ESRF, Grenoble, France
DESY, Hamburg, Germany
SLAC, Menlo Park, California, USA
LBNL, Berkeley, California, USA
Badger* is a software designed to easily access several optimizers (simplex, RCDS**, bayesian optimization, etc.) to solve a given multidimensional minimization/maximization task. The Badger software is very flexible and easy to adapt to different facilities. In the framework of the EURIZON European project Badger was used for the EBS and PETRAIII storage rings interfacing with the Tango and TINE control system. Among other tests, the optimisations of Touschek lifetime was performed and compared with the results obtained with existing tools during machine dedicated times.
* Z. Zhang et al., "Badger: The Missing Optimizer in ACR", doi:10.18429/JACoW-IPAC2022-TUPOST058
** X. Huang, "Robust simplex algorithm for online optimization", 10.1103/PhysRevAccelBeams.21.104601
SLAC, Menlo Park, California, USA
We describe four cyber security related activities of SLAC and collaborations. First, from a broad review of accelerator computing cyber and mission reliability, our analysis method, findings and outcomes. Second, lab-wide and accelerator penetration testing, in particular methods to control, coordinate, and trap, potentially hazardous scans. Third, a summary gap analysis of recent US regulatory orders from common practice at accelerators, and our plans to address these in collaboration with the US Dept. of Energy. Finally, summary attack vectors of EPICS, and technical plans to add authentication and encryption to EPICS itself.
RadiaSoft LLC, Boulder, Colorado, USA
SLAC, Menlo Park, California, USA
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.