The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) is based on plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. To address such practical concerns, the article proposes a novel machine learning (ML) assisted microwave-plasma interaction based strategy which is capable enough to determine the electron density profile within the plasma. The electric field pattern due to microwave scattering is measured to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of Gaussian-shaped density profiles, in the range $10^{16}-10^{19}m^{-3}$, addressing a range of experimental configurations have been considered in our study. The results obtained show promising performance in estimating the 2D radial profile of the density for the given linear plasma device. The performance of the proposed deep learning based approach has been evaluated using three metrics- SSIM, RMSLE and MAPE. The favourable performance affirms the potential of the proposed ML based approach in plasma diagnostics.

翻译：电子密度是表征任何等离子体的关键参数。大多数低温等离子体（LTP）应用及研究均基于等离子体密度与温度。传统电子密度测量方法可获取线性LTP装置的轴向与径向分布，但存在工作范围有限、仪器繁杂及数据分析流程复杂等主要缺陷。为解决上述实际问题，本文提出一种基于机器学习（ML）辅助的微波-等离子体相互作用新策略，可有效确定等离子体内部电子密度分布。通过测量微波散射产生的电场模式来估算密度分布。概念验证在包含低温、非磁化、碰撞等离子体的模拟训练数据集上进行。本研究考虑了$10^{16}-10^{19}m^{-3}$范围内多种高斯型密度分布，覆盖不同实验配置。结果表明，该方法能有效估算给定线性等离子体装置的二维径向密度分布。采用SSIM、RMSLE和MAPE三项指标评估了所提深度学习方法的性能。优异的性能表现验证了所提基于机器学习的方法在等离子体诊断中的潜力。