In this paper, the possibility of using neural networks for fast tomographic reconstructions of tokamak plasma soft X-ray (SXR) emissivity is investigated. Indeed, the radiative cooling of heavy impurities like tungsten could be detrimental for the plasma core performances of ITER, thus developing robust and fast SXR diagnostic tools is a crucial issue to monitor the impurities and to mitigate in real-time their central accumulation. As preliminary work, a database of emissivity phantoms with associated synthetic measurements is used to train the neural network to solve the inversion problem. The inversion method, training process, and first tomographic reconstructions are presented with the perspectives about our future work.

中文翻译：

---

神经网络：从图像识别到托卡马克等离子断层扫描

在本文中，研究了使用神经网络对托卡马克等离子体软 X 射线 (SXR) 发射率进行快速断层扫描重建的可能性。事实上，钨等重杂质的辐射冷却可能不利于 ITER 的等离子体核心性能，因此开发强大且快速的 SXR 诊断工具是监测杂质并实时减轻其中心积累的关键问题。作为初步工作，使用具有相关合成测量值的发射率模型数据库来训练神经网络以解决反演问题。介绍了反演方法、训练过程和第一次断层扫描重建，并对我们未来的工作进行了展望。