Electron multiplier tube can be used in the field of electron beam measurement, which can be used to measure the small current electron beam after amplification. In the measurement, in addition to the internal error of the multiplier tube, different incident electron beams will also affect the magnification of the multiplier tube. In order to analyze the error of different incident electron beam parameters on the electron multiplier tube, this paper uses CST to establish the electron multiplier tube model. After verifying the correctness of the simulation model, the energy, incident angle and radius of the incident electron beam are simulated and analyzed. The simulation results show that The parameters of the incident electron beam mainly affect the first multiplication electrode, including its collection efficiency and the number of secondary electrons generated, and ultimately affect the magnification of the electron multiplier tube.
Streak cameras based on THz-driven split-ring resonator (SRR) are recently proposed to achieve electron bunchlengthmeasurement with femtosecond resolution due to the available GV/m level streaking field. However, to apply the SRRtothe streaking experiment, the SRR needs to have a relatively large gap to accommodate the beamto traverse. Alargergap leads to higher electromagnetic power radiation, which requires high exciting THz power to compensate powerradiation to achieve a strong streaking field. The maximum stored energy in the gap is determined by the availableexciting THz power. If a single THz pulse drives the SRR, the achievable streaking field is not enough for highresolution because of the radiation diluting the stored energy. This paper proposes a novel method to illuminate theSRRwith multipulse, which can accumulate the energy stored in the gap to compensate the electromagnetic radiationuntil saturation and consequently enhance the resonance to a much higher peak field. We explore the effects of drivingpulseswith various intervals and obtain an optimal field enhancement factor up to 47 with the THz field strength of 1MV/m. The particle tracking simulation indicates that the multipulse-driven method can achieve the temporal resolution of 0.4fswith the central frequencies of SRR at 0.3 THz.
Ultrafast Electron Diffraction (UED) is an indispensable tool that enables the study of ultrafast dynamics on an atomic/molecular scale. Ultrashort high brightness electron beams are needed to capture the critical ultrafast events, particularly for studying the irreversible biochemical processes in the single-shot mode. However, the Coulomb interactions in the space-charge dominated electron beam limit attainable beam length and dilute beam quality during its propagation. The beam emittance increases significantly during propagation due to the severe space charge effect (SCE) because of low energy. It is essential to understand the emittance evolution behavior in detail during its passage for improving the UED performance further. The multi-slit method is selected to eliminate the SCE influence on the measurement by a low sampling rate of the electrons, making it possible to diagnose the emittance. However, the insufficient samplings create challenges in reconstructing the original beam information. This paper introduces an algorithm that can precisely reproduce beam parameters from severely under-sampled data.
An Ultrafast Electron Diffraction (UED) based on an RF photocathode electron gun has the advantage of producing MeV relativistic probing electron beams, which can maintain a high time resolution of ~100 fs while keeping more electrons to improve the S/R ratio of the image. However, the jitter of driving RF power in the electron gun between pulse to pulse has an indispensable impact on the electron energy stability leading to the Time of Flight (ToF) jitter, which creates asynchronization between the pump laser and the probing electron worsening the time resolution. To stabilize the beam energy to the designed value 3 MeV and reduce the ToF jitter further, we propose controlling the electron energy based on an energy spectrometer directly. An electron spectrometer based on a C-type dipole is being designed to achieve high energy resolution. This paper will introduce the design of the energy spectrometer, and particle tracking is implemented to demonstrate the feasibility of the design.
The advances in electron accelerator science and technology continue to reach shorter bunch lengths, even down to femtosecond, paving a way to generate coherent Smith-Purcell radiation naturally, taken as one of the most promising THz sources. In order to design a high power and broadly tunable THz radiation source, we make theoretical and numerical analysis of the characteristic of coherent Smith-Purcell radiation, which demonstrates good agreement between them. In the paper, we also present the comparison of spectra of coherent Smith-Purcell produced from the interaction of a single bunch and a train of microbunches.
KEYWORDS: Data modeling, Neural networks, Principal component analysis, Error analysis, Data mining, Transformers, Data processing, Statistical analysis, Performance modeling, Binary data
With the continuous updating of communication network technology and the influence of different factors (such as humidity, specific gravity, temperature, etc.), the monitoring data acquired by the grid equipment is exponentially increasing and the complexity of the data is also continuously improving. Taking full advantages of these big data, studying the measurement characteristics of electronic transformers in operation and discovering the relationship of environment, load and other factors will help optimize the performance of electronic transformers, give users a better experience and improve the benefits of the companies. However, the emergence of massive data makes traditional data analysis methods unable to meet the accuracy and real-time performance of data processing. Therefore, how to effectively and accurately solve the big data analysis and processing problems is particularly urgent. To effectively process this data, we have chosen the popular data mining method. Compared to traditional machine learning, we choose a relatively simple deep learning network for data mining. A feed forward neural network is used for classification. On the basis of classification, a new network is established to perform nonlinear regression prediction on the data, then an error transfer model is established. In the regression prediction problem, due to the high dimensionality and high computational complexity of the original data, we use the PCA method to reduce the feature dimension, which is also helpful to establish a nonlinear relationship between the learning characteristics of the deep neural network and the predicted values. Compared with the traditional feed forward neural network, the accuracy of our network has been significantly improved.
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