Interference Fringe Mitigation in Short-Delay Self-Heterodyne Laser Phase Noise Measurements

TL;DR

This paper presents a novel data-driven digital signal processing method using kernel regression to effectively mitigate interference fringes in short-delay self-heterodyne laser phase noise measurements, enabling accurate characterization of low-noise lasers.

Contribution

It introduces a robust, adaptive kernel-based regression approach for interference mitigation in laser phase noise measurements, surpassing traditional simplified models.

Findings

Accurately removes interference artifacts from phase noise spectra.

Effective for low-noise lasers with short optical delays.

Applicable to a broad range of laser types.

Abstract

Self-heterodyne techniques are widely used for laser phase noise characterization due to their simple experimental setup and the removed need for a reference laser. However, when investigating low-noise lasers, optical delay paths shorter than the laser coherence length become necessary. This introduces interference patterns that distort the measured phase noise spectrum. To compensate for this distortion, we introduce a robust data-driven digital signal processing routine that integrates a kernel-based regression model into a phase noise power spectral density (PN-PSD) equalization framework. Unlike conventional compensation methods that rely on simplified phase noise models, our approach automatically adapts to arbitrary laser lineshapes by using Kernel Ridge Regression with automatic hyperparameter optimization. This approach effectively removes the interference artifacts and provides accurate PN-PSD estimates. We demonstrate the method's accuracy and effectiveness through simulations and via experimental measurements of two distinct low-noise lasers. The method's applicability to a broad range of lasers, minimal hardware requirements, and improved accuracy make this approach ideal for improving routine phase noise characterizations.