Machine Learning Techniques for Optics Measurements and Corrections

Date: October 28, 2020 8:00 am
Speaker: Elena Fol (CERN)

Recently, the application of ML has grown in accelerator physics, in particular in the domain of diagnostics and control. One of the first applications of ML at the LHC is focused on optics measurements and corrections. Unsupervised Learning has been applied to automatic detection of beam position monitors faults to improve optics analysis, demonstrating successful results in operation. A novel ML-based approach for the estimation of magnet errors is developed, using supervised regression models trained on a large set of LHC optics simulations. Also, autoencoder neural networks have found their application in denoising of measurements data and reconstruction of missing data points. The results and future plans for these studies will be discussed following a brief introduction to relevant ML concepts.

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