Speaker
Description
Jiangmen Underground Neutrino Observatory (JUNO), located at the southern part of China, will be the world’s largest liquid scintillator(LS) detector. Equipped with 20 kton LS, 17623 20-inch PMTs and 25600 3-inch PMTs, JUNO will provide a unique apparatus to probe the mysteries of neutrinos, particularly the neutrino mass ordering puzzle. One of the challenges for JUNO is the high precision vertex and energy reconstruction for reactor neutrino events. This talk will cover both traditional event reconstruction algorithms based on likelihood functions and novel algorithms utilizing machine learning techniques in JUNO.
Significance
This abstract will give an overview of the progress of the event reconstruction in JUNO in the past two years. Most of the materials are extracted from our recent papers listed in the References below.
The idea of treating each individual PMT as a pixel and the ensemble of the time/charge information of hundreds and thousands of PMTs as images, then feeding these images to DNN to reconstruct the event was applied to a large LS detector (JUNO in this case) for the first time. It should be applicable to other experiments using large number of PMTs. This topic also presents unique challenges which might be resolved with image denoising/segmentation or data augmentation utilizing Deep Learning techniques. In addition, the traditional reconstruction algorithms are derived using calibration data, which do not depend on Monte Carlo simulation at all. These algorithms also provide a rather accurate charge and time response of PMTs in a realistic large detector, especially near the detector edge region. These ideas/methods could also shed lights on other similar experiments or detectors.
References
“Improving the energy uniformity for large liquid scintillator detectors”, Nucl.Instrum.Meth.A 1001 (2021) 165287
“Event vertex and time reconstruction in large volume liquid scintillator detector”,NUCL SCI TECH (2021)32:49
“Vertex and Energy Reconstruction in JUNO with Machine Learning Methods” Nucl.Instrum.Meth.A 1010 (2021) 165527
| Speaker time zone | Compatible with Asia |
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