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Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory
Date: October 23, 2020 1:00 pm
Speaker: Chris Tennant (Jefferson Laboratory)
Many scientific and engineering challenges can be formulated as fitting a model to existing data. Whether it is comparing a scientific simulation to known experimental observations, finding a continuous representation of sparse/discrete data points, or the values of model parameters which generalize to unforeseen data examples given historical data; all these tasks share a common underlying principle of model fitting, but with different choices made in the model formulation (parametric or analytical) and the assumptions made about the data (acquisition scheme, noise to signal ratio, continuity, or information locality). In this talk I will highlight a few use cases under this framework. Specifically, I will address research conducted at Argonne National Laboratory for X-ray image reconstruction problems, data reduction for scientific simulations, and deep learning approaches for replacing expensive iterative optimization. Additionally, I will present more recent work for sports computer vision applications that enable real time player detection, tracking, and activity prediction from broadcast videoWe report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the 8 cavities in the cryomodule. Subject matter experts (SME) are able to analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type has been implemented. We discuss the development and performance of the ML models as well as valuable lessons learned in bringing a ML system to deployment.
Multi-Objective Bayesian Optimization for Accelerator Tuning
Date: October 30, 2020 1:00 pm
Speaker: Ryan Roussell (University of Chicago)
Particle accelerators require constant tuning during operation to meet beam quality, total charge and particle energy requirements for use in a wide variety of physics, chemistry and biology experiments. Maximizing the performance of an accelerator facility often necessitates multi-objective optimization, where operators must balance trade-offs between multiple objectives simultaneously, often using limited, temporally expensive beam observations. Usually, accelerator optimization problems are solved offline, prior to actual operation, with advanced beamline simulations and parallelized optimization methods (NSGA-II, Swarm Optimization). Unfortunately, it is not feasible to use these methods for online multi-objective optimization, since beam measurements can only be done in a serial fashion, and these optimization methods require a large number of measurements to converge to a useful solution. Here, we introduce a multi-objective Bayesian optimization scheme, which finds the full Pareto front of an accelerator optimization problem efficiently in a serialized manner and is thus a critical step towards practical online multi-objective optimization in accelerators. This method uses a set of Gaussian process surrogate models, along with a multi-objective acquisition function, which reduces the number of observations needed to converge by at least an order of magnitude over current methods. We demonstrate how this method can be modified to specifically solve optimization challenges posed by the tuning of accelerators. This includes the addition of optimization constraints, objective preferences and costs related to changing accelerator parameters
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