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Outer cold Jupiters do not strongly promote or suppress inner small planets at average stellar mass and metallicity, but dynamically quiet giants leave room for more warm companions.

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2026-07-14 15:14 UTC pith:3MD5FAOE

load-bearing objection Solid, homogeneous RV demographics paper that cleanly measures ISP rates around CJ hosts and finds no strong overall correlation at average metallicity/mass; the alim split is secondary and not load-bearing. the 2 major comments →

arxiv 2607.09320 v2 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 Jupitersinner small planetsoccurrence ratesradial velocitiesplanetary architecturesdynamical stabilityhot Jupiterssuper-Earths
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 builds a homogeneous sample of 137 stars that already host cold Jupiters and asks how often those same stars also host small, close-in planets. By compiling radial-velocity data from many instruments, fitting every system the same way, and injecting synthetic signals to measure completeness, the authors measure occurrence rates of roughly 5 percent for hot Neptunes, 13 percent for warm Neptunes, 12 percent for cool Neptunes, 11 percent for hot super-Earths and 16 percent for warm super-Earths. The rates rise when the outer giant leaves a dynamically stable zone inside about 1.5 au, showing that the giant’s mass, distance and eccentricity matter more than its mere presence. The same analysis finds no strong overall correlation between inner small planets and cold Jupiters for typical solar-type stars, while hinting that hot Jupiters may be more common when an outer giant is also present. The result matters because it tells us whether Solar-System-like architectures are typical or exceptional, and it supplies a clean observational benchmark for formation models that try to predict how outer giants shape the inner planetary system.

Core claim

Around stars that already host cold Jupiters, the occurrence of small close-in planets is moderate (a few to roughly 15 percent depending on mass and period) and shows no strong overall correlation with the presence of the outer giant at average stellar metallicity and mass; the rates become significantly higher only when the outer giant leaves the inner region dynamically stable.

What carries the argument

The dynamical-stability boundary alim = a1(1−e1)−2√3 RH, which folds the outer giant’s mass, semi-major axis and eccentricity into a single length that separates systems whose inner zones remain unperturbed from those that do not.

Load-bearing premise

The claim that a single cut at alim of 1.5 au cleanly divides systems into those whose outer giants leave the inner regions free of dynamical interference and those that do not.

What would settle it

A larger, uniformly sampled set of cold-Jupiter hosts in which warm Neptunes appear at comparable rates on both sides of the alim = 1.5 au divide, or in which the conditional probability of an inner small planet given an outer giant rises well above the unconditional rate once completeness is properly accounted for.

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

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

2 major / 6 minor

Summary. The paper constructs a homogeneous sample of 137 FGK stars hosting cold Jupiters (CJs; a_peri > 1 au, m sin i > 0.1 M_J), of which 114 form a regular sample without inner giants and 23 host additional inner gas giants treated separately. Archival RVs from many instruments plus unpublished HARPS-N GAPS data are fitted uniformly with PyORBIT; six new candidates are reported (two flagged non-robust and excluded from statistics). Detection completeness is mapped with two independent injection-recovery codes that agree within 1–2σ, and occurrence rates of inner small planets (ISPs; 3 ≤ m sin i ≤ 31.7 M_⊕, P ≤ 400 d) are inverted as planets per star. Headline rates are ~5/13/12% for hot/warm/cool Neptunes and ~11/16% for hot/warm super-Earths; systems with a_lim ≥ 1.5 au show a significant excess of warm planets. Comparisons with Rosenthal et al. (2022) and Bonomo et al. (2023, 2025) via Bayes’ theorem indicate no strong ISP–CJ correlation at average stellar mass and metallicity, while hot-Jupiter rates in the full sample are formally ~2σ higher than literature field values.

Significance. If the rates and the lack of a strong ISP–CJ correlation hold, the work supplies one of the largest, most homogeneous RV-based conditional occurrence measurements for Solar-System-like architectures and helps resolve the tension between earlier claims of near-100% correlation and more recent null results. Strengths include dual-code cross-validation of completeness, explicit flagging of low super-Earth completeness and non-robust candidates, uniform re-analysis of multi-instrument data, and transparent literature comparisons that reuse external F_ISP and F_CJ. The a_lim dynamical-stability test quantifies an expected architectural dependence without being required for the headline rates. The result is of clear interest to formation and migration theory and to the design of future RV and transit surveys.

major comments (2)
  1. §7.2 and Eqs. (4)–(5): the warm-planet excess is reported only for the binary split a_lim ≥ 1.5 au (3.8σ for warm Neptunes). Because a_lim folds mass, eccentricity and semi-major axis into a single threshold, and multi-giant interactions are neglected by construction, a continuous or multi-threshold test (or a leave-one-out check on the 13 warm detections) would strengthen the claim that dynamical stability, rather than a correlated stellar property, drives the excess. The paper already notes that hot-planet rates are insensitive to the cut; extending that robustness statement to the warm bin would make the secondary result more secure.
  2. Table 1 and §7.1: cool super-Earth completeness is only 4.8% and warm-SE completeness 15.9%, with all three warm SEs coming from a handful of intensively monitored systems. The text correctly cautions that the SE rates should be taken carefully, yet they still appear in the abstract and conclusions at the same footing as the better-constrained Neptune rates. Either demote the SE numbers to upper limits / exploratory values or quantify the bias introduced by the best-sampled systems more explicitly (e.g., by a jackknife that removes HD 219134, HD 164922, etc.).
minor comments (6)
  1. Abstract vs. Table 1: abstract quotes ~5%, ~13%, ~12% (Neptunes) and ~11%, ~16% (SEs); Table 1 gives 4.9, 13.7, 10.1 and 11.5, 16.5. Align the rounded values or state that they are approximate.
  2. §2 criterion 1 and §7.3.1: CJ definition uses a_peri > 1 au here but a > 1 au (or 0.23–10 au) in some comparison works. A short explicit statement that the period-valley gap makes the difference negligible would help readers.
  3. Figure 5 caption and §4: state clearly which of the two codes produced the displayed map and note that the second code yields systematically lower low-mass completeness (already mentioned in text).
  4. Appendix A: for HD 204941 and HD 170469 the candidates are correctly excluded from statistics, but a one-sentence summary table of which new candidates enter the occurrence calculation would improve clarity.
  5. §7.4.1: the ~2σ excess of HJs relative to Howard et al. (2010) and Wittenmyer et al. (2020) is interesting; a brief note on whether the excess survives after removing the 23 systems that were selected precisely because they host inner giants would avoid any selection-loop concern.
  6. Typographical: “lanetary” → “planetary” (App. A, HD 204941); occasional “msini” vs “m sin i” inconsistency; “alim” sometimes written without subscript.

Circularity Check

0 steps flagged

No significant circularity: occurrence rates are inverted from observed counts plus independently computed completeness maps; literature comparisons use external benchmarks.

full rationale

The central derivation (Sect. 4 and 7) injects synthetic Keplerians into post-fit residuals on a period-mass grid, measures recovery fractions with two independent codes (BIC and FAP criteria), averages to a sample completeness map C, then inverts the Poisson (or binomial) distribution for η (or F) given the number of detections n. No free parameter is fitted to the target occurrence rates themselves, and the dual-code cross-check plus explicit low-completeness caveats for cool super-Earths keep the numbers self-contained. The alim dynamical cut (Eqs. 4–5) is a secondary post-hoc split that quantifies an expected warm-planet excess; it is not used to force the headline ISP–CJ rates or the Bayes conversion that recovers F_CJ|ISP. Self-citations (GAPS papers, Barbato et al. 2018, Bonomo et al. 2025) supply sample context or external F_CJ values but do not close a definitional loop on the new 114-star rates. The paper is therefore essentially non-circular.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The demographic claim rests on standard RV detection statistics, conventional mass/period boundaries, and a dynamical-stability proxy whose numerical thresholds are chosen by the authors. No new physical entities are postulated; free parameters are the bin edges and the alim cut that drive the subgroup analysis.

free parameters (4)
  • alim threshold = 1.5 au
    Binary split of the sample at alim = 1.5 au (§7.2) is chosen by hand to match the periastron > 1 au selection; the warm-planet excess significance depends on this value.
  • eccentricity cut for subgroups = 0.3
    e = 0.3 used to separate low- vs moderate-eccentricity CJs (§7.2); justified by a visual minimum in the histogram but not derived from first principles.
  • super-Jupiter mass cut = 4 MJ
    msini = 4 MJ used to split the sample (§7.2); conventional but arbitrary for the demographic test.
  • ISP mass/period bin edges = as stated in §7.1
    Hot/warm/cool (1–10 / 10–100 / 100–400 d) and SE/Neptune (3–10 / 10–31.7 M⊕) boundaries are literature conventions adopted without re-optimisation.
axioms (4)
  • domain assumption RV injection-recovery with fixed e = 0 and random epoch adequately measures completeness for low-mass short-period planets.
    Stated in §4; justified by prior work (Pinamonti et al. 2017) but remains an approximation.
  • domain assumption The Hill-radius stability limit alim = a(1−e)−2√3 RH correctly ranks dynamical influence of the outer giant on the inner system.
    Eqs. 4–5, §7.2; multi-planet interactions among outer giants are acknowledged but ignored.
  • domain assumption Stars with log R'HK < −4.8 and V < 10 are sufficiently inactive and bright for reliable low-mass planet searches.
    Sample selection §2; activity is later checked but the cut itself is conventional.
  • standard math Poisson/binomial inversion of counts and average completeness yields unbiased occurrence rates.
    Eqs. 2–3, §4; standard in the field.

pith-pipeline@v1.1.0-grok45 · 46088 in / 2804 out tokens · 28952 ms · 2026-07-14T15:14:37.498330+00:00 · methodology

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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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