Tao Lemma 4.4: Montgomery's uncertainty principle
ProvedTaoFivePrimes.montgomery_uncertaintyFor a cutoff , a modulus , a scale and a frequency , write
where is the von Mangoldt function and . Let divide , let , and let vanish on . Then
where is the Möbius function and Euler's totient. For not squarefree the right-hand side vanishes and the inequality is trivial; the content is the squarefree case, where .
This is Montgomery's uncertainty principle: the mass of a prime-supported exponential sum cannot concentrate at a single frequency, since shifting by the reduced fractions must recover a definite proportion of it. It is what drives the local estimate (Lemma 4.6) and hence Corollary 4.7, the upper bound on the major-arc mass. Its degenerate case is the anti-symmetry of equation (4.6), and the case of a prime reads .
Formalization Note The residues modulo are represented by the integers coprime to . The Möbius function takes integer values, and its square is cast to a real number; the quotient by is the real division, which is harmless since for .
import Mathlib import Definitions.Def_TaoFivePrimes_SmoothedExpSum open Finset
theorem TaoFivePrimes.montgomery_uncertainty
(eta : ℝ → ℝ) (q q₀ : ℕ) (hq₀ : 0 < q₀) (hdvd : q₀ ∣ q)
(x alpha : ℝ) (hx : 1 ≤ x) (hsupp : ∀ t : ℝ, 1 < t → eta t = 0) :
((ArithmeticFunction.moebius q₀ : ℝ) ^ 2 / (Nat.totient q₀ : ℝ))
* ‖TaoFivePrimes.smoothedExpSum eta q x alpha‖ ^ 2
≤ ∑ a ∈ (Finset.range q₀).filter (fun a => Nat.Coprime a q₀),
‖TaoFivePrimes.smoothedExpSum eta q x (alpha + (a : ℝ) / q₀)‖ ^ 2 := by sorry