A tomographic analysis of reflectometry data I: Component factorization

TL;DR

This paper introduces a spectral decomposition method using eigenvector projections to decompose multi-component signals, demonstrated on simulated and plasma reflectometry data.

Contribution

It presents a novel spectral decomposition technique based on unitary operator eigenvectors for analyzing multi-component signals.

Findings

Effective in separating signal components in simulated data.

Successfully applied to plasma reflectometry data from Tore Supra.

Enhances signal analysis by emphasizing different traits through unitary family choices.

Abstract

Many signals in Nature, technology and experiment have a multi-component structure. By spectral decomposition and projection on the eigenvectors of a family of unitary operators, a robust method is developed to decompose a signals in its components. Different signal traits may be emphasized by different choices of the unitary family. The method is illustrated in simulated data and on data obtained from plasma reflectometry experiments in the tore Supra.