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  "id": "oai:papers.qnfo.org:the-speed-of-light-as-a-regularization-parameter-of-a-distributed-estimation-vac",
  "slug": "the-speed-of-light-as-a-regularization-parameter-of-a-distributed-estimation-vac",
  "title": "The Speed of Light as a Regularization Parameter of a Distributed-Estimation Vacuum: Causal Bandwidth, Early-Stopping Suppression, and Latency Congestion",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "The speed of light c is measured, codified in the SI, and used daily, but it is normally treated as a kinematic axiom rather than as a parameter with a physical function. This paper develops and quantifies the conjecture that a finite causal speed acts as a regularization parameter of a vacuum modeled as a distributed estimation network, suppressing high-frequency divergences the way early stopping suppresses overfitting in iterative learning systems. We formalize the vacuum as a lattice of locally coupled estimators with signal velocity v, derive the dispersion relation ω(k) = (2v/a)sin(ka/2), and show that the causal bound |v_g| ≤ v yields a maximum angular frequency ω_max = 2v/a = 3.71× 10⁴3 mathrms^-1 and a channel bandwidth B = 5.905× 10⁴2 Hz for a lattice spacing at the Planck length. A spectral response model S(ω) = (1+(ω/ω_c)²)^-1 suppresses modes at 10 ω_c by a factor of 9.90× 10^-3 and modes at 100 ω_c by 9.999× 10^-5, a roll-off of approximately -20 dB per decade. A congestion model in which latency scales as τ = (L/v)(1+ρ/ρ_c) reproduces curvature-like delay effects parametrically, with a worked example giving Δτ = 3.336× 10^-12 s per metre at ρ/ρ_c = 10^-3. The result reframes c as a stability parameter and specifies simulations and laboratory tests that would confirm or refute the conjecture.",
  "abstract_markdown": "The speed of light $c$ is measured, codified in the SI, and used daily, but it is normally treated as a kinematic axiom rather than as a parameter with a physical function. This paper develops and quantifies the conjecture that a finite causal speed acts as a regularization parameter of a vacuum modeled as a distributed estimation network, suppressing high-frequency divergences the way early stopping suppresses overfitting in iterative learning systems. We formalize the vacuum as a lattice of locally coupled estimators with signal velocity $v$, derive the dispersion relation $\\omega(k) = (2v/a)\\sin(ka/2)$, and show that the causal bound $|v_g| \\le v$ yields a maximum angular frequency $\\omega_{\\max} = 2v/a = 3.71\\times 10^{43}\\ \\mathrm{s^{-1}}$ and a channel bandwidth $B = 5.905\\times 10^{42}\\ \\mathrm{Hz}$ for a lattice spacing at the Planck length. A spectral response model $S(\\omega) = (1+(\\omega/\\omega_c)^2)^{-1}$ suppresses modes at $10\\,\\omega_c$ by a factor of $9.90\\times 10^{-3}$ and modes at $100\\,\\omega_c$ by $9.999\\times 10^{-5}$, a roll-off of approximately $-20\\ \\mathrm{dB}$ per decade. A congestion model in which latency scales as $\\tau = (L/v)(1+\\rho/\\rho_c)$ reproduces curvature-like delay effects parametrically, with a worked example giving $\\Delta\\tau = 3.336\\times 10^{-12}\\ \\mathrm{s}$ per metre at $\\rho/\\rho_c = 10^{-3}$. The result reframes $c$ as a stability parameter and specifies simulations and laboratory tests that would confirm or refute the conjecture.",
  "date": "2026-10-17",
  "first_published": "2026-10-17",
  "last_revised": "2026-10-17T00:00:00Z",
  "modified": "2026-10-17T00:00:00Z",
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  "url": "https://papers.qnfo.org/papers/the-speed-of-light-as-a-regularization-parameter-of-a-distributed-estimation-vac",
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    "creators": [
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        "name": "Rowan Brad Quni-Gudzinas",
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    "titles": [
      {
        "title": "The Speed of Light as a Regularization Parameter of a Distributed-Estimation Vacuum: Causal Bandwidth, Early-Stopping Suppression, and Latency Congestion"
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        "description": "The speed of light c is measured, codified in the SI, and used daily, but it is normally treated as a kinematic axiom rather than as a parameter with a physical function. This paper develops and quantifies the conjecture that a finite causal speed acts as a regularization parameter of a vacuum modeled as a distributed estimation network, suppressing high-frequency divergences the way early stopping suppresses overfitting in iterative learning systems. We formalize the vacuum as a lattice of locally coupled estimators with signal velocity v, derive the dispersion relation ω(k) = (2v/a)sin(ka/2), and show that the causal bound |v_g| ≤ v yields a maximum angular frequency ω_max = 2v/a = 3.71× 10⁴3 mathrms^-1 and a channel bandwidth B = 5.905× 10⁴2 Hz for a lattice spacing at the Planck length. A spectral response model S(ω) = (1+(ω/ω_c)²)^-1 suppresses modes at 10 ω_c by a factor of 9.90× 10^-3 and modes at 100 ω_c by 9.999× 10^-5, a roll-off of approximately -20 dB per decade. A congestion model in which latency scales as τ = (L/v)(1+ρ/ρ_c) reproduces curvature-like delay effects parametrically, with a worked example giving Δτ = 3.336× 10^-12 s per metre at ρ/ρ_c = 10^-3. The result reframes c as a stability parameter and specifies simulations and laboratory tests that would confirm or refute the conjecture.",
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