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    A novel spectral interferometry system uses a neural network to directly infer the electric field magnitude and phase from femtosecond interferograms. This AI-driven approach offers higher accuracy than traditional methods without needing prior shear frequency knowledge.

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    Area of Science:

    • Optics and Photonics
    • Biomedical Optics
    • Metrology

    Background:

    • Spectral interferometry is crucial for retrieving complex electric field information in various scientific and biomedical fields.
    • Accurate retrieval of spectral magnitude and phase from interferograms is essential for data interpretation.

    Purpose of the Study:

    • To develop and evaluate a neural network-based spectral interferometry system for direct magnitude and phase retrieval.
    • To compare the performance of the neural network approach against the conventional Hilbert transform method.

    Main Methods:

    • A single-shot spectral interferometry system employing a neural network was designed.
    • The neural network was trained using experimentally generated femtosecond interferograms with digitally created, causality-obeying phase and magnitude profiles.
    • Performance was benchmarked against the Hilbert transform method.

    Main Results:

    • The neural network approach directly inferred magnitude and phase from single-shot interference patterns.
    • The system achieved higher accuracy compared to the Hilbert transform under experimental conditions.
    • The method does not require prior knowledge of the shear frequency.

    Conclusions:

    • The developed neural network-based spectral interferometry system provides an accurate and efficient method for electric field retrieval.
    • The training technique accommodates experimental imperfections, simplifying instrument requirements and reducing costs.
    • This AI-driven approach advances spectral interferometry applications in metrology and biomedical fields.