femto-PIXAR: a self-supervised neural network method for reconstructing femtosecond X-ray free electron laser pulses

X-ray free electron lasers (X-FELs) produce ultrafast pulses in a wide range of lasing configurations, supporting a wide variety of scientific applications, including structural biology, materials science, and atomic and molecular physics. Shot-by-shot characterization of the X-FEL pulses is crucial for the analysis of experiments as well as for tuning the X-FEL performance. However, for the weak pulses found in advanced configurations, e.g., those needed for monochromatic, two-pulse studies of quantum materials, there is no current method for reliably resolving pulse profiles. Here, we show that an interpretable neural network (NN) model can reconstruct the individual pulse power profiles for sub-picosecond pulse separation without the need for simulations. Using experimental data from low-signal X-FEL pulse pairs, we demonstrate a NN can learn the pulse characteristics on a shot-by-shot basis when conventional methods fail. This new method enables the characterization of weak pulses-a condition expected to dominate future experimental configurations such as at the Linac Coherent Light Source-II-and opens the door to a wide range of new experiments.