arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2203.00367physics.ins-dethep-ex
收藏

Timing techniques with picosecond-order accuracy for novel gaseous detectors

A. Tsiamis, K. Kordas, I. Manthos, M. Tsopoulou, S. E. Tzamarias

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑