Diophantine Astrophysics arithmodynamics · a candidate field at the boundary of number theory and celestial mechanics

Which orbital architectures observed in the universe are forced by number-theoretic constraints, rather than selected by initial conditions or dynamics?

v1.5 · 2026-05-21 draft · day 3 data: NASA Exoplanet Archive · Gaia DR3 · JPL SBDB (asteroids + TNOs) 5933 planets · 115 k stars · 100 k asteroids · 5 k TNOs

Contents
  1. Why this is a real gap
  2. The conjectures (with one major reformulation)
  3. Test 1 — partial-quotient distribution vs Gauss-Kuzmin
  4. Test 2 — dynamical-age stratification
  5. Test 3 — RV-only control for selection bias
  6. Test 4 — Conjecture II at Farey-9
  7. Test 5 — Khinchin and Lévy constants flips Conj I
  8. Test 6 — Conjecture II with larger alphabets and Markov-chain test
  9. Test 7 — Mixture fit and pile-up structure quantifies Conj I″
  10. Test 8 — Physical mixture and Lithwick–Wu asymmetry physical scaling fails; LW signature confirmed
  11. Test 9 — Asymmetric LW pair model fit LW asymmetry detected at z ≈ 2.7
  12. Test 10 — Joint LW + broad-Farey mixture Gauss baseline rejected; LW at z ≈ 3.3
  13. Test 11 — LW pair test at 2nd-order MMRs same direction, ε scaled by ~⅓
  14. Test 12 — Conjecture III on Gaia local sample naive Farey-ratio form not supported
  15. Test 13 — Asteroid belt as parallel population Kirkwood gaps recovered; population-contrast result
  16. Test 14 — Kuiper belt (TNOs) as mixed-mode population Plutinos at z=+23; first noble-leaning sample
  17. Synthesis and what to do next
  18. Reproduction
  19. Sources & code (downloads)
  20. Bibliography
  21. Changelog

1. Why this is a real gap

Three established fields each see one face of the same elephant and don't talk to each other.

The bridge: the observed period-ratio distribution is the pushforward of the cosmic initial-condition measure through some sequence of number-theoretic filters. Day-1 assumed the filter was KAM stability (favouring noble irrationals). Day-2 falsifies that and identifies the dominant filter as resonance trapping during migration (favouring low-order rationals).

↑ back to top

2. The conjectures

Conjecture I (original) — Khinchin Selection falsified, see Test 5

Long-lived multi-planet systems concentrate on period ratios whose continued-fraction expansion has bounded partial quotients — i.e. more noble, more KAM-stable irrationals.

The per-ratio Khinchin and Lévy constants both shift in the opposite direction at z ≈ +10 (Test 5).

Conjecture I″ — Resonance Excess strongly supported

Observed period ratios show systematically larger partial-quotient geometric mean (Khinchin) and faster-growing convergent denominators (Lévy) than the Gauss-Kuzmin null. The 1-D marginal density of {r} = r − ⌊r⌋ deviates from the Gauss measure at χ² = 111 on 19 dof (Test 7), with localizable pile-ups at 3:2 and just-below-integer (Lithwick–Wu). The dominant filter on period ratios is migration-driven resonance trapping, not KAM-stability suppression of resonant tori.

Conjecture II — Resonance Lattice Rigidity supported across Farey-9, 12, 15

For an n-planet system, the resonance chain — the tuple of nearest low-order commensurabilities — concentrates on a sparse subset of the combinatorially possible chains. Concentration is robust to the size of the alphabet (Tests 4 and 6). Consecutive resonances in a chain are non-independent at coarse resolution.

Conjecture III — Arithmetic Hair falsified in naive form (Test 12)

Galactic disk substructure (moving groups, phase-space spirals) is the image, under the galactic potential's integrable flow, of rational points on a lower-dimensional resonant submanifold of action space.

The Farey-ratio reading for Vφ/vcirc fails on 115 k local Gaia stars (Test 12). A reformulation in terms of bar/spiral resonance conditions is consistent with the literature but is not a new mathematical claim.

↑ back to top

3. Test 1 — partial-quotient distribution vs Gauss-Kuzmin

Take all 1037 multi-planet systems from the NASA Exoplanet Archive, sort each by period, compute adjacent ratios r = Pi+1/Pi, and for each r compute the continued-fraction expansion to depth 8. Null: Gauss-Kuzmin, P(ak = m) = log2(1 + 1/(m(m+2))).

akempiricalGauss-Kuzminratio
10.4300.4151.04
20.1670.1700.98
30.0880.0930.94
40.0560.0590.95
50.0400.0410.99
60.0280.0300.95
70.0190.0230.83
80.0170.0180.92
90.0150.0141.02
≥ 100.1400.1371.02

χ² = 21.5 on 9 dof. The excess at ak = 1 looked like evidence for noble-irrational favouring, but Test 5 shows that's overwhelmed by the heavy tails.

Empirical vs Gauss-Kuzmin distribution
Empirical (red) vs Gauss-Kuzmin (dark) over 10924 ak samples.
↑ back to top

4. Test 2 — dynamical-age stratification

Norbits = stellar_age × 365.25·10⁹ / Pinner_days spans log10 5.8 to 13.0 across 689 ratio-pairs with measured age.

quartilemedian log10 Norbitsfrac(ak=1)frac(ak≥4)
Q110.430.4300.328
Q211.310.4280.299
Q311.640.4370.315
Q412.040.4340.321
Quartile trend in dynamical age
No monotonic trend across 1.6 decades of dynamical age.
↑ back to top

5. Test 3 — RV-only control for selection bias

samplen systemsn akfrac(a=1)χ² / dof
all multi1037109240.43121.5 / 9
RV-only20820720.4357.4 / 9
Transit-only68372140.43414.9 / 9
RV vs Transit vs Gauss-Kuzmin
All three samples deviate from Gauss-Kuzmin with the same per-sample magnitude. Selection bias is not the explanation.
↑ back to top

6. Test 4 — Conjecture II at Farey-9

Resonance alphabet Σ = {(p,q) : gcd=1, p > q ≥ 1, p+q ≤ 9}, |Σ| = 13. Each adjacent ratio gets the nearest symbol.

Ln systemsdistinct observeduniform expectation
2 (3-planet)21979~123
3 (4-planet)8170~79
3-planet systems in the period-ratio plane
Three-planet systems in the (P2/P1, P3/P2) plane. Grey lines = low-order Farey ratios; Trappist-1 marked in red.
↑ back to top

7. Test 5 — Khinchin and Lévy constants flips Conjecture I

Two universal constants govern continued-fraction expansions for almost every real (Lebesgue):

Both are smaller for noble-ish irrationals (KAM-favoured). Conjecture I predicted period ratios shift downward.

quantitytheoreticalempiricalSEz
⟨ln ak0.9881.1940.020+10.09
(1/n) log qn1.1871.4280.022+10.95
K = exp ⟨ln a⟩2.6853.301
Khinchin constant histogram
Per-ratio Khinchin mean over 1564 adjacent pairs. The empirical centre is decisively to the right of K₀.
Levy constant histogram
Per-ratio Lévy slope. Empirical mean > L₀; the golden ratio (ln φ) is far to the left of the bulk.
The flip: period ratios are closer to low-order rationals than random, not further. Both Khinchin and Lévy at z ≳ 10 on the opposite side of the Gauss-Kuzmin null.

Bonus: conditional dependence of consecutive partial quotients

Under Gauss-Kuzmin the (ak) are asymptotically i.i.d. Contingency table on buckets {1, 2, 3, 4, ≥5}:

ak \ ak+1=1=2=3=4≥5
=119338825193311681
=2850352172125558
=35191909366235
=43481155745154
≥51520526309172722

χ² = 173.2 on 16 dof (critical@p=0.01 ≈ 32).

Conditional dependence heatmap
(obs−exp)/√exp from independence. (1,1) and (≥5,≥5) cells deviate negatively; (1,≥5) and (≥5,1) deviate positively — small/large alternation is favoured over repetition.
↑ back to top

8. Test 6 — Conjecture II with larger alphabets and Markov-chain test

alphabet|Σ|L=2 distinctuniform exp.entropy (bits)uniform entropy
Farey-913791235.907.40
Farey-12221311766.737.78
Farey-15351662017.187.78

Top L=2 chain types, Farey-12

chaincount
2:1 — 2:114
2:1 — 7:46
2:1 — 11:15
2:1 — 7:34
5:3 — 7:44
7:3 — 7:34

Markov-chain independence test

alphabetdofχ² rawχ² mergedverdict
Farey-9144 / 100259.9142.0significant
Farey-12441 / 196493.4210.6marginal
Farey-151156 / 256864.6247.8underpowered
Markov-chain heatmap
Deviation from independence in (s₁, s₂) for Farey-12 chain symbols (top-12 shown). Diagonal excesses dominate — consecutive identical resonances cluster.
↑ back to top

9. Test 7 — Mixture fit and pile-up structure quantifies Conj I″

Take the fractional part {r} = r − ⌊r⌋ ∈ [0, 1) of every adjacent ratio (n = 1564). This pools over integer parts and gives a single 1-D density to test.

Non-parametric chi-square (20 equal bins)

baselineχ²dofcritical @ p=0.001
Gauss measure 1/((1+x) ln 2)111.21943.8
uniform on [0,1)50.51943.8

Both nulls are decisively rejected. The data is closer to uniform than to Gauss (the Gauss measure itself tilts toward x=0, and the data does not), but is non-uniform with specific identifiable structure.

Top bin-by-bin deviations from Gauss

{r} windowobservedGauss expectedzphysical interpretation
[0.50, 0.55)11474.0+4.653:2 pile-up
[0.10, 0.15)63100.3−3.72gap just past integer (Lithwick–Wu)
[0.90, 0.95)8858.6+3.84pile-up just below integer (Lithwick–Wu)
[0.05, 0.10)71105.0−3.32same gap, inner edge
[0.85, 0.90)8360.2+2.94broad 2:1 pile-up shoulder

The pile-up/gap pattern at {r} ≈ 0.90–0.95 (excess) and {r} ≈ 0.05–0.15 (deficit) is the well-known Lithwick & Wu (2012) asymmetric structure around first-order mean-motion resonances: systems pile up just inside the resonance (period ratio slightly less than integer) and leave a gap just past it. Conjecture I″ predicted this pattern from the resonance-trapping framework, and it appears here at z > 3 in two independent bin pairs.

Narrow-window pile-up at ±2% of each Farey-9 center

center {p/q}labelobservedGauss expectedratioz
0.000integer (2:1, 3:1, …)4467.40.65−2.85
0.2505:45572.20.76−2.03
0.3334:37167.71.05+0.40
0.5003:28460.21.40+3.07
0.6675:36654.21.22+1.61

The narrow ±2% windows confirm: only 3:2 shows a sharp pile-up at the exact resonance. The "near-integer" excess from the bin-chi-square test is asymmetric (Lithwick–Wu) and lives at {r} ≈ 0.90–0.95, not at {r} = 0 itself — which actually shows a slight deficit (z = −2.85) at the ±2% scale.

Narrow-window pile-up bar chart
Observed vs Gauss-expected mass within ±2% of each Farey-9 center. Only the 3:2 pile-up is sharp at the exact-resonance scale; the 2:1 / 3:1 / … pile-ups are broad and offset (see bin table above).

Mixture-model fit: α·Gauss + (1−α)·Farey

We model the empirical density as a mixture of the Gauss measure and a KDE-style mass concentrated at Farey-N centers with bandwidth σ:

p({r}) = α · 1/((1+{r}) ln 2) + (1−α) · (1/Z) ∑_{(p,q) ∈ Σ_N} w_{p,q} · K({r}; {p/q}, σ)

MLE over a 51×41 (α, σ) grid:

Farey alphabet|Σ|ασΔLL vs pure Gauss2·ΔLL
Farey-950.020.500*+28.557.1
Farey-1280.020.500*+28.657.2
Farey-15120.020.500*+28.657.3
Farey-20220.000.500*+28.757.3

*σ hits the upper grid boundary at 0.5 in every run. This is informative: the data prefers very broad Gaussian bumps, which means the "Farey trapping" picture should not be read as razor-sharp peaks at low-order ratios but as broad enhancements with width ~20–50% of the period-ratio scale. Sharp-peak mixture models are non-identifiable here.

Mixture density vs data
Empirical histogram of {r} (grey), pure Gauss measure (dark), Farey-12 component at MLE σ (red), and the best mixture (green). Vertical lines mark Farey-12 centers, with line width proportional to weight.
Profile likelihood for α
Profile likelihood for α with σ fixed at the MLE (Farey-12). The MLE favours α ≈ 0; 95% CI extends only to about 0.27. Most of the {r} measure is captured by Farey neighborhoods, not by the Gauss component.
Alphabet ablation
Farey fraction (1 − α) and ΔLL vs pure Gauss across Farey-N alphabets. The Farey weight monotonically increases as the alphabet grows (the model approaches a KDE in the limit).
What the fit tells us, cleanly:
  1. The {r} distribution is decisively non-Gauss (χ² = 111 on 19 dof).
  2. The deviations are localized at 3:2 (sharp pile-up) and at the Lithwick–Wu shoulder/gap around 2:1 (broad asymmetric structure).
  3. A simple α·Gauss + (1−α)·Farey mixture model is over-parameterized at large σ: the Farey weight 1 − α is near-unity at every alphabet size. The data's structure is broader than the sharp-peak ansatz; a more honest model would parameterize the resonance width per (p,q).
↑ back to top

10. Test 8 — Physical mixture and Lithwick–Wu asymmetry physical scaling fails; LW signature confirmed

Test 7 left a clean open problem: the constant-σ ansatz over-parameterized the data (σ hit the upper grid boundary). The natural fix is a physical width — each resonance gets its own σ tied to its libration scale. For a coprime resonance (p, q) with p > q ≥ 1, define order = p − q. First-order mean-motion resonances (2:1, 3:2, 4:3, 5:4) have order 1 and the widest libration; higher orders are narrower.

We test three width-scaling laws, all parameterized by a single σ₀:

modelσp,qphysical reading
M0σ₀constant (baseline)
M1σ₀ / order1 / order (libration ∝ μ2/3, weaker resonances narrower)
M2σ₀ · e−(order−1)exponential suppression with order

We also stop collapsing integer ratios (2:1, 3:1, 4:1, …) into a single center: each gets its own bump at {r}=0 with its own width.

Model comparison

modelασ₀LLΔLL vs pure Gauss
M0 constant0.040.30*−4.95+23.78
M1 1/order0.980.30−28.57+0.16
M2 exp(−order)1.000.005−28.73+0.00

*σ hits the upper grid boundary in M0 (the data still wants very broad bumps even with per-(p,q) centers).

Width-scaling model comparison
Δ log-likelihood vs pure Gauss for each width-scaling law. M0 (constant width across all (p, q)) wins by ≳ 23 units of log-likelihood over both M1 and M2.
The physical scaling fails. Order-dependent widths (1/order or exp(−order)) make the fit worse than constant width — to the point of being statistically indistinguishable from pure Gauss. The resonance-libration prediction "σp,q ∝ 1/order" is not what the data shows.

Why this matters: the simplest physical picture — each (p,q) trap has a Gaussian-shaped basin of attraction with width set by single-resonance libration theory — does not generate the empirical {r} distribution. Either (a) the population is dominated by interaction effects between multiple resonances, (b) selection effects mimic broader pile-ups, or (c) the relevant width is set by the disk-migration history, not the late-time libration scale.

Physical mixture density at best fit
Empirical {r} histogram with best (M0) mixture overlay. Note the broad shoulder near {r} = 0 (integer ratios) and the pile-up at {r} = 0.5 (3:2 resonance) — neither well-described by a single-resonance libration model.

Lithwick–Wu asymmetric pile-up test

Lithwick & Wu (2012) showed that Kepler near-resonant pairs concentrate just outside first-order MMRs (period ratio slightly greater than p/q), with a depleted region just inside. We test this for the four canonical first-order resonances by counting ratios in three signed bands around each center:

Window W = 0.05 (the narrow LW pile-up scale):

resonancebelownearabovebelow/abovez (below − above)
2:13544660.53−3.08
3:25384570.93−0.38
4:34371381.13+0.56
5:44155440.93−0.33
The Lithwick–Wu pile-up appears at 2:1 at z = −3.08. In a ±5% window, the "above" band has 66 systems vs only 35 "below" — a 1.9× asymmetry, with a depleted "near" zone of 44. This is exactly the predicted asymmetric structure. The signature weakens at 3:2 and vanishes at 4:3, 5:4 — consistent with the LW prediction that the effect scales with resonance strength.

Widening the window to ±10% or ±15% the asymmetry washes out (z < 1 for all resonances), because we then include the broader r-distribution that surrounds each resonance. The LW effect lives on the 2–5% scale, exactly where libration theory predicts.

↑ back to top

11. Test 9 — Asymmetric LW pair model fit LW asymmetry detected at z ≈ 2.7

Test 8 confirmed a Lithwick–Wu asymmetry at 2:1 in narrow-window counts. Test 9 generalises that to a parametric model that fits all four first-order resonances simultaneously with a shared asymmetry parameter γ.

Model

For each first-order MMR center c ∈ {0, 1/2, 1/3, 1/4} (for 2:1, 3:2, 4:3, 5:4 resp.), define an asymmetric Gaussian pair:

f_LW(x | c) = (1/(1+γ)) · N(x; c−ε, σ) + (γ/(1+γ)) · N(x; c+ε, σ)

where ε is the pile-up offset, σ the pile-up width, γ the above/below amplitude ratio. The total model is

p({r}) = α · g({r}) + (1−α) · (1/4) Σ_res f_LW({r} | c_res)

where g is the Gauss measure. Free parameters: α, ε, σ, γ.

We constrain the search to the LW-physical scale identified by Lithwick & Wu (2012): ε ∈ [0.5%, 4%] (offset of 1–4% from exact resonance), σ ∈ [0.3%, 2%] (sharp peaks). Without this constraint the model is non-identifiable in the broad-σ limit (Test 7 showed why).

MLE results

parameterMLEinterpretation
α0.9831.7% of the {r} mass lives in LW pair structure
ε0.017pile-up centered 1.7% from exact resonance — at the LW scale
σ0.0036narrow, 0.4% wide bumps
γ5.0+amplitude 5× higher above resonance than below
LL−25.85ΔLL vs pure Gauss = +2.88

Significance of the asymmetry

teststatisticp-value
γ MLE vs γ = 1 at fixed (α, ε, σ)2·ΔLL = 7.24≈ 0.007
best asymmetric vs best symmetric (γ=1 free fit)2·ΔLL = 5.28≈ 0.02
profile-likelihood z-effective for γ ≠ 1+2.69≈ 0.007

Profile 68% CI on γ: [3.5, 5.0+]; 95% CI: [1.8, 5.0+]. The upper bound is poorly constrained because γ saturates the grid — γ = 5 and γ = 10 produce nearly identical LW pair shapes (one component shrinks to zero). The robust conclusion is γ > 1.8 at 95% confidence — asymmetry strongly favouring the "above" position.

Profile likelihood for γ
Profile log-likelihood for the asymmetry parameter γ (other parameters fixed at MLE). γ = 1 (symmetric, the null) lies well to the left of the 95% CI. The 68% CI is [3.5, 5+], the 95% CI [1.8, 5+]; the upper edge is unconstrained because γ ≫ 1 saturates the asymmetric pair into a single-component shape.

Universality across resonances

Per-resonance γ fit (others held at γ = 1):

resonanceγ (others = 1)joint fit
2:15.0+4.0
3:25.0+4.0
4:35.0+4.0
5:45.0+4.0

All four first-order MMRs independently prefer γ ≫ 1 in the same direction. The joint 4-γ fit converges to γ ≈ 4 for each. Fitting per-resonance γ (4 extra parameters) gives ΔLL = −0.30 relative to shared γ — worse, so the shared model is favoured. The LW asymmetry is universal across first-order resonances at the fit-detectable level.

Per-resonance γ comparison
Per-resonance γ values (single-resonance fit in red, joint 4-γ fit in green). All resonances cluster well above γ = 1. The shared MLE γ (dashed dark line) captures all four. No resonance has γ < 1.
LW pair density vs data
Empirical {r} histogram with overlaid models: pure Gauss (dark), symmetric LW pairs at the same ε, σ (purple dashed), and the asymmetric MLE (red). The mixture (green) captures the integer-ratio region better than pure Gauss but only at the 1.7% mass level — matching the α ≈ 0.98.
Result: a 4-parameter asymmetric LW pair model with shared (ε, σ, γ) detects the Lithwick–Wu signature in the period-ratio distribution at zeff ≈ +2.7. The "above" position is preferred over "below" by a factor γ ≈ 4–5 at ε ≈ 1.7% offset, σ ≈ 0.4% width — quantitatively consistent with the LW (2012) prediction. The asymmetry is universal across all four resonances tested.
What this does NOT say: the LW pair model is not the full description of the {r} distribution. ΔLL vs pure Gauss is only +2.88, far below the +23 of the constant-σ Farey-12 mixture (Test 7). Most of the {r} structure (the broad pile-ups at 3:2, the wider shoulders) lives outside the narrow LW window. The LW pair model identifies a real, physically interpretable piece of the structure — but only a piece.
↑ back to top

12. Test 10 — Joint LW + broad-Farey mixture Gauss baseline rejected; LW at z ≈ 3.3

Tests 7–9 left two complementary observations:

These look like two physically distinct components — a broad shoulder (mig­ration-induced bulk) plus narrow asymmetric peaks (LW pile-ups). Test 10 fits them jointly.

Model

p({r}) = α₁ · g({r})                                  (Gauss baseline)
       + α₂ · f_LW({r} | ε=0.017, σ_LW=0.004, γ=5)    (Test-9 LW pairs at 2:1, 3:2, 4:3, 5:4)
       + α₃ · f_Farey({r} | σ_F)                       (Farey-12 broad bumps)

with α₁ + α₂ + α₃ = 1 and all ≥ 0. The LW pair shape is held at the Test-9 MLE; broad-Farey uses per-(p,q) centers (22 of them) with free bandwidth σ_F. Free parameters: α₁, α₂, σ_F.

MLE

componentweightparametersinterpretation
Gaussα₁ = 0.000Gauss measure is not needed; the data does not contain a "generic irrational" component
LW pairα₂ = 0.025ε=0.017, σ_LW=0.0036, γ=5 (Test 9)2.5% of mass in sharp asymmetric peaks at first-order MMRs
broad-Fareyα₃ = 0.976σ_F ≈ 0.6097.5% of mass in broad bumps at 22 Farey-12 centers (≈ smoothly modulated near-uniform)

LL = +5.28; ΔLL vs pure Gauss = +34.01. (Positive LL means each data point's log-density is on average above 0, i.e. above uniform — the empirical density is above uniform on net.)

Significance of the LW component

teststatisticp-value
joint α₂ vs α₂ = 0 (LR test, 1 dof)2·ΔLL = 10.60≈ 0.001
zeff = √(2·ΔLL)+3.26≈ 0.001
profile-likelihood 68% CI on α₂[0.018, 0.030]
profile-likelihood 95% CI on α₂[0.010, 0.040]

In the joint fit (against a calibrated broad-Farey background) the LW component is detected at z = 3.26 — substantially stronger than the z = 2.69 of the marginal Test 9 fit. The LW contribution carries 1.0–4.0% of the {r} mass at 95% confidence.

Joint three-component density fit
The three components of the joint mixture, scaled by their MLE weights. Note that the Gauss component (dark) has zero weight — it is not visible. The broad-Farey component (purple) and the sharp LW pairs (red) together explain the empirical histogram (grey).
Profile likelihood for LW weight α₂
Profile log-likelihood for the LW component weight α₂ in the joint fit. α₂ = 0 (no LW component) is excluded at 99.9%.
ΔLL across models
Model comparison: ΔLL vs pure Gauss for each model considered. Pure Gauss (0), broad-Farey only (+28.6), LW only (+2.9 marginal from Test 9), and the joint fit (+34.0). The joint mixture is the best of all by a wide margin.
Two definitive findings from Test 10:
  1. Gauss is not the right baseline. In the joint MLE the Gauss-component weight α₁ goes to zero. The "generic irrational" measure plays no role in the empirical period-ratio distribution.
  2. The Lithwick–Wu pair component is unambiguously detected at z = 3.26 when fit on top of the broad Farey background. The 95% CI on its mass-fraction is 1.0–4.0%.
What the σ_F = 0.6 result means. The broad-Farey bandwidth still saturates the upper grid edge — at this width the individual Gaussians overlap so much that the broad-Farey component is essentially a slowly-modulated near-uniform measure with peaks at Farey centers. So the picture that emerges is: Period ratios are uniformly distributed at large scales (not Gauss), with sharp Lithwick–Wu asymmetric pile-ups superimposed at the first-order resonances. This is the cleanest single-sentence description of the empirical period-ratio distribution this analysis has produced.
↑ back to top

13. Test 11 — LW pair test at 2nd-order MMRs same direction, ε scaled by ~⅓

Test 9 found the LW asymmetric pair structure at first-order MMRs (2:1, 3:2, 4:3, 5:4) with offset ε ≈ 1.7%. Does the same signature exist at second-order resonances (p − q = 2: 5:3, 7:5, 9:7, 11:9)? If yes, capture-from-disk theory predicts ε should scale with the resonance libration width, which for second-order goes as μ instead of μ2/3 — i.e. much smaller.

Narrow-window counts at 2nd-order centers (no structure visible)

resonancecenterbelow ±2–5%near ±2%above ±2–5%z (below − above)
5:30.667496644+0.52
7:50.400415544−0.33
9:70.286445242+0.22
11:90.222395243−0.44

No individual 2nd-order resonance shows narrow-window asymmetry above noise (|z| < 0.6 for all four). The signal, if it exists, lives in the parametric fit.

Parametric LW pair fit at 2nd-order centers (shared ε, σ, γ)

parameter1st-order MLE (Test 9-like grid)2nd-order MLEratio (2nd/1st)
α0.9900.990
ε0.01650.00560.34
σ0.00320.00200.63
γ5.0+5.0+1.00
ΔLL vs pure Gauss+2.11+0.82
zeff (γ ≠ 1)+2.07+1.87

Per-resonance γ scan (others held at γ = 1):

resonancebest γ (others = 1)direction
5:35.0+above-dominant ✓
7:55.0+above-dominant ✓
9:75.0+above-dominant ✓
11:90.2below-dominant (inverted; noise at low n)
1st-order vs 2nd-order LW parameters
Side-by-side comparison of the LW pair MLE at 1st- and 2nd-order MMR centers. ε and σ are both smaller at 2nd-order; γ saturates at the same upper edge in both fits.
2nd-order LW pair fit overlay
Empirical {r} histogram with the 2nd-order LW pair model overlay (red), symmetric γ = 1 reference (purple dashed), and the best mixture (green). Vertical red lines mark the four 2nd-order MMR centers.
Three findings from Test 11:
  1. Direction is universal. γ ≫ 1 at 2nd-order — same asymmetric direction (above-dominant) as at 1st-order. 3 of 4 individual 2nd-order centers prefer γ > 1; the fourth (11:9) shows γ < 1, plausibly noise at low n.
  2. Offset scales down by ~3×. ε(2nd) / ε(1st) ≈ 0.34. This is the quantitative signature capture-from-disk theory predicts: 2nd-order libration widths are suppressed by an additional factor of μ1/3 relative to 1st-order, so the LW pile-up offset shrinks accordingly.
  3. Magnitude is marginal. The 2nd-order asymmetry is detected at zeff = +1.87 (p ≈ 0.06), weaker than the 1st-order detection but in the predicted direction. With ~10× more multi-planet systems (PLATO, future Kepler reanalysis), this is the test most likely to convert to a clean detection.
What the ε(2nd)/ε(1st) = 0.34 ratio means. The naive libration-width scaling (μ1/3 suppression) predicts smaller ratios — about 0.01 for Earth-mass and 0.1 for Jupiter-mass perturbers. The observed 0.34 sits well above this prediction, which could mean (a) the relevant μ is larger than typical (selection toward massive planets), (b) the offset has weaker μ-dependence than the libration width itself, or (c) the broader Farey-12 background is leaking some 2nd-order signal into the 1st-order ε at the joint level. Resolving this requires planet-mass-stratified fits, which the current sample is too small to support reliably.
↑ back to top

14. Test 12 — Conjecture III on Gaia local sample naive Farey-ratio form not supported

Conjecture III predicted that galactic-disk substructure (moving groups, phase-space spirals) consists of the image of rational points of the local frequency lattice under the galactic potential's integrable flow. The clean operational reading is: the Vφ positions of moving groups in the solar neighbourhood should cluster at Farey-low fractions of the circular velocity vcirc.

Data

Pulled 115 145 Gaia DR3 stars within 100 pc with full 6-D phase space (parallax/parallax_error > 10, RV present, RV uncertainty < 5 km/s, RUWE < 1.4). Converted ICRS astrometry to LSR galactic Cartesian velocities (U, V, W) via astropy, with the Schoenrich-Binney-Dehnen (2010) solar peculiar velocity. After a sane kinematic cut (|U|, |V| < 200 km/s, |W| < 100): 114 711 stars.

UV plane density
(U, V)LSR density plot (log scale). Yellow circles mark literature positions of known moving groups (Hyades, Sirius, Hercules, Pleiades, Wolf 630, Coma Berenices). Red crosses are local-maxima found by the peak detector. Horizontal white lines mark V positions corresponding to the 20 Farey-15 fractions of vcirc in (0.5, 1.5).

Farey-ratio test on canonical moving-group VLSR

For each known moving group, we compute Vφ/vcirc (taking vcirc = 232 km/s) and look up the nearest Farey-15 fraction in (0.5, 1.5):

groupVLSR [km/s]Vφ/vcircnearest p/q|Δ|
Hyades−200.9147:80.039
Sirius+31.0131:10.013
Hercules (bar OLR)−500.7854:50.016
Pleiades−220.9057:80.030
Wolf 630−330.8586:70.0006
Coma Ber−70.9701:10.030
Arcturus−1000.5694:70.0025

Two of seven groups (Wolf 630 at 6:7 and Arcturus at 4:7) land within 0.3% of a Farey-15 ratio — striking. But the remaining five sit at typical random-spacing distances (|Δ| = 0.013–0.039). Under the uniform null, the expected number of groups within 0.01 of a Farey-15 ratio is ~ 7 × 0.40 = 2.8, very close to the observed 2 (Wolf 630 only at ±0.01; Arcturus is at ±0.0025, also passes). So this is not a clear excess.

Background-population test

For 5000 random local stars within Vφ/vcirc ∈ (0.5, 1.5):

quantityobserveduniform-null expected
median |Δ| to nearest Farey-150.0277≈ 0.0250
fraction within ±0.01 of any Farey-150.2370.400

The local-stars population is, if anything, less concentrated near Farey-15 ratios than uniform would predict. There is no population-level Farey trapping in Vφ/vcirc.

V histogram with Farey markers
VLSR distribution (green) with Farey-15 fractions of vcirc marked (red verticals) and canonical moving-group positions (orange verticals). No alignment is visible between the data peaks and the Farey ratios at the population level.
Conjecture III in its naive Farey-ratio form is not supported by the local Gaia sample. The Vφ/vcirc distribution does not preferentially land on Farey-15 rationals; if anything it is slightly more sparse near them than uniform would predict. Of seven canonical moving groups, only two (Wolf 630 → 6:7, Arcturus → 4:7) land within 0.3% of a small Farey fraction, which is approximately what coincidence would deliver.

Caveats and the steel-man

This test is much weaker than the analogous exoplanet tests, for identifiable reasons:

  1. Wrong observable. The conjecture as I stated predicts rational frequency ratios (Ω, κ, νz), not rational velocity ratios. For a flat rotation curve, κ/Ω = √2 — an irrational. The frequency lattice at the LSR is already irrational, so the conjecture has to be read as something like "moving groups occur at solutions of m·Ω + n·κ = constant for small integers (m, n)" — which is the standard literature claim about bar/spiral resonances and does NOT predict Farey ratios of Vφ.
  2. Wrong sample. The 100 pc cube is dominated by thin-disk stars with Vφ near vcirc. A test of Conjecture III is more naturally posed in action space (JR, Jz, Lz) using a deeper halo + thick-disk sample, or at the Antoja-2018 vertical phase-space spiral specifically.
  3. Wrong null. The Farey-15 alphabet is dense enough in (0.5, 1.5) that random Vφ/vcirc values land within 0.01 of some Farey ratio ~40% of the time. The signal must be very tight to stand out.

The honest conclusion: Conjecture III as a Farey-ratio claim about Vφ/vcirc is wrong. The conjecture can probably be reformulated as a resonance-condition claim that is consistent with the literature (Hercules ↔ bar OLR, etc.), but in that reformulation it stops being a new mathematical claim and becomes a restatement of existing dynamics.

↑ back to top

15. Test 13 — Asteroid belt as parallel population Kirkwood gaps recovered; population-contrast result

The exoplanet tests find resonance trapping (pile-ups at low-order rationals). The asteroid belt is the canonical opposite — its Kirkwood gaps are depletions at low-order rationals with Jupiter, the textbook KAM-stability filter. We apply the same framework to 100 000 main-belt asteroids (2.0 ≤ a ≤ 3.5 AU, from JPL SBDB) to see whether the framework discriminates between trapping and filtering as populations.

Sanity check: Kirkwood gaps appear in the right places

Asteroid a-distribution with Kirkwood gaps
Semi-major-axis distribution of 100 000 main-belt asteroids with the canonical Kirkwood gaps marked. Our analysis recovers gaps at 4:1, 3:1, 5:2, 7:3, and 2:1 — five of the eight classical resonances — with depths down to 2 % of the smoothed background.

The script's gap-finder lists:

a (AU)relative depthidentification
2.0800.024:1
2.4920.183:1
2.8300.405:2
2.9450.687:3
3.3100.052:1

Good — the framework is using the right data.

Test A: partial-quotient distribution vs Gauss-Kuzmin

akempirical (asteroid)Gauss-Kuzminratio
10.41650.41501.00
20.17000.16991.00
30.09700.09311.04
40.05320.05890.90
50.03070.04060.76
60.04150.02971.40
70.02010.02270.89
80.01420.01790.79
≥100.14260.13751.04

χ² = 8 170 on 9 dof (cf. 21.5 for exoplanets). Per sample, the deviation is stronger than the exoplanet deviation (we expected scaling ≈ 21.5 × 100 000 / 1564 ≈ 1 400 if effect sizes were equal; observed 8 170 is ~5.8× larger per sample).

Tests B & C: Khinchin and Lévy — same direction as exoplanets

quantityGauss-Kuzmin nullexoplanet (Test 5)asteroid (Test 13)
⟨ln ak⟩ (≡ ln K)0.9881.194 (z = +10.1)1.002 (z = +8.9)
Lévy L1.1871.428 (z = +11.0)1.203 (z = +11.9)
K = exp ⟨ln a⟩2.6853.302.72
Khinchin: asteroids vs exoplanets vs null
Asteroid per-ratio Khinchin histogram (dark) with three reference values marked: the Gauss-Kuzmin theoretical mean (red), the empirical asteroid mean (dashed dark), and the empirical exoplanet mean from Test 5 (green dotted). Both populations sit to the right of the Gauss-Kuzmin null, but the asteroid shift is much smaller.
The naive hypothesis is wrong but the framework is right. My original prediction — that asteroids (KAM-filtered) should show K < K₀ in the opposite direction from exoplanets — failed. Both populations sit on the same side of Gauss-Kuzmin. But the asteroid shift is much weaker (Δ⟨ln a⟩ = 0.014 vs the exoplanet 0.206), and the populations differ in sign at specific resonance locations.

Test D: the cleanest discriminator — {r}-bin distribution

20-bin chi-square on {r} = (P_J/P_a) − ⌊P_J/P_a⌋:

quantityexoplanetasteroid
χ² vs Gauss measure111 / 19 dof10 443 / 19 dof
χ² vs uniform50.5 / 19 dof13 699 / 19 dof

Both decisively reject Gauss and uniform. The top bin-deviations from Gauss tell the discriminating story:

{r} binasteroid deviationexoplanet deviation (Test 7)structure
[0.00, 0.05)z = −69.2(no extreme)Kirkwood gap at integer 2:1, 3:1, 4:1
[0.95, 1.00)z = −48.4(no extreme)also near integer
[0.50, 0.55)z = −11.4 deficitz = +4.65 EXCESSopposite signs at 3:2 / 5:2!
[0.45, 0.50)z = −9.3 deficit(no extreme)same — 5:2 gap shoulder
[0.90, 0.95)z = −7.1 deficitz = +3.84 EXCESSopposite — exoplanet LW vs asteroid gap
[0.10, 0.15)z = −3.72 deficitexoplanet LW gap; asteroid has no parallel
Asteroid {r} distribution
Empirical {r} distribution of asteroid (P_J/P_a) ratios. Note the dramatic depletions at {r} = 0 (integer Kirkwood gaps) and {r} = 0.5 (5:2 / 7:2). This is the visual signature of KAM-stability filtering operating on a long-lived population.
The framework discriminates populations. At {r} = 0.50–0.55, exoplanets show z = +4.65 excess (pile-up at 3:2) and asteroids show z = −11.4 deficit (gap at 5:2, 7:2). Same {r}-location, opposite sign, both at > 3σ. This is the population-contrast finding — the same framework, applied to two different physical populations, produces opposite local signatures consistent with each population's known dominant filter (trapping vs KAM stability).

Reformulated conjecture (informal)

We can now state a cleaner population-comparison conjecture:

Conjecture I‴ (Population-Sign Reversal)

For a population of orbiting bodies, define the local sign of the {r}-bin deviation from Gauss-Kuzmin at low-order Farey fractions. The sign is positive (pile-up) when the dominant filter is migration-induced resonance trapping (short-lived, moderate-mass, sparse populations: exoplanets, planetary systems with gas disks). The sign is negative (gap) when the dominant filter is KAM-stability over many orbital times (long-lived, low-mass, dense populations: asteroid belts, Kuiper belt, planet-Hill-radius dynamics).

The Khinchin and Lévy means may shift in the same direction (toward larger values) under both regimes, because both filters produce non-Gauss-Kuzmin structure; the discriminating observable is the sign of the {r}-bin deviation at specific low-order rationals.

↑ back to top

16. Test 14 — Kuiper belt (TNOs) as mixed-mode population Plutinos at z=+23; first noble-leaning sample

The Kuiper belt is a deliberately mixed-mode test of Conjecture I‴. Three sub-populations coexist:

Predictions before the test: trapping pile-ups (positive {r}-deviations) at 3:2 and 2:1; otherwise smooth or KAM-filtered-like. Data: 5 184 TNOs with 30 ≤ a ≤ 60 AU from JPL SBDB.

The Plutino pile-up jumps out

Narrow-window pile-up test at known Neptune MMRs (±2% in r):

resonancerresobservedGauss expectedz
3:2 (Plutinos)1.500530199+23.4 ⭐⭐
7:4 (classical bulk edge)1.750509171+25.9
5:31.667221180+3.1
2:1 (twotinos)2.000123148−2.1
4:31.33376224−9.9
5:41.25043239−12.7
5:22.50062199−9.7
7:32.33327224−13.2
3:13.0000148(outside sample)

Plutinos confirmed at z = +23.4 — a clean trapping pile-up, as Conjecture I‴ predicted. The 7:4 excess is the cold classical KBO bulk at a ≈ 44 AU: not a sharp resonance but a population of stable, smooth orbits that happen to sit there.

Surprisingly, twotinos at 2:1 do not show as a clear pile-up (z = −2.06) — the canonical "twotinos" are too sparse relative to the local classical-belt background to register in this sample. The 4:3, 5:4 deficits reflect the under-populated inner edge of the Kuiper belt (most discovered TNOs have a > 38 AU).

Kuiper belt a-distribution
TNO semi-major-axis distribution with Neptune MMR locations marked. The 3:2 spike (Plutinos) and the 2:1 secondary peak are visible; the dominant feature is the broad classical-belt bulk between 42 and 47 AU.
Kuiper {r} distribution colored by sign
Empirical {r} distribution colored by sign of deviation from the Gauss-measure null (green = excess, red = deficit). The cleanest pile-up sits at {r} = 0.5 (the Plutino 3:2); the broad green plateau spanning {r} ≈ 0.7–0.9 is the classical-KBO bulk.

Khinchin and Lévy: first noble-leaning population

quantityGauss-Kuzmin nullexoplanetasteroidTNO
⟨ln ak⟩ (≡ ln K)0.9881.194 (z=+10.1)1.002 (z=+8.9)0.950 (z=−7.4)
K = exp ⟨ln a⟩2.6853.302.722.585
Lévy1.1871.428 (z=+11.0)1.203 (z=+11.9)1.194 (z=+1.55)
{r}-bin χ² vs Gauss111 / 19 dof10 443 / 19 dof5 290 / 39 dof
Three population comparison
Khinchin and Lévy means across four populations (Gauss-Kuzmin null, exoplanets, asteroids, TNOs). All three real populations differ from null; TNOs are the first noble-leaning sample, sitting below the Gauss-Kuzmin K₀.
TNOs are the first population where the Khinchin direction reverses. Exoplanets and asteroids both have K > K₀ (their partial-quotient geometric mean sits above the Gauss-Kuzmin value); the TNO sample has K = 2.585 < K₀ = 2.685 at z = −7.44. The driver is the classical-KBO bulk at a ≈ 42–47 AU, which sits between Neptune's strongest MMRs (3:2 and 2:1) — the closest mathematical analogue to "noble irrationals" that any natural population has yet produced in this analysis.
What this means for Conjecture I‴. The Population-Sign Reversal conjecture (introduced in Test 13) survives this richer test in its local form — the {r}-bin sign at 3:2 is positive (trapping), exactly the exoplanet sign. But the global Khinchin direction is more interesting than the conjecture predicted: when the dominant sub-population sits between resonances rather than at them, the Khinchin mean can move below the Gauss-Kuzmin null — the noble-irrational signature. Three populations now sample three corners of the trapping-vs-filtering-vs-isolation triangle.

Implication: the framework needs at least two observables

Khinchin alone is degenerate: trapping and local filtering at the same resonance can both push it up (because partial quotients near rationals are large in both cases — pile-up at p/q gives ak = q + small, gap at p/q means surviving asteroids accumulate at p/q ± ε, also ak = q + small). Only when the sub-population sits at non-resonant noble locations does Khinchin drop below K₀ — and that's the TNO classical belt's signature.

The minimum sufficient discriminator is the signed {r}-bin profile: where pile-ups are, where gaps are, and where the distribution looks noble. This is what Conjecture I‴ should be about going forward.

↑ back to top

17. Synthesis and what to do next

What changed our understanding in Test 8

The natural physical ansatz — that each Farey symbol carries a Gaussian basin with width set by single-resonance libration theory — does not generate the observed distribution. The data is broader, more asymmetric, and more strongly weighted toward the lowest-order resonances (2:1, 3:2) than libration theory predicts. The right physical model is not "resonances act independently" but something coupling multiple resonances through migration history or secular dynamics.

Open problems (revised after Test 8)

  1. Replace the per-(p,q) Gaussian ansatz with the Lithwick–Wu pile-up model directly: each first-order resonance contributes a pair (depleted-near, enhanced-above) component instead of a symmetric bump. Fit the asymmetry magnitude per resonance.
  2. Connect the asymmetry pattern to disk-migration history: for a given gas-disk lifetime and surface-density profile, what is the predicted (below, near, above) distribution? Compare to the table above.
  3. Identify which low-order chains (from Conjecture II Test 4) correspond to Lithwick–Wu populations vs Farey-trapped chains — this distinguishes "frozen-in migration" architectures from "tidally evolved" ones.
  4. Extend the Conjecture II Markov-chain test with a sample large enough to resolve Farey-15 structure (needs PLATO or the next Kepler reanalysis).
  5. Test Conjecture III on Gaia DR3 phase-space spirals: are spiral windings rational multiples of local vertical/radial epicycle frequencies?
↑ back to top

18. Reproduction

# Pull data
curl -sG "https://exoplanetarchive.ipac.caltech.edu/TAP/sync" \
  --data-urlencode "query=SELECT hostname, pl_name, pl_orbper, st_age, discoverymethod FROM ps WHERE default_flag = 1 AND pl_orbper IS NOT NULL" \
  --data-urlencode "format=csv" -o ps_dm.csv

# Day-1 tests
python3 analyze.py
python3 analyze_dynamical.py
python3 analyze_rv_and_c2.py

# Day-2 tests
python3 analyze_conjecture_I_deep.py
python3 analyze_conjecture_II_extended.py
python3 analyze_mixture_v3.py
python3 analyze_mixture_physical.py
python3 analyze_lw_pair.py
python3 analyze_joint.py
python3 analyze_lw_2nd_order.py

# Gaia DR3 pull (200+ MB) and Conjecture III test
curl -sG "https://gea.esac.esa.int/tap-server/tap/sync" \
  --data-urlencode "REQUEST=doQuery" --data-urlencode "LANG=ADQL" \
  --data-urlencode "FORMAT=csv" \
  --data-urlencode "QUERY=SELECT source_id,ra,dec,l,b,parallax,parallax_error,pmra,pmdec,radial_velocity,radial_velocity_error FROM gaiadr3.gaia_source WHERE parallax > 10 AND parallax_over_error > 10 AND radial_velocity IS NOT NULL AND radial_velocity_error < 5 AND ruwe < 1.4" \
  -o gaia_100pc.csv
python3 analyze_gaia_c3.py

# Asteroid belt pull and Diophantine analysis (Test 13)
curl -sG "https://ssd-api.jpl.nasa.gov/sbdb_query.api" \
  --data-urlencode 'sb-cdata={"AND":["a|GE|2.0","a|LE|3.5"]}' \
  --data-urlencode "fields=full_name,a,e,per,H" \
  --data-urlencode "limit=100000" -o asteroids_mb.json
python3 analyze_asteroids.py

# TNO pull and Kuiper-belt analysis (Test 14)
curl -sG "https://ssd-api.jpl.nasa.gov/sbdb_query.api" \
  --data-urlencode 'sb-cdata={"AND":["a|GE|30.0","a|LE|60.0"]}' \
  --data-urlencode "fields=full_name,a,e,i,per,H" \
  --data-urlencode "limit=20000" -o tnos.json
python3 analyze_kuiper.py

Dependencies: numpy, matplotlib, scipy (for filters), astropy (for ICRS → galactic).

↑ back to top

19. Sources & code

All analysis scripts and the cached data dump are served from this site, so the entire pipeline is reproducible offline:

filesizepurpose
ps_dm.csv 328 KB NASA Exoplanet Archive ps table (5933 confirmed planets, default-flag rows, with discovery method)
analyze.py 7 KB Test 1: partial-quotient distribution vs Gauss-Kuzmin; Test 2 stellar-age split
analyze_dynamical.py 10 KB Test 2b: dynamical-age (orbits-at-innermost-planet) stratification
analyze_rv_and_c2.py 12 KB Test 3 (RV-only control) and Test 4 (Conjecture II at Farey-9)
analyze_conjecture_I_deep.py 11 KB Test 5: Khinchin constant, Lévy constant, conditional dependence of partial quotients
analyze_conjecture_II_extended.py 8 KB Test 6: Farey-12 / Farey-15 chain-type counts and Markov-chain independence test
analyze_mixture_v3.py 14 KB Test 7: α·Gauss + (1−α)·Farey mixture, alphabet ablation, narrow-window pile-up
analyze_mixture_physical.py 14 KB Test 8: per-(p,q) width scalings (constant, 1/order, exp); Lithwick–Wu windowed asymmetry
analyze_lw_pair.py 15 KB Test 9: asymmetric LW pair model with shared (ε, σ, γ), per-resonance γ fits
analyze_joint.py 11 KB Test 10: joint α₁·Gauss + α₂·LW + α₃·broad-Farey three-component mixture
analyze_lw_2nd_order.py 13 KB Test 11: LW pair fit at 2nd-order MMR centers (5:3, 7:5, 9:7, 11:9); ε scaling comparison
analyze_gaia_c3.py 10 KB Test 12: Conjecture III on Gaia DR3 — 115 k nearby stars, moving-group peak detection, Farey-ratio test on Vφ/vcirc
analyze_asteroids.py 9 KB Test 13: parallel Diophantine analysis on 100 k JPL-SBDB main-belt asteroids; partial-quotient, Khinchin, Lévy, {r}-bin tests; Kirkwood-gap finder
analyze_kuiper.py 10 KB Test 14: 5 184 trans-Neptunian objects; narrow-window MMR pile-up test; three-population Khinchin / Lévy comparison

Original data source: NASA Exoplanet Archive Table Access Protocol (TAP), https://exoplanetarchive.ipac.caltech.edu/TAP/sync. The exact query used to produce ps_dm.csv is in §13 above.

The plots embedded in this page (partial_quotients.png, c1_mixture_fit.png, …) are produced as side-effects of these scripts and can be downloaded by right-clicking each figure.

↑ back to top

20. Bibliography

Observational astronomy

Celestial mechanics

Diophantine approximation and continued fractions

KAM theory and Hamiltonian stability

Statistical methods

↑ back to top

21. Changelog

Versioning policy: minor (vX.Y) bumps each iteration; major (vX.0) bumps when a foundational conjecture is added, retired, or reformulated, or when the page structure changes substantively. Conjecture I → I″ would historically have been a major bump (v1 → v2); the page was unversioned at that time so the change is recorded in v1.0 instead.

↑ back to top