Optimizing design and operation of synchrotron beamline using machine learning

The next generation of synchrotron light sources is set to produce fully coherent x-rays, revolutionizing research in physics, chemistry, and biology. To maximize the potential of these advances, novel technologies like x-ray adaptive optics will enable users to customize the light to reveal new contrasts and achieve higher sensitivity. This research focuses on enhancing beamline performance through the integration of advanced modeling, simulation, and machine learning (ML) techniques. By combining ray tracing models with ML, we analyze simulated data to gain insights into data distribution and guide the optimization process. Specifically, ML is employed to map the influence functions of an x-ray deformable mirror on the light passing through complex optical systems, including gratings and slits. Generative Adversarial Networks (GANs) are leveraged to correlate the input voltages of the adaptive optics with resultant beam positions, providing a sophisticated approach to beam control and alignment. The integration of GANs allows for the exploration of complex, non-linear relationships between control parameters and beam behavior, enabling more precise and adaptive beam positioning that could be used to probe fine effects such as x-ray circular dichroism with increased speed and accuracy.