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REVIEW 3 major objections 7 minor 299 references

Cold Jupiters do not strongly boost close-in small planets at typical stellar mass and metallicity; rates rise only when the giant leaves a stable inner zone.

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2026-07-13 03:53 UTC pith:3MD5FAOE

load-bearing objection Solid, homogeneous 137-star re-analysis that settles the average-metallicity ISP–CJ null correlation and quantifies the dynamical-stability cut; Super-Earth rates remain soft but the Neptune results and literature reconciliation are usable. the 3 major comments →

arxiv 2607.09320 v1 pith:3MD5FAOE submitted 2026-07-10 astro-ph.EP

The GAPS Programme with HARPS-N at TNG LXXVII. Occurrence rates of small close-in planets in the presence of cold Jupiters

A. Ruggieri , S. Desidera , M. Pinamonti , D. Barbato , A. Sozzetti , A. S. Bonomo , L. Naponiello , M. Damasso
show 13 more authors
S. Benatti I. Carleo R. Gratton K. Biazzo A. F. Lanza G. Piccinini N. Nari G. Mantovan J. Maldonado D. Nardiello A. Fiorenzano A. Ghedina A. Bignamini
This is my paper
classification astro-ph.EP
keywords cold Jupitersoccurrence ratesradial velocitiesclose-in small planetsSuper-EarthsNeptunesplanetary architecturesdynamical stability
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 Solar System–like architectures—small planets inside and a cold giant outside—are common. The authors assemble a homogeneous sample of 137 stars already known to host a cold Jupiter, re-analyse all available radial-velocity data with a single pipeline, and measure how often small close-in planets also appear. At average stellar mass and metallicity they find occurrence rates of roughly 5–13 % for Neptunes and 11–16 % for Super-Earths in the hot and warm zones; these rates are not markedly higher than field averages. The decisive factor is dynamical room: when the outer giant’s Hill sphere leaves an inner region stable out to at least 1.5 au, warm small planets become far more common. The same sample also shows a mild excess of hot Jupiters around cold-Jupiter hosts. The result tells us that outer giants neither guarantee nor forbid inner small planets; stability of the inner disk is what matters most.

Core claim

In a volume-limited, consistently re-analysed sample of 137 cold-Jupiter hosts, the occurrence of close-in small planets (P < 400 d, 3–32 M⊕) is statistically compatible with field rates at typical stellar metallicity and mass; the rates rise sharply only for systems whose outer giant leaves a dynamically stable inner zone (a_lim ≥ 1.5 au). Hot Jupiters appear modestly over-abundant relative to literature field values.

What carries the argument

Detection-completeness maps obtained by injecting synthetic Keplerian signals into the post-fit residuals of every star, then averaging to produce a survey-wide map from which occurrence rates are inverted via Poisson or binomial statistics.

Load-bearing premise

The Super-Earth occurrence rates rest on very low completeness (∼16 % warm, ∼5 % cool) that is dominated by a handful of intensively monitored systems; if those systems are atypical, the quoted rates become unreliable.

What would settle it

A larger, uniformly sampled RV survey of cold-Jupiter hosts that reaches ≥50 % completeness for 3–10 M⊕ planets at 10–100 d and still finds no excess of warm Super-Earths when a_lim ≥ 1.5 au would falsify the stability-driven claim.

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

If this is right

  • Solar-System analogues are not the default outcome of cold-Jupiter systems at solar metallicity; formation models must allow both architectures.
  • Dynamical stability of the inner disk (quantified by a_lim) is a stronger predictor of small-planet occurrence than the mere presence of an outer giant.
  • Hot Jupiters may preferentially form or migrate in systems that already possess outer giants, offering a testable demographic signature.
  • Occurrence-rate comparisons across surveys must adopt identical mass–period and periastron cuts, or apparent contradictions will persist.

Where Pith is reading between the lines

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

  • If the stability cut is the true driver, surveys that select cold Jupiters by a > 3 au or e < 0.2 should recover systematically higher ISP rates than surveys that mix close-in or eccentric giants.
  • The mild HJ excess, if confirmed, would favour high-eccentricity migration channels that require an external perturber rather than pure disk migration.
  • Metallicity and stellar-mass dependence reported in other works may be secondary: once a_lim is controlled, the residual correlation with [Fe/H] could shrink.

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 / 7 minor

Summary. This paper constructs a homogeneous sample of 137 FGK stars hosting cold Jupiters (CJs; a_peri > 1 au, m sin i > 0.1 M_J), compiles multi-instrument RV data (including unpublished HARPS-N/GAPS observations), and re-fits all systems consistently with PyORBIT. After confirming 213 known planets and reporting six new candidates, the authors derive RV detection-completeness maps with two independent injection-recovery codes and invert occurrence rates for inner small planets (ISPs; P < 400 d, 3–31.7 M_⊕). They report ~5%/13%/12% for hot/warm/cool Neptunes and ~11%/16% for hot/warm Super-Earths (cool Super-Earths unconstrained), find significantly higher warm-planet rates when the outer giant leaves a dynamically stable inner zone (a_lim ≥ 1.5 au), and, via Bayes’ theorem with published F_ISP and F_CJ, conclude there is no strong ISP–CJ correlation at average stellar mass and metallicity. A secondary analysis of systems with inner giants suggests a possible (~2σ) excess of hot Jupiters among CJ hosts.

Significance. If the rates and null-correlation result hold, the work supplies one of the largest, most uniformly analyzed RV-selected CJ-host samples for testing Solar-System-analog architectures and places a useful empirical bound against claims of near-100% ISP–CJ coupling. Strengths include: (i) explicit, reproducible selection cuts; (ii) cross-validation of completeness with two independent codes that agree within 1–2σ; (iii) a physically motivated dynamical split (a_lim) that quantifies the role of outer-giant eccentricity, mass, and separation; and (iv) a transparent Bayes conversion that reconciles F_ISP|CJ with complementary transit+RV surveys (Bonomo et al. 2023, 2025) and with Rosenthal et al. (2022). The homogeneous re-analysis and public CDS tables of parameters and data counts further increase the paper’s lasting value for demographic studies.

major comments (3)
  1. Table 1 and §7.1: Warm Super-Earth completeness is only ~16% (cool ~5%) and is dominated by a handful of intensively monitored systems (HD 219134, HD 164922, HD 39091, etc.). The authors correctly flag that these rates “should be taken with caution,” yet the abstract and §8 still quote ~11% and ~16% on equal footing with the better-sampled Neptune bins. Because the central “no strong ISP–CJ correlation” claim is most robust for Neptunes and for the a_lim split, the abstract and conclusions should either (a) lead with Neptune rates and the dynamical result, or (b) attach an explicit completeness caveat to the Super-Earth numbers so they are not over-read as field demographics.
  2. §7.4.1 and Table 3: The claimed HJ excess relative to Howard et al. (2010) and Wittenmyer et al. (2020) rests on n = 3–4 detections and is only ~1.7–2.0σ. The abstract’s phrasing (“suggest that HJs may be more commonly associated with external giants”) is appropriately cautious, but the body should state the small-number limitation more prominently (e.g., next to the occurrence values) and avoid language that could be read as a firm demographic enhancement. A simple bootstrap or leave-one-out check on the three/four systems would strengthen the claim or clarify that it remains only a hint.
  3. §7.2, Eqs. (4)–(5): For multi-CJ systems the authors adopt the innermost outer giant when computing R_H and a_lim, noting that mutual interactions “may cause additional zones of dynamical instability” but deferring a case-by-case treatment. Because the a_lim ≥ 1.5 au result is one of the paper’s strongest positive findings (all 13 warm ISPs lie in the stable subsample; 3.8σ for warm Neptunes), a short sensitivity test—e.g., excluding multi-giant systems, or using the most dynamically aggressive giant—would show whether the significance is robust or partly driven by that simplification.
minor comments (7)
  1. Abstract vs. §2: The abstract defines CJs as “a > 1 au” while the selection criterion is a_peri > 1 au. Align the wording to avoid confusion with works that use semi-major axis alone.
  2. Abstract / §6.3: “Six new candidates” are announced, but two (HD 170469, HD 204941) are excluded from the statistical sample as non-robust. State the split (four robust + two tentative, or similar) already in the abstract or early results so the occurrence-rate n values are unambiguous.
  3. Fig. 5 and Table 1: Completeness is quoted as sample averages; a brief note on the star-to-star dispersion (or a supplementary histogram of per-target C) would help readers judge how much a few deep systems pull the mean.
  4. §4: The two completeness codes use different detection criteria (ΔBIC < −10 vs. FAP ≤ 10^−3) and different injection grids (100×100×10 vs. 10×10×100). The agreement within 1–2σ is reassuring; a one-sentence statement of which code’s maps are shown in Fig. 5 (and that rates always come from the first code) would remove residual ambiguity.
  5. §5 / Appendix A: Several systems (HD 156098, HD 47186) receive substantially revised orbits relative to the literature. A short table summarizing “confirmed / revised / new candidate / discarded” for all 137 targets would improve navigability of the appendix material.
  6. Typographical / consistency: mass bounds alternate between strict and non-strict inequalities (3 < m sin i < 31.7 vs. 3 ≤ m sin i ≤ 31.7); “lanetary” in Appendix A (HD 204941); “DH 163607” in Table B.2. Standard copy-edit pass will catch these.
  7. §7.3.3: The Bayes conversion uses F_ISP from Rosenthal et al. (2022) with slightly different ISP mass/period cuts than Bonomo et al. (2023). The authors note the difference is small; quantifying the shift under a common cut (even approximately) would make the 0.1–0.6σ agreement fully transparent.

Circularity Check

0 steps flagged

No significant circularity: occurrence rates are inverted from independent detections and injection-based completeness maps; self-citations supply data or comparison benchmarks only.

full rationale

The paper's central results (occurrence rates η or F for hot/warm/cool Neptunes and Super-Earths, and the a_lim dynamical split) are obtained by (1) homogeneous Keplerian fitting of archival + new HARPS-N RVs, (2) injection-recovery completeness maps C(ΔP,M) generated on a period-mass grid with two independent codes, and (3) inversion of the Poisson (or binomial) distribution for the observed counts n. Completeness is computed from synthetic signals injected into post-fit residuals and is therefore independent of the final rates; the rates are not fitted free parameters that are later re-labeled as predictions. Subgroup splits (e.g., a_lim ≥ 1.5 au from the Hill-radius formula) recompute the same inversion on data-defined subsets and do not close a definitional loop. Literature comparisons (Rosenthal et al. 2022; Barbato et al. 2018; Bonomo et al. 2023/2025) use external absolute rates or Bayes conversion with externally measured F_ISP and F_CJ; overlapping authorship on some comparison papers is ordinary and does not make those numbers load-bearing inputs to the present derivation. No uniqueness theorem, ansatz, or self-definitional identity is invoked. The low Super-Earth completeness is explicitly flagged by the authors and does not constitute circularity. The derivation chain is therefore self-contained against its own inputs.

Axiom & Free-Parameter Ledger

3 free parameters · 3 axioms · 0 invented entities

The central demographic claim rests on standard RV detection statistics, conventional planet-mass/period boundaries, and the dynamical stability formula taken from Murray & Dermott; no new physical entities are postulated. Free parameters are limited to analysis choices (BIC threshold, a_lim cut, mass/period bin edges) that are stated and tested.

free parameters (3)
  • BIC detection threshold = ΔBIC > 10
    ΔBIC > 10 (Kass & Raftery) used to accept new candidates; changes which planets enter the occurrence count.
  • a_lim stability cut = 1.5 au
    1.5 au chosen as the boundary between “stable” and “unstable” inner zones; drives the strongest reported difference in warm-planet rates.
  • mass/period bin edges = 3–10–31.7 M⊕; 1–10–100–400 d
    3/10/31.7 M⊕ and 1/10/100/400 d define the reported occurrence cells; conventional but still free analysis choices.
axioms (3)
  • domain assumption Detection completeness can be estimated by injecting circular Keplerians into residual time series and recovering them with the same BIC/FAP threshold used for real signals.
    Standard in RV occurrence studies (Pinamonti, Barbato, etc.); eccentricity effects are argued to be minor for short-period low-mass planets.
  • domain assumption The Hill-radius stability limit a_lim = a(1–e)–2√3 R_H correctly identifies systems whose inner regions are dynamically safe for small planets.
    Taken from Murray & Dermott (1999); multi-giant interactions are acknowledged but not modeled case-by-case.
  • domain assumption Cold Jupiters are defined by m sin i > 0.1 M_J and a_peri > 1 au (a < 10 au); ISPs by 3 ≤ m sin i ≤ 31.7 M⊕ and P < 400 d.
    Matches several prior works; different definitions would change both sample and rates.

pith-pipeline@v1.1.0-grok45 · 46116 in / 2674 out tokens · 31705 ms · 2026-07-13T03:53:25.303266+00:00 · methodology

0 comments
read the original abstract

Context. The architecture of our Solar System, with inner small planets (ISPs) and outer giants, may or may not be common. Understanding whether a correlation exists between ISPs and outer cold giants is key to evaluating how common systems with a similar architecture to our own are. Aims. This study aims to build a large, homogeneous sample of systems hosting cold Jupiters (CJs, a > 1 au, msini > 0.1 M$_J$) detected via radial velocities (RVs), and to assess the presence of additional ISPs (P < 400 d, 3 < msini < 31.7 M$_{\oplus}$), studying the correlation between these two types of objects. Methods. We selected 137 stars known to host a CJ, including 23 which also harbor a hot Jupiter and were treated separately. Data from various instruments were compiled, including unpublished data gathered with HARPS-N within the GAPS program, and consistently fitted using PyORBIT. We derived RV detection maps and calculated occurrence rates for ISPs, cross-validating results with two independent codes. The sample was divided into subgroups to evaluate how system parameters influence planet occurrence. Results. We confirmed the 213 already known planets in the 137 systems and also identified six new candidates. We divided them, based on mass and period, into Neptunes (10 < m sin i < 31.7 M$_{\oplus}$) and Super-Earths (3 < m sin i < 10 M$_{\oplus}$), and into hot (1 < P < 10 d), warm (10 < P < 100 d), and cool (100 < P < 400 d). We found occurrences of 5%, 13%, and 12% for hot, warm, and cool Neptunes, respectively, and 11% and 16% for hot and warm Super-Earths, respectively. Systems with dynamically stable inner regions show higher rates of small planets. These findings are consistent with previous studies showing no strong correlation between ISPs and CJs at average stellar metallicity and mass, and suggest that HJs may be more commonly associated with external giants.

Figures

Figures reproduced from arXiv: 2607.09320 by A. Bignamini, A. Fiorenzano, A. F. Lanza, A. Ghedina, A. Ruggieri, A. S. Bonomo, A. Sozzetti, D. Barbato, D. Nardiello, G. Mantovan, G. Piccinini, I. Carleo, J. Maldonado, K. Biazzo, L. Naponiello, M. Damasso, M. Pinamonti, N. Nari, R. Gratton, S. Benatti, S. Desidera.

Figure 1
Figure 1. Figure 1: Number of data points used in our RV analysis taken with each instrument. The last bar on the right represents the total. can be computed simply as the average of the completeness for each target: C(∆P,M) = 1 N X N i=0 Ci(∆P,M). (1) Given the completeness C, the planetary occurrence rates focc can be computed from the number of detected planets n and the number of stars in the sample. This can be done in t… view at source ↗
Figure 2
Figure 2. Figure 2: Minimum masses vs orbital period of all the planets in our global sample, color-coded for eccentricity. The green box represents the region of the parameter space that we consid￾ered for our occurrence rates analysis (see Sec￾tion 7). giants in multi-giant systems rather than representatives of the field-star distribution at those separations. Even so, this is consis￾tent with both the demographics of RV-s… view at source ↗
Figure 3
Figure 3. Figure 3: Color-magnitude diagram (top), mass (center), and [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distributions of (from top left to bottom right) CJs semi-major axis, eccentricity, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Detection map of our sample (excluding system with in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

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