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Synchrotron light sources, arguably among the most powerful tools of modern scientific discov-9 ery, are presently undergoing a major transformation to provide orders of magnitude higher brightness and transverse coherence enabling the most demanding experiments. In these experiments, overall source stability will soon be limited by achievable levels of electron beam size stability, presently on the order of several microns, which is still 1-2 orders of magnitude larger than already demonstrated stability of source position and current. Until now source size stabilization has been achieved through corrections based on a combination of static predetermined physics models and lengthy calibration measurements, periodically repeated to counteract drift in the accelerator and instrumentation. We now demonstrate for the first time how application of machine learning allows for a physics-and model-independent stabilization of source size relying only on previously existing instrumentation. Such feed-forward correction based on a neural network that can be continuously online-retrained achieves source size stability as low as 0.2 µm (0.4%) rms which results in overall source stability approaching the sub-percent noise floor of the most sensitive experiments.

Electron localization in different systems has been a topic of study for over a half of a century; polaron formation in the solid state and electron solvation by liquids are just two examples. Structureless by itself but interacting strongly with the condensed phase environment, an electron is often utilized as a probe of its local microscopic structure. Despite that, there are few attempts to access the shape of a localized electronic wave function directly from an experiment. Here, we report an elegant way of estimating the electron localization size parallel to a two-dimensional metal/adsorbate interface by using time and angle resolved two-photon photoemission (TAR TPPE).

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