CNN-based event classification for alpha-decay events in nuclear emulsion

TL;DR

This paper presents a CNN-based classifier that effectively distinguishes alpha-decay events in nuclear emulsion images, significantly reducing manual inspection effort and improving classification precision over previous methods.

Contribution

The study introduces a novel CNN-based approach for classifying alpha-decay events in nuclear emulsion, achieving higher precision and reducing human workload compared to prior techniques.

Findings

Achieved an Average Precision Score of 0.740 for alpha-decay event classification.

Reduced human inspection load by approximately 7 times.

Improved precision from 0.081 to 0.547 at similar recall levels.

Abstract

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.