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X-ray free-electron lasers are sources of coherent, high-intensity X-rayswith numerous applications in ultra-fast measurements and dynamic structuralimaging. Due to the stochastic nature of the self-amplified spontaneousemission process and the difficulty in controlling injection of electrons,output pulses exhibit significant noise and limited temporal coherence.Standard measurement techniques used for characterizing two-coloured X-raypulses are challenging, as they are either invasive or diagnosticallyexpensive. In this work, we employ machine learning methods such as neuralnetworks and decision trees to predict the central photon energies of pairs ofattosecond fundamental and second harmonic pulses using parameters that areeasily recorded at the high-repetition rate of a single shot. Using realexperimental data, we apply a detailed feature analysis on the input parameterswhile optimizing the training time of the machine learning methods. Ourpredictive models are able to make predictions of central photon energy for oneof the pulses without measuring the other pulse, thereby leveraging the use ofthe spectrometer without having to extend its detection window. We anticipateapplications in X-ray spectroscopy using XFELs, such as in time-resolved X-rayabsorption and photoemission spectroscopy, where improved measurement of inputspectra will lead to better experimental outcomes.

Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning