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تسجيل مستخدم جديدMuon reconstruction with a geometrical model in JUNO
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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-neutrinos. For this purpose, JUNO is located 650$,$m underground with a distance of 53$,$km to two nuclear power plants. As a result, it is exposed to a muon flux that requires a precise muon reconstruction to make a veto of cosmogenic backgrounds viable. Established muon tracking algorithms use time residuals to a track hypothesis. We developed an alternative muon tracking algorithm that utilizes the geometrical shape of the fastest light. It models the full shape of the first, direct light produced along the muon track. From the intersection with the spherical PMT array, the track parameters are extracted with a likelihood fit. The algorithm finds a selection of PMTs based on their first hit times and charges. Subsequently, it fits on timing information only. On a sample of through-going muons with a full simulation of readout electronics, we report a spatial resolution of 20$,$cm of distance from the detectors center and an angular resolution of 1.6$,^{circ}$ over the whole detector. Additionally, a dead time estimation is performed to measure the impact of the muon veto. Including the step of waveform reconstruction on top of the track reconstruction, a loss in exposure of only 4% can be achieved compared to the case of a perfect tracking algorithm. When including only the PMT time resolution, but no further electronics simulation and waveform reconstruction, the exposure loss is only 1%.
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The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine the neutrino mass ordering and measure neutrino oscillation parameters. A precise muon reconstruction is crucial to reduce one of the major backgrounds induced by cosmic mu
ons. This article proposes a novel muon reconstruction method based on convolutional neural network (CNN) models. In this method, the track information reconstructed by the top tracker is used for network training. The training dataset is augmented by applying a rotation to muon tracks to compensate for the limited angular coverage of the top tracker. The muon reconstruction with the CNN model can produce unbiased tracks with performance that spatial resolution is better than 10 cm and angular resolution is better than 0.6 degrees. By using a GPU accelerated implementation a speedup factor of 100 compared to existing CPU techniques has been demonstrated.
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 Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment designed to measure the neutrino mass hierarchy using a central detector (CD), which contains 20 kton liquid scintillator (LS) surrounded by about 18,000 phot
omultiplier tubes (PMTs), located 700~m underground. The rate of cosmic muons reaching the JUNO detector is about 3~Hz and the muon induced neutrons and isotopes are major backgrounds for the neutrino detection. Reconstruction of the muon trajectory in the detector is crucial for the study and rejection of those backgrounds. This paper will introduce the muon tracking algorithm in the JUNO CD, with a least squares method of PMTs first hit time (FHT). Correction of the FHT for each PMT was found to be important to reduce the reconstruction bias. The spatial resolution and angular resolution are better than 3~cm and 0.4~degree, respectively, and the tracking efficiency is greater than 90% up to 16~m far from the detector center.
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment designed to measure the neutrino mass hierarchy using a central detector (CD), which contains 20 kton liquid scintillator (LS) surrounded by about 17,000 phot
omultiplier tubes (PMTs). Due to the large fiducial volume and huge number of PMTs, the simulation of a muon particle passing through the CD with the Geant4 toolkit becomes an extremely computation-intensive task. This paper presents a fast simulation implementation using a so-called voxel method: for scintillation photons generated in a certain LS voxel, the PMTs response is produced beforehand with Geant4 and then introduced into the simulation at runtime. This parameterisation method successfully speeds up the most CPU consuming process, the optical photons propagation in the LS, by a factor of 50. In the paper, the comparison of physics performance between fast and full simulation is also given.
The Jiangmen Underground Neutrino Observatory (JUNO) is designed to study neutrino mass hierarchy and measure three of the neutrino oscillation parameters with high precision using reactor antineutrinos. It is also able to study many other physical p
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