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

We present a novel denoising scheme for spectroscopy experiments employing broadband light sources and demonstrate its capabilities using transient absorption measurements with a high-harmonic source. Our scheme relies on measuring the probe spectra before and after interacting with the sample while capturing correlations between spectral components through machine learning approaches. With the present setup we achieve up to a tenfold improvement in noise suppression in XUV transient absorption spectra compared to the conventional pump on/ pump off referencing method.