Activities per year
Abstract
A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.
Original language | English |
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Article number | e9 |
Number of pages | 8 |
Journal | High Power Laser Science and Engineering |
Volume | 11 |
DOIs | |
Publication status | Published - 06 Jan 2023 |
Keywords
- laser wakefield accelerator
- variational neural networks
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Nuclear and High Energy Physics
- Nuclear Energy and Engineering
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Dive into the research topics of 'Laser wakefield accelerator modelling with variational neural networks'. Together they form a unique fingerprint.Datasets
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Code and dataset for "Laser Wakefield Accelerator modelling with Variational Neural Networks"
Streeter, M. (Creator), Zenodo, 06 Jan 2023
Dataset
Activities
- 1 Invited or keynote talk at national or international conference
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Automation and modelling of laser-driven plasma accelerators
Streeter, M. (Advisor)
07 Mar 2023Activity: Talk or presentation types › Invited or keynote talk at national or international conference
Student theses
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Laser-driven muon production for materials inspection and imaging
Calvin, L. (Author), Sarri, G. (Supervisor) & Borghesi, M. (Supervisor), Jul 2025Student thesis: Doctoral Thesis › Doctor of Philosophy
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