Optimization of laser-driven quantum beam generation and the applications with artificial intelligence
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | Physics of Plasmas |
| MU Faculty or unit | |
| Citation | |
| Web | https://pubs.aip.org/aip/pop/article/31/5/053108/3295205/Optimization-of-laser-driven-quantum-beam |
| Doi | http://dx.doi.org/10.1063/5.0190062 |
| Keywords | Convolutional neural network; Artificial intelligence; Artificial neural networks; Machine learning; Astrophysics; Graphene; Spectroscopy; Tracking devices; Lasers; Plasma turbulence |
| Description | We have investigated space and astrophysical phenomena in nonrelativistic laboratory plasmas with long high-power lasers, such as collisionless shocks and magnetic reconnections, and have been exploring relativistic regimes with intense short pulse lasers, such as energetic ion acceleration using large-area suspended graphene. Increasing the intensity and repetition rate of the intense lasers, we have to handle large amounts of data from the experiments as well as the control parameters of laser beamlines. Artificial intelligence (AI) such as machine learning and neural networks may play essential roles in optimizing the laser and target conditions for efficient laser ion acceleration. Implementing AI into the laser system in mind, as the first step, we are introducing machine learning in ion etch pit analyses detected on plastic nuclear track detectors. Convolutional neural networks allow us to analyze big ion etch pit data with high precision and recall. We introduce one of the applications of laser-driven ion beams using AI to reconstruct vector electric and magnetic fields in laser-produced turbulent plasmas in three dimensions. |
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