RadiaSoft LLC, Boulder, Colorado, USA
BNL, Upton, New York, USA
Synchrotron radiation beamlines transport X-rays from the electron beam source to the experimental sample. Precise alignment of the beamline optics is required to achieve adequate beam properties at the sample. This process is often done manually and can be quite time consuming. Further, we would like to know the properties at the sample in order to provide metadata for X-ray experiments. Diagnostics may provide some of this information but important properties may remain unmeasured. In order to solve both of these problems, we are developing tools to create fast online models (also known as digital twins). For this purpose, we are creating reduced models that fit into a hierarchy of X-ray models of varying degrees of complexity and runtime. These are implemented within a software framework called Sirepo-Bluesky* that allows for the computation of the model from within a Bluesky session which may control a real beamline. This work is done in collaboration with NSLS-II. We present the status of the software development and beamline measurements including results from the TES beamline. Finally, we present an outlook for continuing this work and applying it to more beamlines at NSLS-II and other synchrotron facilities around the world.
*https://github.com/NSLS-II/sirepo-bluesky
RadiaSoft LLC, Boulder, Colorado, USA
ORNL RAD, Oak Ridge, Tennessee, USA
ORNL, Oak Ridge, Tennessee, USA
Neutron cameras are utilized at the HB2A powder diffractometer to image the sample for alignment in the beam. Typically, neutron cameras are quite noisy as they are constantly being irradiated. Removal of this noise is challenging due to the irregular nature of the pixel intensity fluctuations and the tendency for it to change over time. RadiaSoft has developed a novel noise reduction method for neutron cameras that inscribes a lower envelope of the image signal. This process is then sped up using machine learning. Here we report on the results of our noise reduction method and describe our machine learning approach for speeding up the algorithm for use during operations.
RadiaSoft LLC, Boulder, Colorado, USA
ORNL RAD, Oak Ridge, Tennessee, USA
ORNL, Oak Ridge, Tennessee, USA
Neutron scattering experiments are a critical tool for the exploration of molecular structure in compounds. The TOPAZ single crystal diffractometer at the Spallation Neutron Source studies these samples by illuminating samples with different energy neutron beams and recording the scattered neutrons. During the experiments the user will change temperature and sample position in order to illuminate different crystal faces and to study the sample in different environments. Maintaining alignment of the sample during this process is key to ensuring high quality data are collected. At present this process is performed manually by beamline scientists. RadiaSoft in collaboration with the beamline scientists and engineers at ORNL has developed a new machine learning based alignment software automating this process. We utilize a fully-connected convolutional neural network configured in a U-net architecture to identify the sample center of mass. We then move the sample using a custom python-based EPICS IOC interfaced with the motors. In this talk we provide an overview of our machine learning tools and show our initial results aligning samples at ORNL.