论文标题
在Pandax-III实验中优化用于背景抑制的卷积神经网络
Optimization of convolutional neural networks for background suppression in the PandaX-III experiment
论文作者
论文摘要
气态检测器记录的轨道为带电颗粒识别提供了可能。为了在Pandax-III实验中搜索136XE的中性betaβ衰减事件，我们基于蒙特卡洛模拟数据优化了卷积神经网络，以提高信号背景歧视能力。选择有效网络作为基线模型，并通过调整超参数来执行优化。特别是，通过优化顶部卷积层的通道数来实现最大歧视能力。与我们以前的工作相比，歧视的重要性已提高了约70％。
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 about 70%.
