# Model subspaces techniques to study Fourier expansions in L^2 spaces associated to singular measures

## TL;DR

This paper explores Fourier expansions in L^2 spaces with singular measures using model subspaces of the Hardy space, identifying frames and characterizing measures related to reproducing kernels.

## Contribution

It introduces a novel approach linking Fourier expansions with model subspaces and characterizes measures reproducing specific kernels in Hardy spaces.

## Key findings

- Identifies frames in L^2(μ) as boundary values of model subspace projections.
- Provides a complete characterization of measures reproducing kernels in model subspaces.
- Analyzes the implications of these characterizations for Fourier analysis with singular measures.

## Abstract

Let $\mu$ be a probability measure on $\mathbb{T}$ that is singular with respect to the Haar measure. In this paper we study Fourier expansions in $L^2(\mathbb{T},\mu)$ using techniques from the theory of model subspaces of the Hardy space. Since the sequence of monomials $\{z^n\}_{n\in \mathbb{N}}$ is effective in $L^2(\mathbb{T},\mu)$, it has a Parseval frame associated via the Kaczmarz algorithm. Our first main goal is to identify the aforementioned frame with boundary values of the frame $P_\varphi(z^n)$ for the model subspace $\mathcal{H}(\varphi)= H^2 \ominus \varphi H^2$, where $P_\varphi$ is the orthogonal projection from the Hardy space $H^2$ onto $\mathcal{H}(\varphi)$. The study of Fourier expansions in $L^2(\mathbb{T},\mu)$ also leads to consider positive kernels in the Hardy space. Our second main goal is to study the set of measures $\mu$ which reproduce a kernel contained in a model subspace. We completely characterize this set when the kernel is the reproducing kernel of a model subspace, and we study the consequences of this characterization.