Optimization of convolutional neural networks for background suppression in the PandaX-III experiment

Shangning Xia, Suizhi Huang, Kexin Xu, Tao Li,Xun Chen,Ke Han,Shaobo Wang

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS(2023)

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摘要
The tracks recorded by a gaseous detector provide a possibility for charged particle identification. For searching the neutrinoless double beta decay events of 136Xe in the PandaX-III experiment, we optimized the convolutional neural network based on the Monte Carlo simulation data to improve the signal-background discrimination power. EfficientNet is chosen as the baseline model and the optimization is performed by tuning the hyperparameters. In particular, the maximum discrimination power is achieved by optimizing the channel number of the top convolutional layer. In comparison with our previous work, the significance of discrimination has been improved by similar to 70%.
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关键词
neutrinoless double beta decay,background suppression,time projection chamber,track,convolutional neural network
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