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A group of researchers from Shanghai Jiao Tong University and Peking University greatly improved the discrimination power of tracks from different particles passing through the gaseous detector with the help of deep convolutional neural networks. The work will help to improve the sensitivity of detection for the PandaX-III neutrinoless double beta decay experiment, and deepen our knowledge of the nature of neutrinos.
This work is published by Science China Physics, Mechanics & Astronomy (SCPMA) with the title "Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation." Hao Qiao, a master student from Peking University, is the first author.
Beta - Decay - Phenomenon - Electrons - Neutrinos
Double beta decay is a phenomenon in which two electrons and two neutrinos are emitted. The so called neutrinoless double beta decay, without the emission of neutrinos, is directly related to the nature of the neutrino itself, and has not been observed in any experiments. The process is only possible when neutrino is Majorana fermion, or, neutrino and anti-neutrino are the same. Scientists suspect that such property is also key to understanding the asymmetry between matter and antimatter in our universe.
Specially designed experiments might be able to find the rare neutrinoless double beta decay process. Among them, the PandaX-III experiment plans to search for the neutrinoless double beta decay of the Xe-136 isotope with a high pressure gaseous xenon time projection chamber. It will not only record the energy of the electrons produced in the process, but also take "snapshot" of their tracks in the detector, obtaining the projections on two mutually perpendicular planes parallel to the drifting direction. The features of the tracks can be used for the discrimination between desired signals and backgrounds. But the stochastic property makes it challenging to define and identify the features of neutrinoless double beta decay signals. One example of the simulated tracks...
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