The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
n_spacings: int64
unfolded_mean: double
unfolded_var: double
KS_vs_GUE: double
KS_vs_Poisson: double
hist_binwidth: double
hist_counts: list<item: double>
child 0, item: double
P_gap_lt_0.3: struct<empirical: double, GUE: double, Poisson: double>
child 0, empirical: double
child 1, GUE: double
child 2, Poisson: double
unfolded_spacings: list<item: double>
child 0, item: double
note: string
date: timestamp[s]
HONEST_SCOPE: string
5_weyl_counting: string
operator: string
2_boundary_maslov_phase: struct<claim: string, evidence: string, code: string>
child 0, claim: string
child 1, evidence: string
child 2, code: string
4_sum_rule: struct<identity: string, reason: string, code: string>
child 0, identity: string
child 1, reason: string
child 2, code: string
1_closed_form_small_a: struct<formula: string, equivalently: string, status: string, code: string>
child 0, formula: string
child 1, equivalently: string
child 2, status: string
child 3, code: string
3_prime_onset_sign_law: struct<statement: string, proof_sketch: string, honest_note: string, code: string>
child 0, statement: string
child 1, proof_sketch: string
child 2, honest_note: string
child 3, code: string
producer: string
to
{'date': Value('timestamp[s]'), 'operator': Value('string'), 'producer': Value('string'), '1_closed_form_small_a': {'formula': Value('string'), 'equivalently': Value('string'), 'status': Value('string'), 'code': Value('string')}, '2_boundary_maslov_phase': {'claim': Value('string'), 'evidence': Value('string'), 'code': Value('string')}, '3_prime_onset_sign_law': {'statement': Value('string'), 'proof_sketch': Value('string'), 'honest_note': Value('string'), 'code': Value('string')}, '4_sum_rule': {'identity': Value('string'), 'reason': Value('string'), 'code': Value('string')}, '5_weyl_counting': Value('string'), 'HONEST_SCOPE': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
n_spacings: int64
unfolded_mean: double
unfolded_var: double
KS_vs_GUE: double
KS_vs_Poisson: double
hist_binwidth: double
hist_counts: list<item: double>
child 0, item: double
P_gap_lt_0.3: struct<empirical: double, GUE: double, Poisson: double>
child 0, empirical: double
child 1, GUE: double
child 2, Poisson: double
unfolded_spacings: list<item: double>
child 0, item: double
note: string
date: timestamp[s]
HONEST_SCOPE: string
5_weyl_counting: string
operator: string
2_boundary_maslov_phase: struct<claim: string, evidence: string, code: string>
child 0, claim: string
child 1, evidence: string
child 2, code: string
4_sum_rule: struct<identity: string, reason: string, code: string>
child 0, identity: string
child 1, reason: string
child 2, code: string
1_closed_form_small_a: struct<formula: string, equivalently: string, status: string, code: string>
child 0, formula: string
child 1, equivalently: string
child 2, status: string
child 3, code: string
3_prime_onset_sign_law: struct<statement: string, proof_sketch: string, honest_note: string, code: string>
child 0, statement: string
child 1, proof_sketch: string
child 2, honest_note: string
child 3, code: string
producer: string
to
{'date': Value('timestamp[s]'), 'operator': Value('string'), 'producer': Value('string'), '1_closed_form_small_a': {'formula': Value('string'), 'equivalently': Value('string'), 'status': Value('string'), 'code': Value('string')}, '2_boundary_maslov_phase': {'claim': Value('string'), 'evidence': Value('string'), 'code': Value('string')}, '3_prime_onset_sign_law': {'statement': Value('string'), 'proof_sketch': Value('string'), 'honest_note': Value('string'), 'code': Value('string')}, '4_sum_rule': {'identity': Value('string'), 'reason': Value('string'), 'code': Value('string')}, '5_weyl_counting': Value('string'), 'HONEST_SCOPE': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
A Numerical Spectral Portrait of Suzuki's Weil-Form Operator
This repository contains the data, code, and audio behind the interactive demo Hear the Riemann Zeta Zeros. Its scientific core is, to the best of our knowledge, the first numerical realization of the self-adjoint operator constructed by M. Suzuki in Weil's quadratic form via the screw function (arXiv:2606.09096, 2026) — a theory-only paper that contains no numerics — together with a closed-form characterization of that operator's small-scale spectrum.
Everything here is fully reproducible with standard open-source tools (numpy, scipy,
mpmath); no special hardware is required.
⚠️ Honest scope (read first)
This repository is a numerical spectral characterization of a 2026 operator. It is not a proof of, or progress toward, the Riemann Hypothesis (RH), and it makes no such claim.
More precisely:
- All spectral results below are archimedean / universal: they are governed by the
½·t·log|t|(Gamma-factor) singularity of the screw function, they are the same for every L-function, and they are not specific to the Riemann zeta zeros. The zeta zeros live in the large-aregime and require the full prime sum; the clean small-aresults here are, by construction, prime-free. - "First realization" is stated to the best of our knowledge.
- The closed form for the constants (Result 1) is a strong 1σ candidate, not a proof: the
additive constant matches
−2 ln 2to one standard error, and a full confirmation would require~1e-6accuracy. - The GUE / critical-line data reproduce established facts (Montgomery 1972; Odlyzko 1987); they are toolchain certification on the real object, not new mathematics.
- The sonification is an educational / artistic mapping, not a mathematical claim.
Corrections and independent reproduction are welcome.
Summary of results
We discretize the Weil quadratic form Q_W^a(v) = ∬_{(-a,a)²} g(x−y) v'(x) v'(y) dx dy on
H₀¹(−a,a) with P1 finite elements, solve the generalized eigenproblem Q v = λ M v, and write
the eigenvalues as λ_k(a) = log(1/a) + μ_k + O(a). Suzuki's Theorem 1.4 gives the leading
log(1/a) and asserts the existence of a lowest constant μ₁ > 0 but leaves it undetermined.
| # | Result | Statement | Script | Status |
|---|---|---|---|---|
| 1 | Closed form | μ_k = ln(k − ½) + γ + ln(π/2), i.e. additive constant B₀ = −2 ln 2; R² = 1.000000 over 40 modes |
suzuki_closedform.py |
strong 1σ candidate |
| 2 | −½ = boundary phase |
Sweeping Dirichlet→Neumann moves the offset c: −0.4946 → +0.4971, a clean integer shift Δc ≈ +1 (½ per endpoint) — a Maslov / winding phase |
suzuki_boundary_maslov.py |
numerically clean |
| 3 | Prime-onset sign law | A prime power n switches on exactly at a = log(n)/2; even eigenfunctions shift down, odd shift up (one-line parity proof); shift is linear in the von Mangoldt weight Λ(n)/√n, read back to 0.08% (n=2) |
suzuki_onset_lemma.py, suzuki_comb_weights.py |
proved (sign) + measured (weight) |
| 4 | Conservation | Σ_k δλ_k = 0 exactly: primes redistribute eigenvalues without shifting the total (off-diagonal, trace-preserving perturbation) |
suzuki_sumrule.py |
exact |
| 5 | Weyl counting | N(μ) ~ e^μ · 2/(π e^γ); exp(μ_k) ∝ (k − ½) is a Landau-type equally-spaced ladder — a "logarithm of Landau levels" |
suzuki_spectrum.py |
derived from Result 1 |
| — | GUE certification | Nearest-neighbour spacings of the first 100 zeros: KS distance to GUE ≈ 0.068 vs to Poisson ≈ 0.349 | gen_dataset.py |
reproduces known result |
The half-integer offset in Result 2 was anticipated by VIDRAFT's circulation-phase hypothesis
— from the conservation law Σ δλ = 0 and the even/odd (parity) alternation of the ladder, we
conjectured that −½ is not a static constant but a "once-around" winding phase, then confirmed
it numerically by the boundary sweep.
Files
Suzuki Weil-form operator (the scientific core)
| File | Contents |
|---|---|
suzuki_closedform.py |
P1-FEM assembly of Q_W^a; Richardson N-extrapolation (N=1000, 2000) and a-quadratic fit of λ_k(a); recovers μ_k = ln(k−½) + γ + ln(π/2). Prints B₀ = −1.38612 ± 1.7e-4 vs −2 ln 2 = −1.38629. |
suzuki_boundary_maslov.py |
Sweeps the endpoint stiffness from Dirichlet (v=0) to Neumann (v'=0) and fits the offset c at each end; demonstrates the integer shift Δc ≈ +1. |
suzuki_onset_lemma.py |
First-order perturbation theory for the prime-onset at a = log(n)/2; verifies the even-down / odd-up sign law (8/8 modes) and the pre-threshold null (δλ ≡ 0 for 2a < log n). |
suzuki_comb_weights.py |
Cluster-trace extraction of the von Mangoldt weight Λ(n)/√n from the eigenvalue shifts (0.08% for n=2, 1.4% for n=3 at a degeneracy). |
suzuki_sumrule.py |
Trace identity Tr(M⁻¹ dQ) = Σ δλ_k = 0; also reports the parity-graded trace (which is not a clean sum rule — honestly labeled). |
suzuki_spectrum.py / suzuki_spectrum.json |
Full low-lying spectrum vs a, the Weyl counting function, and the Landau-ladder check. |
suzuki_mu1.json |
Legacy single-constant record: the lowest eigenvalue λ_a and the first numerical value μ₁ ≈ 0.235 (superseded by the closed form μ₁ = ln(½) + γ + ln(π/2) in suzuki_closedform.py). |
Riemann zeta zeros and GUE statistics
| File | Contents |
|---|---|
zeta_zeros.json |
First 100 nontrivial zeros γ_k (imaginary parts), all with Re(s) = ½; `max |
gue_spacings.json |
Unfolded nearest-neighbour spacings, histogram, KS distances to the GUE Wigner surmise and to Poisson, and the small-gap fractions (the level-repulsion signature). |
gen_dataset.py |
Regenerates all three JSONs from scratch (mpmath + numpy/scipy). |
Sonification ("music of the primes")
| File | Contents |
|---|---|
song.wav |
38.9 s arrangement: the first 40 zeros mapped to an A-minor lead melody over an Am–F–C–G progression with synth drums and bass. |
render_song.py |
Offline numpy WAV renderer for song.wav. |
index.html |
The full interactive demo page (also hosted as the Space above). |
Documentation
| File | Contents |
|---|---|
findings_2026-07-12.json |
Machine-readable summary of the five findings, with an explicit HONEST_SCOPE block. |
NARRATIVE.md |
Short prose summary with the honest-scope statement. |
ARTICLE.md |
Longer expository write-up (background, methods, interpretation). |
Key numbers
- Critical line: all 100 zeros have
Re(s) = ½;max|ζ|at those heights≈ 1e-24. - GUE level repulsion: KS distance to GUE
≈ 0.068, to Poisson≈ 0.349.P(gap < 0.3): empirical≈ 0vs Poisson≈ 0.26. - Closed-form constant:
B₀ = −1.38612 ± 1.7e-4vs−2 ln 2 = −1.38629(1σ agreement), soμ_k = ln(k − ½) + γ + ln(π/2)withR² = 1.000000over 40 modes. - Boundary phase:
c(Dirichlet) = −0.4946,c(Neumann) = +0.4971,Δc = +0.99 ≈ +1. - Weight readout: von Mangoldt
Λ(2)/√2recovered to 0.08%,Λ(3)/√3to 1.4%.
Methods (brief)
Operator (Results 1–5). We discretize the Weil quadratic form
Q_W^a(v) = ∬ g(x−y) v'(x) v'(y) with g the screw function of ζ (eq. 1.3 of arXiv:2606.09096),
using P1 hat functions on a uniform mesh of H₀¹(−a,a). The stiffness-like matrix Q is
assembled from the double antiderivative G₂ of g (a Toeplitz construction), and the mass
matrix M is the standard P1 mass matrix; eigenvalues come from scipy.linalg.eigh(Q, M).
N-convergence of reported eigenvalues is ~1e-4 (Richardson from N = 1000, 2000). For
2a < log 2 the von Mangoldt sum in g is empty, so the small-a spectrum is prime-free and
well conditioned; the constants μ_k are extracted by an a-quadratic fit over
a ∈ [0.02, 0.05]. The prime-onset study (Result 3) adds a single ramp term
g_n(t) = (Λ(n)/√n)·(2a − |t|)_+ that switches on at a = log(n)/2, and reads its first-order
effect via perturbation theory δλ_k ∝ v_k'(a) v_k'(−a).
Zeros / GUE (certification). Zeros from mpmath.zetazero; unfolding by the density
ρ(t) = (1/2π) log(t/2π); KS distances against the GUE Wigner surmise
p(s) = (32/π²) s² e^{−4s²/π} and against Poisson e^{−s}. This reproduces the classic Montgomery
(1972) / Odlyzko (1987) result at low height (the razor-sharp match is at height ~10²⁰).
Reproduce
pip install numpy scipy mpmath
# Suzuki operator — one script per result
python suzuki_closedform.py # 1) μ_k = ln(k−½) + γ + ln(π/2)
python suzuki_boundary_maslov.py # 2) −½ = boundary Maslov phase
python suzuki_onset_lemma.py # 3) prime-onset sign law
python suzuki_comb_weights.py # 3) von Mangoldt weight readout
python suzuki_sumrule.py # 4) Σ δλ = 0 conservation
python suzuki_spectrum.py # 5) Weyl counting / Landau ladder
# Zeta zeros, GUE statistics, and the audio
python gen_dataset.py # zeta_zeros.json, gue_spacings.json, suzuki_mu1.json
python render_song.py # song.wav
Relation to Suzuki (arXiv:2606.09096)
Suzuki's paper is purely analytic: it constructs the operator, proves self-adjointness and the
leading small-a asymptotic (Theorem 1.4), and connects the Weil quadratic form to the explicit
formula — but it reports no numerical spectrum and leaves the constants μ_k undetermined. This
repository supplies (i) the first numerical spectrum, (ii) a closed-form candidate for the μ_k,
(iii) the geometric identification of the −½ offset, and (iv) the prime-onset sign law and its
weight readout. Two questions we would ask the author: is the closed form what the construction
predicts (can γ + ln(π/2) and the −½ be derived analytically)? and is there a clean
asymptotic law for the onset magnitude (whose exponent we find to be mode-dependent, ν ≈ 1–3.6,
so a naive ε³ guess is refuted)?
Citation / lineage
The "music of the primes" idea follows Michael Berry (quantum chaos) and Marcus du Sautoy (The Music of the Primes). The operator whose spectrum we characterize is:
M. Suzuki, Weil's quadratic form via the screw function, arXiv:2606.09096 (2026).
References
- M. Suzuki, Weil's quadratic form via the screw function, arXiv:2606.09096 (2026).
- H. L. Montgomery, The pair correlation of zeros of the zeta function, Proc. Sympos. Pure Math. 24 (1973).
- A. M. Odlyzko, On the distribution of spacings between zeros of the zeta function, Math. Comp. 48 (1987).
- H. Widom, spectral asymptotics of integral operators with logarithmic kernels.
- Chen–Weth, spectral properties of the logarithmic Laplacian (context for the archimedean constants).
Produced by VIDRAFT. Data is honest and reproducible; corrections welcome.
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