论文标题
基于CNN的基于核乳液中α-Decay事件的事件分类
CNN-based event classification for alpha-decay events in nuclear emulsion
论文作者
论文摘要
我们开发了一种有效的分类器，该分类器使用卷积神经网络（CNN）对核乳液中各种顶点样物体的α-末期事件进行分类。乳液中的Alpha-Decay事件是每个乳液片中轨道长度和动能之间关系的标准校准源。我们使用15,885张类似顶点的对象的图像培训了CNN，其中包括906个alpha-decay事件，并使用46,948张图像的数据集进行了测试，其中包括255个Alpha-Decay事件。通过调整CNN的超参数，训练有素的模型的平均精度得分为0.740 +/- 0.009，用于测试数据集。对于获得的模型，可以根据精度和召回之间的平衡任意调整分类的歧视阈值。对于同一数据集，使用先前的方法使用先前方法的分类和回忆分别为0.081 +/- 0.006和0.788 +/- 0.056。相比之下，当设置了相似的召回率为0.788时，开发的分类器的精度为0.547 +/- 0.025。与没有CNN的前者的估计负载相比，开发的CNN方法将分类后的人载量减少了约1/7。
We developed an efficient classifier that sorts alpha-decay events from various vertex-like objects in nuclear emulsion using a convolutional neural network (CNN). Alpha-decay events in the emulsion are standard calibration sources for the relation between the track length and kinetic energy in each emulsion sheet. We trained the CNN using 15,885 images of vertex-like objects including 906 alpha-decay events and tested it using a dataset of 46,948 images including 255 alpha-decay events. By tuning the hyperparameters of the CNN, the trained models achieved an Average Precision Score of 0.740 +/- 0.009 for the test dataset. For the model obtained, a discrimination threshold of the classification can be arbitrarily adjusted according to the balance between the precision and recall. The precision and recall of the classification using previous method without a CNN were 0.081 +/- 0.006 and 0.788 +/- 0.056, respectively, for the same dataset. By contrast, the developed classifier obtained a precision of 0.547 +/- 0.025 when a similar recall value of 0.788 was set. The developed CNN method reduced the human load for further visual inspection after the classification by approximately 1/7 compared to the estimated load of the former method without a CNN.
