核乳胶中alpha-decay事件的CNN事件分类
CNN-based event classification for alpha-decay events in nuclear emulsion
摘要：
我们开发了一个有效的分类器，使用卷积神经网络（CNN）从核乳胶中的各种顶点状物体中分类α衰变事件。乳胶中的α衰变事件是每个乳胶片中径迹长度和动能之间关系的标准校准源。我们使用15，885张顶点状物体的图像（包括906个α衰变事件）训练了CNN，并使用46，948张图像（包括255个α衰变事件）的数据集对其进行了测试。通过调整CNN的超参数，训练的模型在测试数据集上的平均精度得分为0.740 +/- 0.009。对于所得到的模型，分类的区分阈值可以根据精确度和召回率之间的平衡来任意调整。对于相同的数据集，使用没有CNN的先前方法的分类的精确度和召回率分别为0.081 +/- 0.006和0.788 +/- 0.056。相比之下，开发的分类器获得了0.547 +/- 0.025的精度时，类似的召回值为0.788设置。与没有CNN的前一种方法的估计负荷相比，开发的CNN方法减少了分类后进一步目视检查的人力负荷约1/7。
英文摘要：
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.
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