    2009, , 703, 1545

    Fischer , D., Driscoll , P., Isaacson , H., et al. 2009, , 703, 1545

    2009

  64. [64]

    A., Marcy , G

    Fischer , D. A., Marcy , G. W., Butler , R. P., Vogt , S. S., & Apps , K. 1999, , 111, 50

    1999

  65. [65]

    A., Marcy , G

    Fischer , D. A., Marcy , G. W., Butler , R. P., et al. 2001, , 551, 1107

    2001

  66. [66]

    Fischer , D. A. & Valenti , J. 2005, , 622, 1102

    2005

  67. [67]

    J., Mulders , G

    Flock , M., Turner , N. J., Mulders , G. D., et al. 2019, , 630, A147

    2019

  68. [68]

    Ford , E. B. & Rasio , F. A. 2008, , 686, 621

    2008

  69. [69]

    Fortier , A., Alibert , Y., Carron , F., Benz , W., & Dittkrist , K. M. 2013, , 549, A44

    2013

  70. [70]

    H., & Ballering , N

    G \'a sp \'a r , A., Rieke , G. H., & Ballering , N. 2016, , 826, 171

    2016

  71. [71]

    1997, , 285, 403

    Gonzalez , G. 1997, , 285, 403

    1997

  72. [72]

    & Lissauer , J

    Greenzweig , Y. & Lissauer , J. J. 1992, , 100, 440

    1992

  73. [73]

    Y., Bohn , A

    Haffert , S. Y., Bohn , A. J., de Boer , J., et al. 2019, Nature Astronomy, 3, 749

    2019

  74. [74]

    C., Bou \'e , G., Laskar , J., Delisle , J

    Hara , N. C., Bou \'e , G., Laskar , J., Delisle , J. B., & Unger , N. 2019, , 489, 738

    2019

  75. [75]

    J., Andrews , S

    Harris , R. J., Andrews , S. M., Wilner , D. J., & Kraus , A. L. 2012, , 751, 115

    2012

  76. [76]

    Hope , A. C. A. 1968, Journal of the Royal Statistical Society. Series B (Methodological), 968, 582

    1968

  77. [77]

    W., Johnson , J

    Howard , A. W., Johnson , J. A., Marcy , G. W., et al. 2011, , 730, 10

    2011

  78. [78]

    Hunter , J. D. 2007, Computing in Science and Engineering, 9, 90

    2007

  79. [79]

    & Lin , D

    Ida , S. & Lin , D. N. C. 2004, , 604, 388

    2004

  80. [80]

    & Makino , J

    Ida , S. & Makino , J. 1993, , 106, 210

    1993

Showing first 80 references.