| Paper | Title | Page |
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| MOPA89 | RHIC Electron Beam Cooling Analysis Using Principle Component and Autoencoder Analysis | 260 |
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Funding: Work supported by the US Department of Energy under contract No. DE-AC02-98CH10886. Principal component analysis and autoencoder analysis were used to analyze the experimental data of RHIC operation with low energy RHIC electron cooling (LEReC). This is unsupervised learning which includes electron beam settings and observable during operation. Both analyses were used to gauge the dimensional reducibility of the data and to understand which features are important to beam cooling. |
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| DOI • | reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA89 | |
| About • | Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 12 August 2022 | |
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |
| MOPA90 | Relating Initial Distribution to Beam Loss on the Front End of a Heavy-Ion Linac Using Machine Learning | 263 |
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Funding: This work was supported by a sub-reward from Argonne National Laboratory and supported by the U.S. Department of Energy, under Contract No. DE-AC02-06CH11357. This work demonstrates using a Neural Network and a Gaussian Process to model the ATLAS front-end. Various neural network architectures were created and trained on the machine settings and outputs to model the phase space projections. The model was then trained on a dataset, with non-linear distortion, to gauge the transferability of the model from simulation to machine. |
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| DOI • | reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA90 | |
| About • | Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 11 September 2022 | |
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |