SBIR/STTR Award attributes
X-ray beamlines comprise crucial components of the four synchrotron light sources (ALS, NSLS-II, APS, and SSRL) operated by the US Department of Energy. These high precision instruments require careful alignment and calibration, which is a slow, error prone process that wastes precious time and beam availability resources. The goal of this project is to create rapid beamline modeling tools that may be combined with the diagnostics data in real time to construct an “online model” that will improve the beamline operator’s knowledge of the photon beam and enable automated alignment and reconfiguration. The recently developing Bluesky software used to run beamline experiments will be combined with rapidly executing beamline models and diagnostics data to build the continually updated online model. During Phase I of this project, we constructed a variety of rapid x-ray transport models using both first principles and machine learning. The physics models adapted transfer matrix methods in conjunction with Wigner function methods to represent partially coherent radiation, an important property of the x-ray beams in these light source facilities. We also integrated these tools with our existing web-based Graphical User Interfaces for SHADOW and SRW. In Phase II, we will complete the construction of the rapid x-ray transport and machine learning models and apply this to operating beamlines at a synchrotron light source. In collaboration with our subcontract, we will expand the Bluesky software that already operates on these beamlines to include the rapid optics models and our machine learning methods. This technology will dramatically increase the efficiency of x-ray beamlines, reducing the time needed to set up new experiments, and to optimize beamline performance. It will also provide improved metadata associated with synchrotron experiments to create more repeatability, and improve the data analysis potential over a wide array of photon science techniques.
