Bayesian filtering framework for noise characterization of frequency combs.

Area of Science:

• Optics and Photonics
• Quantum Optics
• Metrology

Background:

• Amplitude and phase noise correlation matrices are vital for understanding frequency comb noise properties.
• Accurate estimation is crucial for applications like optical communication and precision measurements.
• Distinguishing noise from the comb versus the measurement system is challenging.

Purpose of the Study:

• To propose and demonstrate a novel Bayesian filtering framework for joint estimation of amplitude and phase noise in frequency combs.
• To improve the accuracy of noise correlation matrix measurements.

Main Methods:

• Developed a Bayesian filtering based framework for joint estimation of amplitude and phase noise.
• Applied the framework to characterize phase noise in multiple frequency comb lines.
• Utilized Bayesian filtering for optimal measurement noise filtering.

Main Results:

• The proposed approach yields significantly more accurate correlation matrix measurements compared to conventional methods.
• The framework operates effectively across a wide range of signal-to-noise ratios.
• Provides insights into frequency comb dynamics at short timescales (<10^-8 s).

Conclusions:

• The novel Bayesian filtering framework offers a theoretically optimum and highly accurate method for characterizing frequency comb noise.
• This advancement is essential for pushing the boundaries of low-noise performance in frequency comb applications.