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BiBTeX citation export for TUPDP113: A Flexible EPICS Framework for Sample Alignment at Neutron Beamlines

@inproceedings{edelen:icalepcs2023-tupdp113,
  author       = {J.P. Edelen and S. Calder and R.D. Gregory and G.S. Guyotte and M.J. Henderson and C.M. Hoffmann and M.C. Kilpatrick and B.K. Krishna and B. Vacaliuc},
% author       = {J.P. Edelen and S. Calder and R.D. Gregory and G.S. Guyotte and M.J. Henderson and C.M. Hoffmann and others},
% author       = {J.P. Edelen and others},
  title        = {{A Flexible EPICS Framework for Sample Alignment at Neutron Beamlines}},
% 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        = {836--840},
  paper        = {TUPDP113},
  language     = {english},
  keywords     = {controls, EPICS, framework, 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-TUPDP113},
  url          = {https://jacow.org/icalepcs2023/papers/tupdp113.pdf},
  abstract     = {{RadiaSoft has been developing a flexible front-end framework, written in Python, for rapidly developing and testing automated sample alignment IOCs at Oak Ridge National Laboratory. We utilize YAML-formatted configuration files to construct a thin abstraction layer of custom classes which provide an internal representation of the external hardware within a controls system. The abstraction layer takes advantage of the PCASPy and PyEpics libraries in order to serve EPICS process variables & respond to read/write requests. Our framework allows users to build a new IOC that has access to information about the sample environment in addition to user-defined machine learning models. The IOC then monitors for user inputs, performs user-defined operations on the beamline, and reports on its status back to the control system. Our IOCs can be booted from the command line, and we have developed command line tools for rapidly running and testing alignment processes. These tools can also be accessed through an EPICS GUI or in separate Python scripts. This presentation provides an overview of our software structure and showcases its use at two beamlines at ORNL. }},
}