Elena Fol (CERN) Machine Learning application in the Large Hadron Collider (LHC)
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US/Pacific
ZOOM LINK: https://lbnl.zoom.us/j/97341258188
Description
Machine Learning (ML) as a research field incorporates a broad variety of techniques ranging from traditional statistical methods to deep neural networks capable to surpass human performance in control and optimization tasks. Particle accelerator optimization problems deal with non-linear, multi-objective functions which depend on thousands of time-varying machine components and settings. These properties often meet the limitations of traditional optimization methods and make this problem a perfect candidate for application of ML-based techniques. In this talk I will present, how ML can improve the control of the beams in the LHC and give a short outlook on the ML application to accelerator design. Main focus of the presentation will be the application of decision tree - based methods to instrumentation faults detection, reconstruction and correction of magnet errors, and supervised learning for virtual diagnostics, which enables to obtain accurate information of beam properties without time-costly measurements.
