Universal analytic properties of noise. Introducing the J-Matrix formalism

TL;DR

This paper introduces the J-Matrix formalism, a spectral analysis method that separates noise from signals in damped oscillators using a novel operator approach based on Padé Approximations and the Z-transform.

Contribution

The paper presents a new J-Matrix formalism that models noise and signals in the complex plane, revealing universal properties of noise distributions in spectral analysis.

Findings

Poles and zeros tend to a uniform distribution on the unit circle as data length increases

The J-operator effectively separates signal from noise in spectral analysis

Noise roots in the complex plane act as attractors at roots of unity

Abstract

We propose a new method in the spectral analysis of noisy time-series data for damped oscillators. From the Jacobi three terms recursive relation for the denominators of the Pad\'e Approximations built on the well-known Z-transform of an infinite time-series, we build an Hilbert space operator, a J-Operator, where each bound state (inside the unit circle in the complex plane) is simply associated to one damped oscillator while the continuous spectrum of the J-Operator, which lies on the unit circle itself, is shown to represent the noise. Signal and noise are thus clearly separated in the complex plane. For a finite time series of length 2N, the J-operator is replaced by a finite order J-Matrix J_N, having N eigenvalues which are time reversal covariant. Different classes of input noise, such as blank (white and uniform), Gaussian and pink, are discussed in detail, the J-Matrix formalism allowing us to efficiently calculate hundreds of poles of the Z-transform. Evidence of a universal behaviour in the final statistical distribution of the associated poles and zeros of the Z-transform is shown. In particular the poles and zeros tend, when the length of the time series goes to infinity, to a uniform angular distribution on the unit circle. Therefore at finite order, the roots of unity in the complex plane appear to be noise attractors. We show that the Z-transform presents the exceptional feature of allowing lossless undersampling and how to make use of this property. A few basic examples are given to suggest the power of the proposed method.