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تسجيل مستخدم جديدA semi-analytical energy response model for low-energy events in JUNO
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The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation neutrino experiment under construction in China expected to be completed in 2022. As the main goal it aims to determine the neutrino mass ordering with 3-4 $sigma$ significance using a 20 kton liquid scintillator detector. It will measure the oscillated energy spectrum of electron anti-neutrinos from two nuclear power plants at about 53 km baseline with an unprecedented energy resolution of 3% at 1 MeV. A requirement of the JUNO experiment is the knowledge of the energy non-linearity of the detector with a sub-percent precision. As the light yield of the liquid scintillator is not fully linear to the energy of the detected particle and dependent on the particle type, a model for this light yield is presented in this paper. Based on an energy non-linearity model of electrons, this article provides the conversion to the more complex energy response of positrons and gammas. This conversion uses a fast and simple algorithm to calculate the spectrum of secondary electrons generated by a gamma, which is introduced here and made open access to potential users. It is also discussed how the positron non-linearity can be obtained from the detector calibration with gamma sources using the results presented in this article.
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The Jiangmen Underground Neutrino Observatory (JUNO) is an experimental project designed to determine the neutrino mass ordering and probe the fundamental properties of the neutrino oscillations. The JUNO central detector is a spherical liquid scinti
llator detector with a diameter of 35.4 m and equipped with approximately 18,000 20-inch PMTs. A trigger threshold of 0.5 MeV can be easily achieved by using a common multiplicity trigger and can meet the requirements for measuring neutrino mass ordering. However, it is essential to further reduce the trigger threshold for detecting solar neutrinos and supernova neutrinos. A sophisticated trigger scheme is proposed to achieve a low energy threshold by reducing the level of low energy radioactivity and dark noise coincidence. With the new trigger scheme, the events rate of the central detector from different types of sources have been carefully studied by using a detailed detector simulation. It shows that the trigger threshold can be reduced to 0.2 MeV, or even 0.1 MeV, if the concentration of $^{14}$C in liquid scintillator can be well controlled.
The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters $sin^2 2theta_{12}$, $Delta m^2_{21}$ an
d $Delta m^2_{32}$ are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present a few machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: $sigma_E=3%$ at $E_text{vis}=1~text{MeV}$ for the energy and $sigma_{x,y,z}=10~text{cm}$ at $E_text{vis}=1~text{MeV}$ for the position.
The Jiangmen Neutrino Underground Observatory (JUNO) is a 20$,$kton liquid scintillator detector currently under construction near Kaiping in China. The physics program focuses on the determination of the neutrino mass hierarchy with reactor anti-neu
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