Optimizing a Superconducting Radiofrequency Gun Using Deep Reinforcement Learning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2022
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917959237107712 |
|---|---|
| author | Meier, David Ramirez, Luis Vera Völker, Jens Sick, Bernhard Viefhaus, Jens Hartmann, Gregor |
| author_facet | Meier, David Ramirez, Luis Vera Völker, Jens Sick, Bernhard Viefhaus, Jens Hartmann, Gregor |
| contents | Superconducting photoelectron injectors are a promising technique for generating high brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultra-fast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimization of the beam properties is challenging due to the high amount of possible machine parameter combinations. In this article, we show the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the amount of required computation time, we replace the costly simulation by a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms common optimization methods for the required function evaluations given a defined minimum accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_09935 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Optimizing a Superconducting Radiofrequency Gun Using Deep Reinforcement Learning Meier, David Ramirez, Luis Vera Völker, Jens Sick, Bernhard Viefhaus, Jens Hartmann, Gregor Accelerator Physics Data Analysis, Statistics and Probability Superconducting photoelectron injectors are a promising technique for generating high brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultra-fast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimization of the beam properties is challenging due to the high amount of possible machine parameter combinations. In this article, we show the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the amount of required computation time, we replace the costly simulation by a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms common optimization methods for the required function evaluations given a defined minimum accuracy. |
| title | Optimizing a Superconducting Radiofrequency Gun Using Deep Reinforcement Learning |
| topic | Accelerator Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2203.09935 |