论文标题
新型气态探测器的Picsecd-dord阶准精度的计时技术
Timing techniques with picosecond-order accuracy for novel gaseous detectors
论文作者
论文摘要
开发了一个模拟模型来训练人工神经网络（ANN），以确切的时间为Picosec Micromegas探测器信号。目的是开发快速的在线定时算法，并最大程度地减少在数据获取过程中要保存的信息。在Femptsecond激光测试梁运行期间，通过快速示波器收集Picosec波形并通过快速示波器进行数字化。通过模拟算法利用了一个包含带动激光束强度的波形的数据集，从picoSec光电座上消除了每次光脉冲的一个以上光电脉冲的发射，通过模拟算法来生成波形以训练ANN。相对于快速光电二极管时间引用，使用了第二个数据集的多光电子波形来评估ANN的性能。 ANN的定时性能与完整的离线信号处理的结果相同，达到18.3 $ \ pm $ 0.6 ps的时序精度。
A simulation model is developed to train Artificial Neural Networks (ANN), for precise timing of PICOSEC Micromegas detector signals. The aim is to develop fast, online timing algorithms as well as minimising the information to be saved during data acquisition. PICOSEC waveforms were collected and digitised by a fast oscilloscope during a femptosecond-laser test beam run. A data set comprising waveforms collected with attenuated laser beam intensity, eradicating the emission of more than one photoelectron per light pulse from the PICOSEC photocathode, was utilised by a simulation algorithm to generate waveforms to train an ANN. A second data set of multi-photoelectron waveforms was used to evaluate the ANN performance in determining the PICOSEC Signal Arrival Time, relative to a fast photodiode time-reference. The ANN timing performance is the same as the results of a full offline signal processing, achieving a timing precision of 18.3$\pm$0.6 ps.
