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Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators
Feb. 20, 2024, 5:45 a.m. | Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta
cs.LG updates on arXiv.org arxiv.org
Abstract: Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, particle accelerators can fault and abort operations for numerous reasons. These faults impact the availability of particle accelerators during scheduled run-time and hamper the efficiency and the overall science output. To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability of particle …
abstract accelerators arxiv availability components cs.lg efficiency equipment impact modeling operations peak power robust running type
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