TY - CONF AU - Einstein-Curtis, J.A. AU - Abell, D.T. AU - Du, Y. AU - Giles, A. AU - Keilman, M.V. AU - Lynch, J. AU - Moeller, P. AU - Morris, T. AU - Nash, B. AU - Pogorelov, I.V. AU - Rakitin, M. AU - Walter, A.L. ED - Schaa, Volker RW ED - Götz, Andy ED - Venter, Johan ED - White, Karen ED - Robichon, Marie ED - Rowland, Vivienne TI - Online Models for X-ray Beamlines Using Sirepo-Bluesky J2 - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023 CY - Cape Town, South Africa T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 19 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 165 EP - 170 KW - synchrotron KW - optics KW - radiation KW - electron KW - controls DA - 2024/02 PY - 2024 SN - 2226-0358 SN - 978-3-95450-238-7 DO - doi:10.18429/JACoW-ICALEPCS2023-MO3BCO05 UR - https://jacow.org/icalepcs2023/papers/mo3bco05.pdf ER - TY - CONF AU - Einstein-Curtis, J.A. AU - Barber, S.K. AU - Berger, C.E. AU - Coleman, S.J. AU - Cook, N.M. AU - Edelen, J.P. AU - van Tilborg, J. ED - Schaa, Volker RW ED - Götz, Andy ED - Venter, Johan ED - White, Karen ED - Robichon, Marie ED - Rowland, Vivienne TI - Laser Focal Position Correction Using FPGA-Based ML Models J2 - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023 CY - Cape Town, South Africa T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 19 LA - english AB - High repetition-rate, ultrafast laser systems play a critical role in a host of modern scientific and industrial applications. We present a diagnostic and correction scheme for controlling and determining laser focal position by utilizing fast wavefront sensor measurements from multiple positions to train a focal position predictor. This predictor and additional control algorithms have been integrated into a unified control interface and FPGA-based controller on beamlines at the Bella facility at LBNL. An optics section is adjusted online to provide the desired correction to the focal position on millisecond timescales by determining corrections for an actuator in a telescope section along the beamline. Our initial proof-of-principle demonstrations leveraged pre-compiled data and pre-trained networks operating ex-situ from the laser system. A framework for generating a low-level hardware description of ML-based correction algorithms on FPGA hardware was coupled directly to the beamline using the AMD Xilinx Vitis AI toolchain in conjunction with deployment scripts. Lastly, we consider the use of remote computing resources, such as the Sirepo scientific framework, to actively update these correction schemes and deploy models to a production environment. PB - JACoW Publishing CP - Geneva, Switzerland SP - 262 EP - 266 KW - controls KW - laser KW - network KW - FPGA KW - simulation DA - 2024/02 PY - 2024 SN - 2226-0358 SN - 978-3-95450-238-7 DO - doi:10.18429/JACoW-ICALEPCS2023-TU1BCO04 UR - https://jacow.org/icalepcs2023/papers/tu1bco04.pdf ER - TY - CONF AU - Edelen, J.P. AU - Calder, S. AU - Gregory, R.D. AU - Guyotte, G.S. AU - Henderson, M.J. AU - Hoffmann, C.M. AU - Kilpatrick, M.C. AU - Krishna, B.K. AU - Vacaliuc, B. ED - Schaa, Volker RW ED - Götz, Andy ED - Venter, Johan ED - White, Karen ED - Robichon, Marie ED - Rowland, Vivienne TI - A Flexible EPICS Framework for Sample Alignment at Neutron Beamlines J2 - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023 CY - Cape Town, South Africa T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 19 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 836 EP - 840 KW - controls KW - EPICS KW - framework KW - neutron KW - operation DA - 2024/02 PY - 2024 SN - 2226-0358 SN - 978-3-95450-238-7 DO - doi:10.18429/JACoW-ICALEPCS2023-TUPDP113 UR - https://jacow.org/icalepcs2023/papers/tupdp113.pdf ER - TY - CONF AU - Pogorelov, I.V. AU - Calder, S. AU - Edelen, J.P. AU - Gregory, R.D. AU - Guyotte, G.S. AU - Henderson, M.J. AU - Hoffmann, C.M. AU - Kilpatrick, M.C. AU - Krishna, B.K. AU - Vacaliuc, B. ED - Schaa, Volker RW ED - Götz, Andy ED - Venter, Johan ED - White, Karen ED - Robichon, Marie ED - Rowland, Vivienne TI - Machine Learning Based Noise Reduction of Neutron Camera Images at ORNL J2 - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023 CY - Cape Town, South Africa T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 19 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 841 EP - 845 KW - neutron KW - network KW - timing KW - target KW - operation DA - 2024/02 PY - 2024 SN - 2226-0358 SN - 978-3-95450-238-7 DO - doi:10.18429/JACoW-ICALEPCS2023-TUPDP114 UR - https://jacow.org/icalepcs2023/papers/tupdp114.pdf ER - TY - CONF AU - Edelen, J.P. AU - Diaz Cruz, J.A. AU - Edelen, A.L. AU - Einstein-Curtis, J.A. AU - Henderson, M.J. AU - Kilpatrick, M.C. ED - Schaa, Volker RW ED - Götz, Andy ED - Venter, Johan ED - White, Karen ED - Robichon, Marie ED - Rowland, Vivienne TI - Machine Learning for Compact Industrial Accelerators J2 - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023 CY - Cape Town, South Africa T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 19 LA - english AB - The industrial and medical accelerator industry is an ever-growing field with advancements in accelerator technology enabling its adoption for new applications. As the complexity of industrial accelerators grows so does the need for more sophisticated control systems to regulate their operation. Moreover, the environment for industrial and medical accelerators is often harsh and noisy as opposed to the more controlled environment of a laboratory-based machine. This environment makes control more challenging. Additionally, instrumentation for industrial accelerators is limited making it difficult at times to identify and diagnose problems when they occur. RadiaSoft has partnered with SLAC to develop new machine learning methods for control and anomaly detection for industrial accelerators. Our approach is to develop our methods using simulation models followed by testing on experimental systems. Here we present initial results using simulations of a room temperature s-band system. PB - JACoW Publishing CP - Geneva, Switzerland SP - 846 EP - 850 KW - cavity KW - controls KW - simulation KW - industrial-accelerators KW - network DA - 2024/02 PY - 2024 SN - 2226-0358 SN - 978-3-95450-238-7 DO - doi:10.18429/JACoW-ICALEPCS2023-TUPDP115 UR - https://jacow.org/icalepcs2023/papers/tupdp115.pdf ER - TY - CONF AU - Henderson, M.J. AU - Calder, S. AU - Edelen, J.P. AU - Gregory, R.D. AU - Guyotte, G.S. AU - Hoffmann, C.M. AU - Kilpatrick, M.C. AU - Krishna, B.K. AU - Pogorelov, I.V. AU - Vacaliuc, B. ED - Schaa, Volker RW ED - Götz, Andy ED - Venter, Johan ED - White, Karen ED - Robichon, Marie ED - Rowland, Vivienne TI - Machine Learning Based Sample Alignment at TOPAZ J2 - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023 CY - Cape Town, South Africa T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 19 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 851 EP - 855 KW - controls KW - alignment KW - network KW - neutron KW - operation DA - 2024/02 PY - 2024 SN - 2226-0358 SN - 978-3-95450-238-7 DO - doi:10.18429/JACoW-ICALEPCS2023-TUPDP116 UR - https://jacow.org/icalepcs2023/papers/tupdp116.pdf ER - TY - CONF AU - Einstein-Curtis, J.A. AU - Drees, K.A. AU - Edelen, J.P. AU - Kilpatrick, M.C. AU - Laster, J.S. AU - O’Rourke, R. AU - Valette, M. ED - Schaa, Volker RW ED - Götz, Andy ED - Venter, Johan ED - White, Karen ED - Robichon, Marie ED - Rowland, Vivienne TI - Classification and Prediction of Superconducting Magnet Quenches J2 - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023 CY - Cape Town, South Africa T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 19 LA - english AB - Robust and reliable quench detection for superconducting magnets is increasingly important as facilities push the boundaries of intensity and operational runtime. RadiaSoft has been working with Brookhaven National Lab on quench detection and prediction for superconducting magnets installed in the RHIC storage rings. This project has analyzed several years of power supply and beam position monitor data to train automated classification tools and automated quench precursor determination based on input sequences. Classification was performed using supervised multilayer perceptron and boosted decision tree architectures, while models of the expected operation of the ring were developed using a variety of autoencoder architectures. We have continued efforts to maximize area under the receiver operating characteristic curve for the multiple classification problem of real quench, fake quench, and no-quench events. We have also begun work on long short-term memory (LSTM) and other recurrent architectures for quench prediction. Examinations of future work utilizing more robust architectures, such as variational autoencoders and Siamese models, as well as methods necessary for uncertainty quantification will be discussed. PB - JACoW Publishing CP - Geneva, Switzerland SP - 856 EP - 859 KW - power-supply KW - superconducting-magnet KW - GUI KW - operation KW - experiment DA - 2024/02 PY - 2024 SN - 2226-0358 SN - 978-3-95450-238-7 DO - doi:10.18429/JACoW-ICALEPCS2023-TUPDP117 UR - https://jacow.org/icalepcs2023/papers/tupdp117.pdf ER - TY - CONF AU - Cook, N.M. AU - Barbour, A.M. AU - Carlin, E.G. AU - Einstein-Curtis, J.A. AU - Nagler, R. AU - O’Rourke, R. AU - Rakitin, M. AU - Wiegart, L. AU - Wijesinghe, H. ED - Schaa, Volker RW ED - Götz, Andy ED - Venter, Johan ED - White, Karen ED - Robichon, Marie ED - Rowland, Vivienne TI - Integrating Online Analysis with Experiments to Improve X-Ray Light Source Operations J2 - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023 CY - Cape Town, South Africa T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 19 LA - english AB - The design, execution, and analysis of light source experiments requires the use of sophisticated simulation, controls and data management tools. Existing workflows require significant specialization to accommodate specific beamline operations and data pre-processing steps necessary for more intensive analysis. Recent efforts to address these needs at the National Synchrotron Light Source II (NSLS-II) have resulted in the creation of the Bluesky data collection framework, an open-source library for coordinating experimental control and data collection. Bluesky provides high level abstraction of experimental procedures and instrument readouts to encapsulate generic workflows. We present a prototype data analysis platform for integrating data collection with real time analysis at the beamline. Our application leverages Bluesky in combination with a flexible run engine to execute user configurable Python-based analyses with customizable queueing and resource management. We discuss initial demonstrations to support X-ray photon correlation spectroscopy experiments and future efforts to expand the platform’s features. PB - JACoW Publishing CP - Geneva, Switzerland SP - 921 EP - 924 KW - experiment KW - interface KW - real-time KW - simulation KW - framework DA - 2024/02 PY - 2024 SN - 2226-0358 SN - 978-3-95450-238-7 DO - doi:10.18429/JACoW-ICALEPCS2023-TUSDSC02 UR - https://jacow.org/icalepcs2023/papers/tusdsc02.pdf ER -