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    An extreme learning machine neural network models complex cavity maladjustments in cavity ringdown spectroscopy. This approach improves measurement accuracy for high reflectivity determination.

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    Area of Science:

    • Spectroscopy
    • Optical Engineering
    • Machine Learning

    Background:

    • Cavity ringdown spectroscopy (CRDS) is a sensitive technique for measuring optical properties.
    • Cavity maladjustment introduces complex, nonlinear effects that reduce measurement accuracy.
    • Existing methods struggle to precisely model these nonlinear effects.

    Purpose of the Study:

    • To develop a novel method for modeling the nonlinear relationship between intracavity loss and cavity maladjustments in CRDS.
    • To improve the accuracy of high reflectivity measurements using CRDS.
    • To reduce measurement uncertainty caused by cavity misalignment.

    Main Methods:

    • Utilized an extreme learning machine (ELM), a type of neural network, to model the nonlinear system.
    • Employed two-dimensional angular scanning simulations and experimental data for training and validation.
    • Applied the trained ELM model to a CRDS system for reflectivity measurements.

    Main Results:

    • The ELM model accurately predicted intracavity loss with root mean square deviations of approximately 0.27 ppm (simulation) and 0.44 ppm (experiment).
    • The method successfully reduced measurement uncertainty in high reflectivity determination.
    • Measurement uncertainty improved from ±0.0025% to ±0.0019%.

    Conclusions:

    • Extreme learning machine offers a robust solution for modeling complex nonlinearities in CRDS.
    • This data-driven approach significantly enhances the precision of CRDS measurements.
    • The improved accuracy has direct implications for applications requiring precise optical property determination, such as high reflectivity measurements.