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Register a new userMuon reconstruction with a convolutional neural network in the JUNO detector
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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 muons. 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.
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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%.
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 photomultiplier 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 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 photomultiplier 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 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 phenomena, including supernova neutrinos, solar neutrinos, geo-neutrinos, atmosphere neutrinos, and so forth. The central detector of JUNO contains 20,000~tons of liquid scintillator (LS) and about 18,000 20-inch photomultiplier tubes (PMTs), which is the largest liquid scintillator one under construction in the world up today. The energy resolution is expected to be 3%/$sqrt{E(MeV)}$. To meet the requirements of the experiment, an algorithm of vertex reconstruction, which takes into account time and charge information of PMTs, has been developed by deploying the maximum likelihood method and well understanding the complicated optical processes in the liquid scintillator.
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure $CP$-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to $CP$-violating effects.
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