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TY - CONF AU - Edelen, J.P. AU - Bruhwiler, K. AU - Calder, S. AU - Diaw, A. AU - Hall, C.C. AU - Hoffmann, C.M. ED - Furukawa, Kazuro ED - Yan, Yingbing ED - Leng, Yongbin ED - Chen, Zhichu ED - Schaa, Volker R.W. TI - Sample Alignment in Neutron Scattering Experiments Using Deep Neural Network J2 - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021 CY - Shanghai, China T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 18 LA - english AB - Access to neutron scattering centers, such as Oak Ridge National Laboratory (ORNL) and the NIST Center for Neutron Research, has provided beam energies to investigating a wide variety of applications such as particle physics, material science, and biology. In these experiments, the quality of collected data is very sensitive to sample and beam alignment, and stabilization of the experimental environment, requiring human intervention to tune the beam. While this procedure works, it is inefficient and time-consuming. In the work we present progress towards using machine learning to automate the alignment of a beamline in neutron scattering experiments. Our algorithm uses convolutional neural network to both learn a surrogate of the image data of the sample and to predict the sample contour using a u-net. We tested our algorithm on neutron camera images from the H2-BA powder diffractometer and the Topaz single crystal diffractometer beamlines of ORNL. PB - JACoW Publishing CP - Geneva, Switzerland SP - 686 EP - 690 KW - neutron KW - network KW - experiment KW - alignment KW - scattering DA - 2022/03 PY - 2022 SN - 2226-0358 SN - 978-3-95450-221-9 DO - doi:10.18429/JACoW-ICALEPCS2021-WEPV022 UR - https://jacow.org/icalepcs2021/papers/wepv022.pdf ER -