Weighted multiple ergodic averages and correlation sequences

TL;DR

This paper investigates the conditions under which weighted multiple ergodic averages converge, characterizing universal weights via correlation with nilsequences and establishing decomposition results for bounded sequences.

Contribution

It introduces new decomposition theorems for bounded sequences and correlation sequences, extending previous results and applying them to ergodic averages and recurrence in dynamical systems.

Findings

Bounded sequences that are good weights are characterized by their correlation with nilsequences.

Every bounded sequence satisfying regularity conditions decomposes into a nilsequence plus a small uniformity norm sequence.

Multiple correlation sequences can be decomposed into a nilsequence plus a sequence small in uniform density.

Abstract

We study mean convergence results for weighted multiple ergodic averages defined by commuting transformations with iterates given by integer polynomials in several variables. Roughly speaking, we prove that a bounded sequence is a good universal weight for mean convergence of such averages if and only if the averages of this sequence times any nilsequence converge. Key role in the proof play two decomposition results of independent interest. The first states that every bounded sequence in several variables satisfying some regularity conditions is a sum of a nilsequence and a sequence that has small uniformity norm (this generalizes a result of the second author and B. Kra); and the second states that every multiple correlation sequence in several variables is a sum of a nilsequence and a sequence that is small in uniform density (this generalizes a result of the first author). Furthermore, we use the previous results in order to establish mean convergence and recurrence results for a variety of sequences of dynamical and arithmetic origin and give some combinatorial implications.