Speaker
Description
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 and the Powder Diffractometer at the High Flux Isotope Reactor study these samples by illuminating them with different energy neutron beams and recording the scattered neutrons. Aligning and maintaining the alignment of the sample during an experiment 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 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 poster we provide an overview of our machine learning tools and show our results aligning samples at ORNL.
Funding Agency
This work is funded by the Department of Energy Office of Science, Office of Basic Energy Science award number DE-SC0021555
| Region represented | America |
|---|---|
| Paper preparation format | LaTeX |
