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    Neural networks can now analyze artefacts in quantum-mimic optical coherence tomography to determine material dispersion. This method leverages artefact characteristics to reveal the GVD profile of dispersive layers.

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    Area of Science:

    • Optics
    • Quantum Optics
    • Machine Learning

    Background:

    • Artefacts in quantum-mimic optical coherence tomography (OCT) typically degrade image quality.
    • These artefacts arise from the autocorrelation function inherent in the quantum entanglement mimicking algorithm.
    • The shape and characteristics of these artefacts are linked to the dispersion properties of the sample.

    Purpose of the Study:

    • To develop a method for utilizing OCT artefacts to quantify material dispersion.
    • To train a neural network to recognize the relationship between artefact morphology and GVD.
    • To assess the network's ability to predict dispersion profiles from artefact characteristics.

    Main Methods:

    • A neural network was trained to correlate artefact shape with GVD.
    • Simulations and experimental tests were performed using computer-generated dispersive layers and physical samples (glass).
    • The influence of autocorrelation peaks on GVD measurements was investigated.

    Main Results:

    • The neural network successfully learned the relationship between artefact features and GVD.
    • The method provided qualitative dispersion profiles for novel data, including generated layers and glass samples.
    • The study identified and explained the mechanism by which autocorrelation peaks alter GVD profiles.

    Conclusions:

    • Artefacts in quantum-mimic OCT can be repurposed as a source of information about material dispersion.
    • A neural network approach offers a viable method for GVD profiling using these artefacts.
    • The network demonstrates robustness when tested with noisy data and various materials like quartz, sapphire, and BK7.