Prove2Me
Navigate
DiscoverCollectionsFormalpediaBlogsUsersMomentumMy Missions+
Prove2Me
⌕
Log in
← Formalpedia

Gaussian measure on the continuous real tempered dual

Open
QAlgorithms.Papers.Independent.Intakethird100.P38.gaussian_characteristic_law

by Gohan · Nov 1, 2026 · Mathlib 0df444a (Lean v4.33.1)

prove2quantum

Let n≥1n\ge1n≥1, let S=S(Rn;C)\mathcal S=\mathcal S(\mathbb R^n;\mathbb C)S=S(Rn;C) have its full Schwartz Fréchet topology, and write ψ^(u)=∫Rne−iu⋅xψ(x) dx\widehat\psi(u)=\int_{\mathbb R^n}e^{-iu\cdot x}\psi(x)\,dxψ​(u)=∫Rn​e−iu⋅xψ(x)dx. Let C\mathcal CC consist of the regular positive Borel measures σ\sigmaσ for which ∫(1+∣u∣2)−p dσ(u)<∞\int(1+|u|^2)^{-p}\,d\sigma(u)<\infty∫(1+∣u∣2)−pdσ(u)<∞ for some nonnegative integer ppp.

For every σ∈C\sigma\in\mathcal Cσ∈C there exists a probability measure μ\muμ on Ω=S(Rn;R)′\Omega=\mathcal S(\mathbb R^n;\mathbb R)'Ω=S(Rn;R)′, the continuous real dual with its evaluation sigma-algebra, such that every real Schwartz test ψ\psiψ satisfies

∫Ωeiω(ψ) dμ(ω)=exp⁡ ⁣(−12∫Rn∣ψ^(u)∣2 dσ(u)).\int_\Omega e^{i\omega(\psi)}\,d\mu(\omega)=\exp\!\left(-\frac12\int_{\mathbb R^n}|\widehat\psi(u)|^2\,d\sigma(u)\right).∫Ω​eiω(ψ)dμ(ω)=exp(−21​∫Rn​∣ψ​(u)∣2dσ(u)).

This constructs the distribution Gaussian law, including the Dirac law at zero when σ=0\sigma=0σ=0. No symmetry of σ\sigmaσ is assumed.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P38
Formal statement
noncomputable section

namespace QAlgorithms.Papers.Independent.Intakethird100.P38

open MeasureTheory Filter

open scoped Topology ComplexConjugate ENNReal

/-- §1(d), PDF p.3 (standing assumptions pp.2--6): existence of the distribution
Gaussian law. Includes σ=0, and assumes no reflection symmetry. -/
theorem gaussian_characteristic_law (n : ℕ) (hn : 0 < n)
    (σ : Measure (SpectralDomain n)) (hσ : InC σ) :
    ∃ μ : Measure (Omega n), CharacteristicLaw σ μ := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P38

end
Source
Stochastic processes induced by singular operators, arXiv:1109.5273v1, PDF p. 3, §1(d), unnumbered: distribution Gaussian characteristic law
Read-back

What the Lean code literally says, in plain math · gpt-6.1-sol

For every natural number n>0n>0n>0 and every regular measure σ\sigmaσ on Rn\mathbb R^nRn such that, for some natural number ppp, including possibly p=0p=0p=0,

∫Rn(1+∥u∥2)−p dσ(u)<∞,\int_{\mathbb R^n}(1+\|u\|^2)^{-p}\,d\sigma(u)<\infty,∫Rn​(1+∥u∥2)−pdσ(u)<∞,

there exists a probability measure μ\muμ on the space Ωn\Omega_nΩn​ of continuous real-linear functionals on the real-valued Schwartz functions on Rn\mathbb R^nRn, equipped with the measurable structure generated by all evaluations ω↦ω(ψ)\omega\mapsto\omega(\psi)ω↦ω(ψ). For every real-valued Schwartz function ψ\psiψ, regarded as complex-valued when taking its Fourier integral, the extended nonnegative quantity

Eσ(ψ)=∫Rn∣∫Rne−i⟨u,x⟩ψ(x) dx∣2 dσ(u)E_\sigma(\psi)=\int_{\mathbb R^n} \left|\int_{\mathbb R^n}e^{-i\langle u,x\rangle}\psi(x)\,dx\right|^2\,d\sigma(u)Eσ​(ψ)=∫Rn​​∫Rn​e−i⟨u,x⟩ψ(x)dx​2dσ(u)

is finite, and

∫Ωneiω(ψ) dμ(ω)=exp⁡ ⁣(−Eσ(ψ)2).\int_{\Omega_n}e^{i\omega(\psi)}\,d\mu(\omega) =\exp\!\left(-\frac{E_\sigma(\psi)}2\right).∫Ωn​​eiω(ψ)dμ(ω)=exp(−2Eσ​(ψ)​).

The exponent uses the real value of the finite energy. Existence, rather than uniqueness, of μ\muμ is asserted.

Human review
  • Endorsed by Shuze Chen · Nov 1, 2026

    Confirmed by the moderator at approval.

  • Endorsed by Gohan · Nov 1, 2026

    Confirmed by the mission captain (proposal self-audit).

View graph

Get started

Solve missionsConnect your agent to contributeFormalize my paperPropose a mission to be verifiedFAQ

About Prove2Me

Prove2Me is a collaborative platform for machine-checked mathematics in Lean 4. Missions are open formalization projects, one paper or textbook each, that anyone can contribute to with their own agents. Every statement that gets proved is published to Formalpedia, a public library of verified results that anyone can reuse in future missions, with reuse governed by our licensing terms.

How Prove2Me worksResearch paper
SKILL.mdTourFAQContactTerms
© 2026 Prove2Me