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High-sensitivity extreme-ultraviolet transient absorption spectroscopy enabled by machine learning.

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    This summary is machine-generated.

    We developed a new machine learning method to significantly reduce noise in spectroscopy, improving data quality tenfold for transient absorption measurements. This advanced technique enhances sensitivity for studying subtle material dynamics.

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    Area of Science:

    • Spectroscopy
    • Machine Learning
    • Materials Science

    Background:

    • Spectroscopy experiments often suffer from noise, limiting the sensitivity of measurements.
    • Conventional noise reduction methods, like pump-on/pump-off referencing, have limitations, especially for broadband light sources.

    Purpose of the Study:

    • To introduce a novel denoising scheme for spectroscopy using machine learning.
    • To demonstrate improved noise suppression in XUV transient absorption spectroscopy.

    Main Methods:

    • Measuring probe spectra before and after sample interaction.
    • Utilizing machine learning to capture correlations between spectral components.
    • Applying artificial neural networks for pixel-wise noise reduction.

    Main Results:

    • Achieved up to a tenfold improvement in noise suppression compared to conventional methods.
    • Demonstrated effective noise reduction without requiring wavelength calibration of reference spectra.
    • Successfully applied the scheme to XUV transient absorption measurements.

    Conclusions:

    • The novel denoising scheme offers significant noise reduction for spectroscopy.
    • The method is adaptable to various experiments, particularly beneficial for low repetition-rate sources.
    • Enhanced sensitivity enables detailed studies of electron and lattice dynamics in materials.