| Paper | Title | Page |
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MOIYSP1 |
Machine Learning as a Tool for Online, Surrogate Modelling of Beam Dynamics | |
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| The detailed design and optimization of accelerators has historically relied on high-fidelity simulations whose computational requirements limit their use as online tools. Recently, a growing community has begun reducing this computational burden by applying techniques from machine learning. For example, by learning from a sparse sampling of physics simulations one can develop fast-executing "surrogate models" that approximately predict accelerator performance for entirely new design parameters. Using these models can reduce compute times for multi-objective optimization studies by several orders of magnitude. In addition, surrogate models are now being applied in operational settings to enable non-invasive diagnostics and real-time optimization. This talk will cover developments in this field, applications to medium-energy electron photoinjectors, and how such surrogate models may improve our physics understanding of present and future accelerators. | ||
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Slides MOIYSP1 [19.989 MB] | |
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| MOPOPT058 | Machine Learning Training for HOM Reduction in a TESLA-Type Cryomodule at FAST | 400 |
| SUSPMF099 | use link to see paper's listing under its alternate paper code | |
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| Low emittance electron beams are of high importance at facilities like the Linac Coherent Light Source II (LCLS-II) at SLAC. Emittance dilution effects due to off-axis beam transport for a TESLA-type cryomodule (CM) have been shown at the Fermilab Accelerator Science and Technology (FAST) facility. The results showed the correlation between the electron beam-induced cavity high-order modes (HOMs) and the Beam Position Monitor (BPM) measurements downstream the CM. Mitigation of emittance dilution can be achieved by reducing the HOM signals. Here, we present a couple of Neural Networks (NN) for bunch-by-bunch mean prediction and standard deviation prediction for BPMs located downstream the CM. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT058 | |
| About • | Received ※ 15 June 2022 — Revised ※ 18 June 2022 — Accepted ※ 24 June 2022 — Issue date ※ 26 June 2022 | |
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| TUPOST056 | Multi-Objective Bayesian Optimization at SLAC MeV-UED | 995 |
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| SLAC MeV-UED, part of the LCLS user facility, is a powerful ’electron camera’ for the study of ultrafast molecular structural dynamics and the coupling of electronic and atomic motions in a variety of material and chemical systems. The growing demand of scientific applications calls for rapid switching between different beamline configurations for delivering electron beams meeting specific user run requirements, necessitating fast online tuning strategies to reduce set up time. Here, we utilize multi-objective Bayesian optimization(MOBO) for fast searching the parameter space efficiently in a serialized manner, and mapping out the Pareto Front which gives the trade-offs between key beam parameters, i.e., spot size, q-resolution, pulse length, pulse charge, etc. Algorithm, model deployment and first test results will be presented. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST056 | |
| About • | Received ※ 08 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 09 July 2022 | |
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| TUPOST058 | Badger: The Missing Optimizer in ACR | 999 |
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| Badger is an optimizer specifically designed for Accelerator Control Room (ACR). It’s the spiritual successor of Ocelot optimizer. Badger abstracts an optimization run as an optimization algorithm interacts with an environment, by following some pre-defined rules. The environment is controlled by the algorithm and tunes/observes the control system/machine through an interface, while the users control/monitor the optimization flow through a graphical user interface (GUI) or a command line interface (CLI). This paper would introduce the design principles and applications of Badger. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST058 | |
| About • | Received ※ 08 June 2022 — Revised ※ 10 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 17 June 2022 | |
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| TUPOST059 | PyEmittance: A General Python Package for Particle Beam Emittance Measurements with Adaptive Quadrupole Scans | 1003 |
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| The emittance of a particle beam is a critically important parameter for many particle accelerator applications. Its measurements guide the initial tuning of an accelerator and are typically done using quadrupole or wire scans. Quadrupole scans are time-intensive, and it can be difficult to determine scan values that provide a good emittance measurement. To address this issue, we describe an adaptive quadrupole scan method that automates the determination of the scan range. With a given initial set of scanning values, our method adapts the range to capture the waist of the beam, and returns the Twiss parameters and a measure of the beam matching at the measurement screen. With the added capability to repeat beam size measurements when needed, this method provides a reliable measurement of the emittance even with sub-optimal initial conditions. To efficiently integrate these measurements into Python-based machine learning optimizations, the method was developed into a Python package, PyEmittance, at the SLAC National Accelerator Laboratory. We present the experimental tests of PyEmittance as performed at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Test (FACET-II). | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST059 | |
| About • | Received ※ 08 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 17 June 2022 | |
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| WEPOMS013 | Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-Based Charged Particle Beams | 2261 |
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| Particle accelerators support a wide array of scientific, industrial, and medical applications. To meet the needs of these applications, accelerator physicists rely heavily on detailed simulations of the complicated particle beam dynamics through the accelerator. One of the most computationally expensive and difficult-to-model effects is the impact of Coherent Synchrotron Radiation (CSR). CSR is one of the major drivers of growth in the beam emittance, which is a key metric of beam quality that is critical in many applications. The CSR wakefield is very computationally intensive to compute with traditional electromagnetic solvers, and this is a major limitation in accurately simulating accelerators. Here, we demonstrate a new approach for the CSR wakefield computation using a neural network solver structured in a way that is readily generalizable to new setups. We validate its performance by adding it to a standard beam tracking test problem and show a ten-fold speedup along with high accuracy. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS013 | |
| About • | Received ※ 10 June 2022 — Revised ※ 16 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 03 July 2022 | |
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