Machine Learning Applications for Particle Accelerators

Material Package

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Sessions*

ML Tutorial

Ocelot Tutorial

Prognosics

Data Analysis

Tuning

Simulations and Modeling

Facility needs

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Contributions*

Beam loss plane recognition for the LHC (in session "Prognosics")

Data mining at HIPA (in session "Data Analysis")

Detection of bad BPMs (in session "Prognosics")

Discussion (in session "Prognosics")

Discussion (in session "Simulations and Modeling")

Discussion (in session "Facility needs")

Discussion: Tuning (in session "Tuning")

Experience at SLAC with Bayesian Optimization using Gaussian Processes (in session "Tuning")

Experience with FEL taper tuning using reinforcement learning and clustering (in session "Tuning")

Experience with Model Predictive Control and Model-based Reinforcement Learning using Neural Networks (in session "Simulations and Modeling")

Experience with tuning at FERMI@Elettra (in session "Tuning")

Experience with tuning at XFEL (in session "Tuning")

Facility needs - or chances - seen from the other side (in session "Facility needs")

Facility needs: Hadron Synchrotrons: Operational challenges at the LHC (in session "Facility needs")

Facility needs: Synchrotrons (in session "Facility needs")

Facility needs: XFELs (in session "Facility needs")

Fault detection and alarm systems for the CERN Technical Infrastructure (in session "Facility needs")

Forecasting of Beam Interlocks in High Intensity (Hadron) Accelerators (in session "Simulations and Modeling")

GANs for Simulation in HEP (in session "Simulations and Modeling")

General experience with online optimization (in session "Tuning")

Introduction to Bayesian Optimization (in session "Tuning")

Light Source Simulations (in session "Simulations and Modeling")

Logistic Regression, Neural Networks (in session "ML Tutorial")

Machine learning application on the investigation of the micro-bunching instability at storage rings (in session "Data Analysis")

Machine learning for anomaly detection in large distributed accelerator systems (in session "Prognosics")

Neural Network Modeling and Virtual Diagnostics at FAST (in session "Simulations and Modeling")

Neural Networks (in session "ML Tutorial")

Non-parametric Density Estimators for the Measurements of Ionization Cooling (in session "Data Analysis")

Poster Blitz (in session "Tuning")

Sloppy and Genetic Algorithms for Low-Emittance Tuning at CESR (in session "Tuning")

Summary

Unsupervised Learning (Clustering), Toy Optics model (in session "ML Tutorial")

Welcome

Whitepaper and Collaboration planning

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Events scheduled on*

27/02/2018

28/02/2018

01/03/2018

02/03/2018

03/03/2018

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