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Comparison on PMT Waveform Reconstructions with JUNO Prototype

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 Added by Haiqiong Zhang
 Publication date 2019
  fields Physics
and research's language is English




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JUNO is proposed to determine the neutrino mass hierarchy and rich in many other neutrino topics. A prototype is designed and set up for better understanding sub-systems of future detector. The preliminary results show that its threshold reaches ~0.3MeV with trigger rate ~290 Hz on the ground with cosmic muon rate ~35 Hz. Aiming for a better detector understanding from PMT signal, three reconstruction algorithms are compared for PMT waveforms with different overshoot ratios, including charge integration, waveform fitting, and deconvolution. It is concluded that the three methods have similar performance on uncertainty and systematic bias while deconvolution algorithm is best to handle larger overshoot and the simplest charge integration could be considered with controlled overshoot for future fast preliminary reconstruction.



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The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using a 20 kiloton underground liquid scintillator detector (CD). One of the keys is the energy resolution of the CD to reach <3% at 1 MeV, where totally 15,000 MCP-PMT will be used. The optimization of the 20-inch MCP-PMT is very important for better detection efficiency and stable performance. In this work, we will show the study to optimize the MCP-PMT working configuration for charge measurement. Particularly, the quality of PMT signal is another key for high-precision neutrino experiments while most of these experiments are affected by the overshoot of PMT signal from the positive HV scheme. The overshoot coupled with positive HV which is troubling trigger, dead time and precise charge measurement, we have studied to control it to less than 1% of signal amplitude for a better physics measurement. In this article, on the one hand, the optimized HV divider ratio will be presented here to improve its collection efficiency; on the other hand, we will introduce the method to reduce the ratio of overshoot from 10% to 1%.
112 - Y. P. Zhang , J. C. Liu , C. Guo 2017
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