Study of UV Ne II line shapes in the cathode sheath of an abnormal glow discharge

We report results of an experimental and theoretical study of complex UV line shapes of Ne II 369.421 nm, Ne II 370.962 nm, Ne II 371.308 nm, and.Ne II 372.711 nm lines in the cathode sheath (CS) region of an abnormal DC glow discharge in pure neon. Two sets of experimental profiles and electric field distributions, one for discharge with tungsten and the other with titanium cathode, were obtained by means of the optical emission spectroscopy (OES) and theoretically studied by the iterative CS kinetic model. It is shown that our theoretical model enables the determination of the most important CS parameters (e.g. the thickness of the CS region, and the theoretical distributions of electric field and gas temperature) and thereupon based accurate theoretical predictions of the experimentally observed profiles of the studied lines.     

芯片级光钟技术综述

芯片级光钟是一种基于热原子中光频跃迁的新型光钟,近年来随着光子集成技术、激光技术和微机电技术的发展,该类光钟可以实现较小的体积和较高的精度,有望广泛应用于对体积、质量、功耗和精度敏感的各种国防装备中.介绍了国内外芯片级光钟技术的进展情况,阐述了芯片级光钟的相关技术,包括基于双光子跃迁的光频标技术、微制造气室技术和微腔光频梳技术等,分析了影响频率稳定度和准确度的主要因素,最后对芯片级光钟面临的机遇和挑战进行了简要的展望.     

Time resolved study of laser triggered electric discharge spark in atmosphere: Machine learning approach

In this paper we analyze the possibilities of using machine learning algorithms for analysis of optical spectra of electric discharge spark in atmosphere. Breakdown in air can be initiated by intense laser pulse, making plasma which has a significant electrical conductivity. The formed plasma can be further maintained by electric current obtained from capacitor discharge. In such a case the capacitor voltage can be much lower than the striking voltage (the voltage needed to initiate the electric breakdown in air). Present setup has timing precision and low jitter of fast laser and arbitrary high energies corresponding to capacitance and voltage to which the capacitor is charged. We have used a streak camera equipped with a spectrograph to analyze optical emission of plasma obtained in this way. Q-switched Nd:Yag laser was used to achieve the initial breakdown in air. Machine learning methods were used in order to classify optical spectra of plasmas with different electron temperatures obtained with different excitation energies. We have shown that, instead of using the usual way of identifying the spectral peaks and calculating their intensity ratio, it is possible to train the computer software to recognize the spectra corresponding to different electron temperatures. Principal component analysis was used to reduce the dimensionality of problem. We present possibilities of plasma electron temperature estimation based on several clustering algorithms.