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3rd ICFA Beam Dynamics Mini-Workshop on Machine Learning Applications for Particle Accelerators (Chicago, IL - Palmer House Hilton)
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US/Central
Chicago, IL
Chicago, IL
Kevin Brown
(C-AD)
Description
We are pleased to announce the 3rd ICFA Beam Dynamics Mini-Workshop on Machine Learning Applications for Particle Accelerators will be held in Chicago, Il, USA. This will be the third workshop in a series that began in 2018 at SLAC, CA, USA followed by a second workshop held at Villigen PSI, Switzerland in 2019. A third workshop had been planned to be held in Seoul, Korea in 2020, but unfortunately had to be canceled due to the COVID-19 pandemic.
The goal of this third workshop is to continue to work on building a world-wide community of researchers and engineers interested in applying artificial intelligence and machine learning technologies to particle accelerators.
We will be accepting abstracts for authors who want to attend the workshop. The guidelines and instructions are as follows:
Abstract Title: The title of the contribution using initial capital letters, for example, โThis is a Presentation Title in Initial Capital Lettersโ.
Author Names and Affiliations
Abstract Content: An abstract is a concise summary of a presentation. It should describe the presentation and include a statement of the issue, research methods, and significant findings. Abstracts should be written as one continuous paragraph. Abstract text should not exceed 1200 characters.
Contribution Type: Select the preferred presentation type.
Comments: Include acknowledgments, funding statements, etc.
Important Notice: If you have any difficulties submitting an abstract, please email your Title, list of authors, abstract, and whether it is a requested talk or poster presentation directly to brownk@bnl.gov and petway@bnl.gov.
Event ID: 44128
Workshop Coordinator
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1
Registration 3rd Floor Wabash Room
3rd Floor Wabash Room
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2
Tutorials - Prep and Instructions
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09:45
Break (with cold drinks and snacks)
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4
Tutorial on Anomaly DetectionSpeaker: Antonin Sulc (MCS (MCS Fachgruppe 4))
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10:50
Break
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5
Tutorial on Reinforcement Learning (Speaker: Kishansingh Rajput, JLAB)
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11:55
Lunch Break (on your own)
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7
Tutorial on Uncertainty QuantificationSpeaker: Natalie Isenberg (Computational Science Initiative)
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14:55
Break
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Collaborations: Online Optimizations
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BadgerSpeaker: Zhe Zhang (SLAC)
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RadiaSoft Tools (RadiaSoft Team, RadiaSoft)
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Open Discussion
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16:15
Break
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Collaborations: Online Models
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BadgerSpeaker: Zhe Zhang (SLAC)
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RadiaSoft Tools (RadiaSoft Team, RadiaSoft)
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Open Discussion
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18
Meet & Greet Honore Room (The Palmer House Hilton)
Honore Room
The Palmer House Hilton
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19
Registration (Coffee and Snacks will be available)
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Bayesian Optimization (Session Moderator: Andrea Santamaria Garcia, Karlsruhe Institute of Technology)
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21
Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with Partial MeasurementsSpeaker: Sara Miskovich (staff@stanford.edu;member@stanford.edu)
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22
Machine Learning Tools to support the ATLAS Ion Linac Operations at ArgonneSpeaker: Jose L. Martinez-Marin (member@anl.gov)
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21
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10:20
Break
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Reinforecement Learning (Session Moderator: Christine Sweeney, LANL)
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24
Machine Learning for Slow Spill Regulation in the Fermilab Delivery RingSpeaker: Mattson Thieme (NorthWestern)
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25
Reinforcement Learning Applied to Optimization of LHC Beams in the CERN Proton SynchrotronSpeaker: Joel Axel Wulff (CERN)
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26
A Closer Look at Reinforcement Learning for Beam-Based Feedback SystemsSpeaker: Leander Grech (Univ. of Malta)
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24
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11:55
Lunch Break (on your own)
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Prognostics (Session Moderator: Verena Kain, CERN)
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27
SRF Cavity Fault Classification and Prediction at Jefferson LabSpeaker: Chris Tennant (Jefferson Laboratory)
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28
Beam-Based RF Station Fault Identification at the Linac Coherent Light SourceSpeaker: Ryan Humble (Stanford)
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Status of Data-Driven Bea Trajectory Anomaly DetectionSpeaker: Antonin Sulc (MCS (MCS Fachgruppe 4))
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30
Disentangling Beam Losses in the Fermilab Main Injector Enclosure Using Real-time Edge AISpeaker: Kyle Hazelwood (FNAL)
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15:05
Break
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31
Uncertainty Aware Anomaly Detection to Predict Errant Beam Pulses and GradCAM AnalysisSpeaker: Kishansingh Rajput (Jefferson Lab)
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27
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Al for Muon Collider Design: Progress and PlansSpeaker: Elena Fol (member@cern.ch)
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33
RadiaSoft Tools (RadiaSoft Team, RadiaSoft)Speaker: Jonathan Edelen (RadiaSoft, LLC)
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Poster Session (cold drinks and snacks) 4th Floor Exhibit Hall
4th Floor Exhibit Hall
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34
Classification and Prediction of Superconducting Magnet QuenchesSpeaker: Joshua Curtis-Einstein (RadiaSoft, LLC)
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35
Beam Loss Disentangling Model Hyperparameter Tuning MethodsSpeaker: Isaiah Jones (Northwestern University)
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36
Graph Embeddings for CEBAF Operations: Progress and Future PlansSpeaker: Chris Tennant (Jefferson Laboratory)
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37
Emittance Measurement Speedup with Machine Learning at the Coherent electron Cooling Experiment at RHICSpeaker: Weijian Lin (Cornell Univ.)
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Machine Learning for Beam Emittance Measurement and Aberration Correction of an Electron MicroscopeSpeaker: Desheng Ma (member@cornell.edu;alum@cornell.edu;student@cornell.edu)
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Enhancing UED temporal resolution with time-stamping virtual diagnosticsSpeaker: FREDERICK CROPP (UCLA)
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40
Analysis and Visualisation of Transverse beam properties at the European XFELSpeaker: Raimund Kammering (Deutsches Elektronen-Synchrotron DESY)
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41
Muon monitor signal to predict NuMI beam parameters and horn current by applying Machine Learning techniquesSpeaker: Don Athula Wickremasinghe
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Relating Initial Distribution to Beam Loss on the Front End of a Heavy-Ion Linac Using Machine LearningSpeaker: Anthony Tran (student@msu.edu;member@msu.edu)
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Machine learning-based surrogate model construction for optics matching at the European XFELSpeaker: Zihan Zhu (DESY)
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An Efficient Classifier-Based Surrogate Assisted Evolutionary AlgorithmSpeaker: Christopher Pierce (member@cornell.edu;alum@cornell.edu;student@cornell.edu)
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45
Image Segmentation for Automated Sample Alignment in Neutron Scattering ExperimentsSpeaker: M. Henderson (RadiaSoft, LLC)
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BPM measurement prediction based on HOM signals using Machine LearningSpeaker: Jorge Diaz Cruz (University of New Mexico)
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Updates on the surrogate model design for MUEDSpeaker: Salvador Sosa (UNM)
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Prospects for Machine Learning and Pulse Shaping in the Scorpius AcceleratorSpeaker: E.R. Scott (NNSS)
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Exploring and Applying Different Machine Learning Techniques in a SynchrotronSpeaker: Bohong Huang (Stony Brook University)
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Neural Network Surrogate Priors for Efficient Bayesian OptimizationSpeaker: Ryan Roussel (SLAC National Laboratory)
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Longitudinal Phase Space Manipulation at the LCLS using Neural Networks and Bayesian OptimizationSpeaker: Auralee Edelen (SLAC)
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Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World TrainingSpeaker: Jan Kaiser (Deutsches Elektronen-Synchrotron DESY)
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Xopt: A Simplified Framework for Optimization of Arbitrary Problems using Advanced AlgorithmsSpeaker: Ryan Roussel (SLAC National Laboratory)
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Fast THz Radiation Optimization for Linear Accelerator using Surrogate ModelSpeaker: Chenran Xu (member@kit.edu;employee@kit.edu)
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Automatic Optimization of X-ray Free-Electron Laser at SACLASpeaker: Hirokazu Maesaka (Spring8)
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A Smart Alarm for the CEBAF InjectorSpeaker: Chris Tennant (Jefferson Laboratory)
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Continuous Anomaly Detection and Labeling for the Fermilab LinacSpeaker: Jason St. John (Fermilab)
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Apply Machine Learning in Orbit Control and Accelerator StabilizationSpeaker: Zeyu Dong (Stony Brook University)
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Machine Learning to Support the ATLAS Linac Operations at ArgonneSpeaker: Brahim Mustapha (ANL)
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Design Optimization and In Situ Surrogate Modeling Activities in the Beam, Plasma & Accelerator Simulation Toolkit (BLAST)Speaker: Axel Huebl (Lawrence Berkeley National Laboratory)
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Data-Driven Chaos Indicators for Beam DynamicsSpeaker: Robert Rainer (Brookhaven National Laboratory)
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34
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19
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Modeling (Session Moderator: Chris Tennant, JLAB)
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Data-Driven Chaos Indictor for Nonlinear Dynamics and Applications on Storage Ring Lattice DesignSpeaker: Robert Rainer (BNL)
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Towards End-to-End Differentiable Accelerator ModelingSpeaker: Juan Pablo Gonzalez-Aguilera (University of Chicago)
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Experience with Integrated Systems for Online Physics Modeling, Adaptive ML Modeling, and Model-Based ControlSpeaker: Auralee Edelen (SLAC)
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Luminosity Tuning for Future Electron Ion ColliderSpeaker: Derong Xu (Brookhaven National Laboratory)
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64
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10:10
Break
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Facility (Session Moderator: Francesco Maria Velotti, CERN)
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68
Application of Natural Language Processing on Electronic LogbooksSpeaker: Jennefer Maldonado (Brookhaven National Laboratory)
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Towards (more) autonomous accelerators - Status and Vision at CERNSpeaker: Verena Kain (CERN)
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How to get ML into the Control Room - the CERN ML FrameworksSpeaker: Nico Madysa (member@cern.ch)
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11:55
Lunch Break (on your own)
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Data Analysis and Control of an MeV Ultrafast Electron Diffraction System Using Machine LearningSpeaker: Trudy Bolin (UNM)
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68
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Optimization (Session Moderator: Hirokazu Maesaka, RIKEN Spring-8 Center)
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Machine Learning for Tuning, Prediction, and Control at the KIT electron acceleratorsSpeaker: Andrea Santamaria Garcia (Karlsruhe Institute of Technology)
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Towards Linac RF Optimization with Machine LearningSpeaker: Ralitsa Sharankova (FNAL)
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ID Orbit Compensation with Neural Networks for PETRA IIISpeaker: Bianca Veglia (DESY)
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15:05
Break (Coffee and Snacks will be available)
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75
Recent Progress on ML-based Optimization and anomaly prediction at APSSpeaker: Nikita Kuklev (ANL)
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72
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Analysis (Session Moderator: Raimund Kammering, DESY)
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Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable SimulationsSpeakers: Ryan Roussel (SLAC National Laboratory), Ryan Roussel (SLAC)
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Improving Neural Networks Predictions using Physics - PINN for the CERN AcceleratorsSpeaker: Francesco Maria Velotti (CERN)
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Transverse 2D Phase-Space Tomography Using BPMsSpeaker: Kilean Hwang (member@msu.edu;faculty@msu.edu;employee@msu.edu)
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Simulation Studies and Machine Learning Applications for Orbit Correction at the Alternating Gradient SyndhrotronSpeakers: Lucy Lin (Cornell University), Weijian Lin (Cornell Univ.)
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76
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Summaries - Modeling & Anallysis Summary
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Modeling, Affiliation
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Analysis Affiliation
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Open Discussion
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80
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Summaries - Optimization Summary
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83
Opt, Affiliation
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RL, Affiliation
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BO, Affiliation
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86
Open Discussion
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83
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Facility & Prognostics Summary
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87
Prog, Affiliation
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88
Facility, Affiliation
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Open Discussion
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87
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Future WorkshopSpeaker: Daniel Ratner (SLAC National Laboratory)
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Concluding RemarksSpeakers: Kevin Brown (C-AD), Sandra Biedron (Element Aero), Sandra Biedron (Univ. of NM)
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11:05
Lunch Break
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