Optical Frequency Comb Noise Characterization Using Machine Learning

TL;DR

This paper introduces a machine learning-based Bayesian filtering method for precise optical frequency comb noise characterization, demonstrating superior accuracy and robustness over traditional techniques through numerical and experimental validation.

Contribution

It presents a novel Bayesian filtering and expectation maximization approach for optical frequency comb noise analysis, improving accuracy and noise estimation over existing methods.

Findings

Outperforms conventional noise estimation methods.

Works effectively across a wide range of SNRs.

Validated through both numerical simulations and experiments.

Abstract

A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.