Dossier B.1: The Search for a Seam (Born Rule Deviation)

B.1.1. Core Hypothesis (The Falsifier): A weak, phase-synchronized resonant driving field, applied during the measurement (collapse) window of a quantum system prepared in a superposition, will produce a statistically significant, reversible deviation from the probabilities predicted by the Born rule. (RC-3)

B.1.2. Experimental Protocol 1: Photon Polarization

Setup: Use a single-photon source and a Polarizing Beam Splitter (PBS)

to prepare photons in a 50/50 superposition of horizontal (|H⟩) and vertical (|V⟩) polarization.

Intervention (The Resonant Nudge): Place an electro-optic modulator

(EOM) just before the PBS. The EOM is triggered to apply a very fast, weak electrical pulse during the exact ∼nanosecond window of measurement/collapse. This pulse is phase-synced to resonate with one polarization state (e.g., |H⟩). The pulse must be weak enough that it does not classically rotate the state (verified by pre-measurement tomography).

Procedure:
Baseline Run: Record 10⁶ events with the EOM off to establish a

P = 50.00% baseline.

Test Run: Record 5 × 10⁶ events with the EOM pulse applied

(phase-locked to |H⟩).

Reversal Run: Record 5 × 10⁶ events with the EOM pulse phase

inverted by 180^(∘) (favoring |V⟩).

Predicted Outcome (FRC Support): The "drive on" events will show a

statistically significant bias, e.g., P(|H⟩) = 50.05% (a deviation of ΔP ≈ 5 × 10⁻⁴). The bias must flip sign in the Reversal Run.

Falsification Condition: If, after 10⁷ trials, no statistically

significant, reversible bias above ΔP < 5 × 10⁻⁴ (a five-sigma deviation) is detected, the core physical claim of Resonant Collapse is falsified.

B.1.3. Experimental Protocol 2: Superconducting Qubits

Setup: Use a transmon qubit prepared in a |0⟩ + |1⟩ superposition (the

foundation of quantum computing).

Intervention: During the highly controllable qubit readout process,

apply a very weak, precisely shaped microwave pulse phase-synced to resonate with the |1⟩ state.

Advantage: This platform offers high timing precision, allowing for a

more accurate search for the exact "collapse window" where the resonant nudge has maximum effect.

Falsification Condition: Same as above. The core challenge is the

precision required to apply the nudge during the rapid, femtosecond-scale collapse window.

--- Dossier B.2: The Harmony of the Elements (Spectral Clustering)

B.2.1. Core Hypothesis: The collected emission spectra of complex neutral atoms exhibit a non-random harmonic structure of "overtones" not predicted by the standard model of discrete electron transitions alone.

B.2.2. Data and Method:

Data Source: The public NIST (National Institute of Standards and

Technology) Atomic Spectra Database, containing certified line lists and relative intensities for complex elements (e.g., Iron, Cobalt).

Methodology:

Transform: Convert the discrete list of lines into a continuous

spectral density function, S(ν).

Harmonic Analysis (Fourier Transform): Compute the Fourier

transform, F(f) = |ℱ[S(ν)]|, to identify dominant "beat frequencies" (f_(n)).

Coherence Score (RC-12): Calculate the Harmonic Conformity Score

H(f₀) to find a fundamental frequency (f₀) whose integer multiples (nf₀) yield statistically significant peaks.

Predicted Outcome (FRC Support): The Fourier spectrum F(f) will not be

flat noise. It will show sharp, statistically significant peaks at f₀ and its integer multiples (2f₀, 3f₀, …).

Falsification Condition: If the Fourier spectra for multiple complex

elements, after accounting for known instrumental artifacts, show no harmonic peaks that significantly exceed a robust statistical null model, the claim that atomic structure is governed by universal harmonic resonance is falsified.

B.2.3. Critical Controls:

Differential Analysis (The Iron-Cobalt Test): Perform the analysis on

two adjacent elements (e.g., Iron and Cobalt). A universal harmonic law should persist across both, with the fundamental frequency possibly shifting predictably based on nuclear charge/electron count.

Statistical Null Model: Generate thousands of randomized "fake"

spectra by slightly jittering the real line positions. Use this large sample to build a robust statistical boundary for what "random" looks like, proving that the real data's peaks are extreme outliers.

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