Plastic scintillators(PSs)embedded with wavelength-shifting fibers are widely used in high-energy particle physics,such as in muon taggers,as well as in medical physics and other applications.In this study,a simulatio...Plastic scintillators(PSs)embedded with wavelength-shifting fibers are widely used in high-energy particle physics,such as in muon taggers,as well as in medical physics and other applications.In this study,a simulation package was built to evaluate the effects of the diameter and layout of optical fibers on the light yield with different configurations.The optimal optical configuration was designed based on simulations and validated using two PS prototypes under certain experimental condi-tions.A top veto tracker(TVT)for the JUNO-TAO experiment,comprising four layers of 160 strips of PS,was designed and evaluated.The threshold was evaluated when the muon tagging efficiency of a PS strip was>99%.The efficiency of three layer out of four layer of TVT is>99%,even with a tagging efficiency of a single strip as low as 97%,using a threshold of 10 photoelectrons and assuming a 40%silicon PM photon detection efficiency.展开更多
Recent reactor antineutrino experiments have observed that the neutrino spectrum changes with the reactor core evolution and that the individual fissile isotope antineutrino spectra can be decomposed from the evolving...Recent reactor antineutrino experiments have observed that the neutrino spectrum changes with the reactor core evolution and that the individual fissile isotope antineutrino spectra can be decomposed from the evolving data,providing valuable information for the reactor model and data inconsistent problems.We propose a machine learning method by building a convolutional neural network based on a virtual experiment with a typical short-baseline reactor antineutrino experiment configuration:by utilizing the reactor evolution information,the major fissile isotope spectra are correctly extracted,and the uncertainties are evaluated using the Monte Carlo method.Validation tests show that the method is unbiased and introduces tiny extra uncertainties.展开更多
基金supported by the School of Physics at Sun Yat-sen University,China
文摘Plastic scintillators(PSs)embedded with wavelength-shifting fibers are widely used in high-energy particle physics,such as in muon taggers,as well as in medical physics and other applications.In this study,a simulation package was built to evaluate the effects of the diameter and layout of optical fibers on the light yield with different configurations.The optimal optical configuration was designed based on simulations and validated using two PS prototypes under certain experimental condi-tions.A top veto tracker(TVT)for the JUNO-TAO experiment,comprising four layers of 160 strips of PS,was designed and evaluated.The threshold was evaluated when the muon tagging efficiency of a PS strip was>99%.The efficiency of three layer out of four layer of TVT is>99%,even with a tagging efficiency of a single strip as low as 97%,using a threshold of 10 photoelectrons and assuming a 40%silicon PM photon detection efficiency.
基金supported by the National Natural Science Foundation of China (Nos.11675273 and 12075087)the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDA10011102)。
文摘Recent reactor antineutrino experiments have observed that the neutrino spectrum changes with the reactor core evolution and that the individual fissile isotope antineutrino spectra can be decomposed from the evolving data,providing valuable information for the reactor model and data inconsistent problems.We propose a machine learning method by building a convolutional neural network based on a virtual experiment with a typical short-baseline reactor antineutrino experiment configuration:by utilizing the reactor evolution information,the major fissile isotope spectra are correctly extracted,and the uncertainties are evaluated using the Monte Carlo method.Validation tests show that the method is unbiased and introduces tiny extra uncertainties.
