The high power laser system at Extreme Light Infrastructure—Nuclear Physics has demonstrated 10 PW power shot capability. It can also deliver beams with powers of 1 PW and 100 TW in several different experimental areas that carry out dedicated sets of experiments. An array of diagnostics is deployed to characterize the laser beam spatial profiles and to monitor their evolution during the amplification stages. Some of the essential near-field and far-field profiles acquired with CCD cameras are monitored constantly on a large screen television for visual observation and for decision making concerning the control and tuning of the laser beams. Here, we present results on the beam profile classification obtained from datasets with over 14 600 near-field and far-field images acquired during two days of laser operation at 1 PW and 100 TW. We utilize supervised and unsupervised machine learning models based on trained neural networks and an autoencoder. These results constitute an early demonstration of machine learning being used as a tool in the laser system data classification.

中文翻译：

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在 ELI-NP 高功率激光系统中使用机器学习对激光束轮廓进行分类

极光基础设施-核物理中心的高功率激光系统已经展示了10 PW功率的发射能力。它还可以在几个不同的实验区域提供功率为 1 PW 和 100 TW 的光束，以进行专门的实验组。部署一系列诊断来表征激光束空间轮廓并监测其在放大阶段的演变。使用 CCD 摄像机获取的一些基本近场和远场轮廓在大屏幕电视上持续监控，以进行视觉观察以及有关激光束控制和调谐的决策。在这里，我们展示了从具有超过 14 600 个近场和远场图像的数据集中获得的光束轮廓分类结果，这些图像是在 1 PW 和 100 TW 的激光操作两天期间获得的。我们利用基于经过训练的神经网络和自动编码器的监督和无监督机器学习模型。这些结果构成了机器学习被用作激光系统数据分类工具的早期演示。