JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{henderson:icalepcs2023-tupdp116,
author = {M.J. Henderson and S. Calder and J.P. Edelen and R.D. Gregory and G.S. Guyotte and C.M. Hoffmann and M.C. Kilpatrick and B.K. Krishna and I.V. Pogorelov and B. Vacaliuc},
% author = {M.J. Henderson and S. Calder and J.P. Edelen and R.D. Gregory and G.S. Guyotte and C.M. Hoffmann and others},
% author = {M.J. Henderson and others},
title = {{Machine Learning Based Sample Alignment at TOPAZ}},
% booktitle = {Proc. ICALEPCS'23},
booktitle = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)},
eventdate = {2023-10-09/2023-10-13},
pages = {851--855},
paper = {TUPDP116},
language = {english},
keywords = {controls, alignment, network, neutron, operation},
venue = {Cape Town, South Africa},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {19},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {02},
year = {2024},
issn = {2226-0358},
isbn = {978-3-95450-238-7},
doi = {10.18429/JACoW-ICALEPCS2023-TUPDP116},
url = {https://jacow.org/icalepcs2023/papers/tupdp116.pdf},
abstract = {{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. }},
}