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| MOPAB289 | Machine Learning Training for HOM reduction and Emittance Preservation in a TESLA-type Cryomodule at FAST | 916 |
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| Low emittance electron beams are of high importance at facilities like the 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 facility. The results showed the correlation between the electron beam-induced cavity high-order modes (HOMs) and submacropulse centroid slewing and oscillation downstream of the CM. Mitigation of emittance dilution can be achieved by reducing the HOM signals and the variances in the submacropulse beam positions downstream of the CM. Here we present a Machine Learning based optimization and model construction for HOM signal level reduction using Neural Networks and Gaussian Processes. To gather training data we performed experiments using single bunch and 50 bunch electron beams with charges up to 125 pC/b. We measured HOM signals of all cavities and beam position with a set of BPMs downstream of the CM. The beam trajectory was changed using V/H125 corrector set located upstream of the CM. The results presented here will inform the LCLS-II injector commissioning and will serve as a prototype for HOM reduction and emittance preservation. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB289 | |
| About • | paper received ※ 19 May 2021 paper accepted ※ 09 June 2021 issue date ※ 14 August 2021 | |
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| TUPAB273 | Observations on Submicropulse Electron-Beam Effects From Short-Range Wakefields in Tesla-Type Superconducting Rf Cavities | 2105 |
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Funding: Work supported by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. In previous experiments at the Fermilab Accelerator Science and Technology (FAST) facility, the effects of higher-order modes (HOMs) in TESLA-type cavities on submacropulse centroid motion were elucidated*. We now have extended our investigations to short-range wakefields (SRWs) in these cavities. The latter result in submicropulse effects where the transverse wakefields cause head-tail centroid shifts. We used a Hamamatsu C5680 UV-visible synchroscan streak camera to synchronously sum the OTR from each of the 50 micropulses in the macropulse. We generated the y-t effect in the 41-MeV beam by purposely steering the beam off axis in y at the entrance of the first capture cavity. The head-tail transverse kicks within the 11-ps-long micropulses of 500 pC each were observed at the 100-micron level for steering off-axis in one cavity and several 100 microns for two cavities. These SRW results will be compared to simulations from the ASTRA model of a single micropulse in FAST. Since the SRW kicks go inversely with energy, these emittance-dilution effects are particularly relevant to the LCLS-II injector commissioning plans where <1 MeV beam will be injected into a TESLA-type cryomodule. * A.H. Lumpkin et al, Phys. Rev. Accel. and Beams 23, 054401 (2020). |
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| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB273 | |
| About • | paper received ※ 18 May 2021 paper accepted ※ 09 June 2021 issue date ※ 28 August 2021 | |
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| WEPAB308 | Measurement-Based Surrogate Model of the SLAC LCLS-II Injector | 3395 |
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Funding: This project was funded by the DOE SCGSR Program. There is significant effort within particle accelerator physics to use machine learning methods to improve modeling of accelerator components. Such models can be made realistic and representative of machine components by training them with measured data. These models could be used as virtual diagnostics or for model-based control when fast feedback is needed for tuning to different user settings. To prototype such a model, we demonstrate how a machine learning based surrogate model of the SLAC LCLS-II photocathode injector was developed. To create machine-based data, laser measurements were taken at the LCLS using the virtual cathode camera. These measurements were used to sample particles, resulting in realistic electron bunches, which were then propagated through the injector via the Astra space charge simulation. By doing this, the model is not only able to predict many bulk electron beam parameters and distributions which are often hard to measure or not usually available to measure, but the predictions are more realistic relative to traditionally simulated training data. The methods for training such models, as well as model capabilities and future work are presented here. |
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| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308 | |
| About • | paper received ※ 26 May 2021 paper accepted ※ 27 July 2021 issue date ※ 24 August 2021 | |
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| THPAB217 | Lightsource Unified Modeling Environment (LUME), a Start-to-End Simulation Ecosystem | 4212 |
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| SLAC is developing the Lightsource Unified Modeling Environment (LUME) for efficient modeling of X-ray free electron laser (XFEL) performance. This project takes a holistic approach starting with the simulation of the electron beams, to the production of the photon pulses, to their transport through the optical components of the beamline, to their interaction with the samples and the simulation of the detectors, and finally followed by the analysis of simulated data. LUME leverages existing, well-established simulation codes, and provides standard interfaces to these codes via open-source Python packages. Data are exchanged in standard formats based on openPMD and its extensions. The platform is built with an open, well-documented architecture so that science groups around the world can contribute specific experimental designs and software modules, advancing both their scientific interests and a broader knowledge of the opportunities provided by the exceptional capabilities of X-ray FELs. | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB217 | |
| About • | paper received ※ 20 May 2021 paper accepted ※ 20 July 2021 issue date ※ 19 August 2021 | |
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