Signed diagonal covariance comparison with the squarefree reference
ProvedErdos390.Full.SquarefreeCovarianceReference.abs_sigmaMixture_covII_diagonal_sub_reference_leanalytic-number-theoryerdos-390erdos390-source-construction
For a finite mixture of bounded valuation laws, let and be a moving-band prime. Assume every component has with , where is the paper divisibility main term. Suppose for the Dickman covariance kernel. With and reference diagonal , one has
The two scales separate the one-divisor profile error from the Bernoulli square and inserted kernel diagonal. The moving prime band is .
Preamble
import Mathlib import Definitions.Def_erdos390_source_bank_interface import Definitions.Def_erdos390_dickman_step_function import Theorems.Thm_MediumPNT import Definitions.Def_erdos390_analytic_foundations_001 import Theorems.Thm_Erdos390_Full_DickmanBasic_kernel_product_bound import Theorems.Thm_Erdos390_Full_DickmanBasic_rho_pos_on_zero_five import Theorems.Thm_Erdos390_Full_DickmanBasic_kernel_secondDerivative_first_bound import Definitions.Def_erdos390_analytic_foundations_003 import Definitions.Def_erdos390_analytic_foundations_004 import Definitions.Def_erdos390_analytic_foundations_005 universe u_1 u_2
Formal statement
theorem Erdos390.Full.SquarefreeCovarianceReference.abs_sigmaMixture_covII_diagonal_sub_reference_le :
∀ {Cell : Type u_1} [inst : Fintype.{u_1} Cell] {Omega : Cell → Type u_2}
[inst_1 : (c : Cell) → Fintype.{u_2} (Omega c)] {M : Nat} {E CKernel : Real},
LE.le.{0} (0 : Real) E →
∀ (weight : Erdos390.Full.FiniteProbability.{u_1} Cell)
(law : (c : Cell) → Erdos390.Full.PrimePowerCovariance.BoundedValuationLaw.{u_2} (Omega c) M) {n W p : Nat},
LT.lt.{0} (1 : Nat) n →
Membership.mem.{0, 0} (Erdos390.Full.ArithmeticModel.primeBand n W) p →
(∀ (c : Cell),
LE.le.{0}
(abs.{0}
(HSub.hSub.{0, 0, 0}
(Erdos390.Full.FiniteProbability.expect.{u_2}
(Erdos390.Full.PrimePowerCovariance.BoundedValuationLaw.probability.{u_2} (law c)) fun omega =>
Erdos390.Full.ArithmeticModel.divInd p
(Erdos390.Full.PrimePowerCovariance.BoundedValuationLaw.value.{u_2} (law c) omega))
(Erdos390.Full.PaperScaleMarkedCell.paperDivisibilityMain n p)))
(HMul.hMul.{0, 0, 0} E (Erdos390.Full.PaperPrimePowerChamberError.singleWeight p (1 : Nat)))) →
LE.le.{0}
(abs.{0}
(Erdos390.Full.ConditionedPoissonLimit.covarianceKernel (Erdos390.Full.ArithmeticModel.tPrime n p)
(Erdos390.Full.ArithmeticModel.tPrime n p)))
CKernel →
have mix := Erdos390.Full.PrimePowerCovariance.BoundedValuationLaw.sigmaMixture.{u_1, u_2} weight law;
LE.le.{0}
(abs.{0}
(HSub.hSub.{0, 0, 0}
(Erdos390.Full.PrimePowerCovariance.BoundedValuationLaw.covII.{max u_1 u_2} mix p p)
(Erdos390.Full.SquarefreeCovarianceReference.squarefreeReferenceEntry n p p)))
(HAdd.hAdd.{0, 0, 0} (HDiv.hDiv.{0, 0, 0} (HMul.hMul.{0, 0, 0} (2 : Real) E) ↑p)
(HDiv.hDiv.{0, 0, 0}
(HAdd.hAdd.{0, 0, 0}
(HPow.hPow.{0, 0, 0}
(HAdd.hAdd.{0, 0, 0}
(HDiv.hDiv.{0, 0, 0} (1 : Real) (Erdos390.Full.DickmanBasic.rho Erdos390.Full.DickmanBasic.U))
(HMul.hMul.{0, 0, 0} (2 : Real) E))
(2 : Nat))
CKernel)
(HPow.hPow.{0, 0, 0} ↑p (2 : Nat)))) := by sorry
Source