claimroutineinferred_dep

Selected low-pass dominated by envelope and maximal function

Dominates selected low-pass pieces by a maximal function envelope
removes dependence on the cutoff for selected pieces
Claim·§B.3 Summation over the spatial and angular frequencies·inside thmSharp logarithm with angular regularity

Statement (paper)

For rr with m(j,r)≥0m(j,r)\ge0, the contribution U(t)ΠNjSm(j,r)ajU(t)\Pi_{N_j}S_{m(j,r)}a_j at (t,rθ)(t,r\theta) is controlled by the profile GMSdfjG_{\mathcal M_{\mathbb S^d}f_j} of Lemma , with MSdfj\mathcal M_{\mathbb S^d}f_j in place of Sm(j,r)fjS_{m(j,r)}f_j, so the profile does not depend on the cutoff m(j,r)m(j,r). Moreover, for arbitrary nonnegative coefficients cjc_j, ∥(cjGMSdfj)j∥Lp( dv dθ;ℓ2)≲∥(cjfj)j∥Lp(Rn;ℓ2)\|(c_jG_{\mathcal M_{\mathbb S^d}f_j})_j\|_{L^p(\dd v\dd\theta;\ell^2)}\lesssim\|(c_jf_j)_j\|_{L^p(\RR^n;\ell^2)}.

In plain English

Where the cutoff satisfies m(j,r)≥0m(j,r)\ge0, the piece U(t)ΠNjSm(j,r)ajU(t)\Pi_{N_j}S_{m(j,r)}a_j at (t,rθ)(t,r\theta) is bounded by the envelope profile GMSdfjG_{\mathcal M_{\mathbb S^d}f_j}, which does not depend on the cutoff m(j,r)m(j,r). For any nonnegative coefficients cjc_j, the ℓ2\ell^2-valued Lp( dv dθ)L^p(\dd v\dd\theta) norm of (cjGMSdfj)j(c_jG_{\mathcal M_{\mathbb S^d}f_j})_j is at most a constant times ∥(cjfj)j∥Lp(Rn;ℓ2)\|(c_jf_j)_j\|_{L^p(\RR^n;\ell^2)}.

Why it's here

Applying the angular envelope lemma to the low-pass piece gives a pointwise bound by a profile of Sm(j,r)fjS_{m(j,r)}f_j. Replacing Sm(j,r)fjS_{m(j,r)}f_j by the spherical maximal function MSdfj\mathcal M_{\mathbb S^d}f_j removes the dependence on rr through the cutoff, and the vector-valued maximal inequality then converts the profile norm into the norm of (cjfj)(c_jf_j). This lets the selected pieces be summed over jj and bounded in the strong and weak estimates that follow.
Classified routine:
Follows from the angular envelope lemma by dominating the angular projection SmfjS_{m}f_j pointwise by the spherical maximal function, then applying the Fefferman–Stein vector-valued maximal inequality (Fefferman–Stein 1971) to bound the f f f-valued profile norm.

Sources

PaperOpen
Section
§B.3 Summation over the spatial and angular frequencies
Page
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Label
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Line
sections/angular.tex:308
Leannot aligned

Phase 2 aligns this node to a Lean declaration.

Friction

  • info
    inferred_dep
    outline: "Selected low-pass via envelope and vector maximal" rests on lem:angular-envelope
  • info
    inferred_dep
    outline: "Selected low-pass via envelope and vector maximal" rests on claim-thm-angular-1