Online Bayesian Optimization for Beam Alignment in the SECAR Recoil Mass Separator

A., Miskovich, Sara; Montes, F.; Berg, G. P. A.; Blackmon, J. C.; Chipps, K. A.; Couder, M.; Hermansen, Kirby; Hood, Ashley; Jain, Rahul; Schatz, H.; Smith, Michael S.; Tsintari, Pelagia; Wagner, Louis K.

doi:10.48550/arxiv.2112.01557

Cited by 1 publication

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“…By utilizing online data from existing fast diagnostics such as beam position monitors, beam loss monitors, and radio-frequency cavity signals, an effort has been made to develop ML-based controllers. Recent demonstrations include BO, RL, and GPs for accelerator tuning (McIntire et al, 2016;Hao et al, 2019;Li, Rainer, and Cheng, 2019;Duris et al, 2020;Shalloo et al, 2020;Miskovich et al, 2021;Roussel and Edelen, 2021;Wang, Bagri et al, 2021) and polynomial chaos expansion-based surrogate models for uncertainty quantification (Adelmann, 2019). Particle swarm techniques have been evaluated to optimize the tuning of aperiodic ion transport lines and for advanced particle separators (Amthor et al, 2018).…”

Section: Control Optimizationmentioning

confidence: 99%

Colloquium: Machine learning in nuclear physics

et al. 2022

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TABLE I. Table of ML methods discussed in this Colloquium with an indication of the main type of learning (S, supervised; U, unsupervised; semi-S, semisupervised). Acronym Method Description Learning type AE, VAE Autoencoders, Variational autoencoders ANN capable of learning efficient representations of the input data without any supervision U ANN Artificial neural network Models for learning defined by connected units (or nodes) and hidden layers with well-defined inputs and outputs S BED Bayesian experimental design Bayesian inference for experimental design S BM Boltzmann machine Generative ANN that can learn a probability distribution from sets of changing inputs U BMA, BMM Bayesian model averaging, Bayesian model mixing Bayesian inference applied to model selection or the combined estimation, or performed over a mixture model S BNN Bayesian neural network ANN where the parameters of the network are represented by probabilities learned by Bayesian inference S BO Bayesian optimization Optimization of functions without an a priori knowledge of functional forms. S and semi-S CNN Convolutional neural network ANN where convolution is used to reduce dimensionalities S EMB Ensemble methods and boosting Methods based on collections of decision trees as simple learners S GAN Generative adversarial network System of two ANNs where a generative network generates outputs while a discriminative network evaluates them U GP Gaussian process Collection of random variables that have a joint Gaussian distribution used in Bayesian inference Semi-S KNN k-nearest neighbors Nonparametric method where inputs consist of the k closest training examples in a dataset S KR Kernel regression Extension of linear regression methods to include nonlinear function kernels S LR Logistic regression Convex optimization method based on maximum likelihood estimate for classification problems S LSTM Long short-term memory RNN capable of learning long-term dependencies S PCA Principal component analysis Dimensionality reduction technique based on retaining the largest eigenvalues of the covariance matrix U REG Linear regression Linear algebra methods used for modeling continuous functions in terms of their explanatory variables S RL Reinforcement learning Learning achieved by trial and error of desired and undesired events Neither S nor U RNN Recurrent neural network ANN where connections between nodes allow for temporal dynamic behavior S SVM Support vector machine Convex optimization techniques with efficient ways to distinguish features in datasets S

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