The DeepFMKit Python package: A toolbox for simulating and analyzing deep frequency modulation interferometers

TL;DR

DeepFMKit is an open-source Python toolbox that simulates and analyzes deep frequency modulation interferometers, aiding in system design, optimization, and systematic error investigation for high-precision laser interferometry.

Contribution

It introduces a comprehensive, high-fidelity simulation framework with advanced parameter estimation algorithms and automation tools for deep frequency modulation interferometry research.

Findings

High-accuracy modeling of physical effects in DFMI systems

Efficient parameter estimation with parallelized algorithms

Automated large-scale analysis for system performance characterization

Abstract

Deep Frequency Modulation Interferometry (DFMI) is an emerging laser interferometry technique for high-precision metrology, offering picometer-level displacement measurements and the potential for absolute length determination with sub-wavelength accuracy. However, the design and optimization of DFMI systems involve a complex interplay between interferometer physics, laser technology, multiple noise sources, and the choice of data processing algorithm. To address this, we present DeepFMKit, a new open-source Python library for the end-to-end simulation and analysis of DFMI systems. The framework features a high-fidelity physics engine that rigorously models key physical effects such as time-of-flight delays in dynamic interferometers, arbitrary laser modulation waveforms, and colored noise from user-defined $1/f^\alpha$ spectral densities. This engine is coupled with a suite of interchangeable parameter estimation algorithms, including a highly-optimized, parallelized frequency-domain Non-linear Least Squares (NLS) for high-throughput offline analysis, and multiple time-domain Extended Kalman Filter (EKF) implementations for real-time state tracking, featuring both random walk and integrated random walk (constant velocity) process models. Furthermore, DeepFMKit includes a high-throughput experimentation framework for automating large-scale parameter sweeps and Monte Carlo analyses, enabling systematic characterization of system performance. DeepFMKit's modular, object-oriented architecture facilitates the rapid configuration of virtual experiments, providing a powerful computational tool for researchers to prototype designs, investigate systematic errors, and accelerate the development of precision interferometry.