Image sorting of nuclear reactions recorded on CR-39 nuclear track detector using deep learning

Abstract

Deep learning has been utilized to trace nuclear reactions in the CR-39 nuclear track detector. Etch pit images on front and back surfaces of the CR-39 detector were obtained sequentially by moving the objective lens of a microscope, and merged to one image. This image merging makes it possible to combine information on the displacement of the position of the etch pits produced by single particle traversals through a CR-39 layer in a single image, thereby making it easier to recognize corresponding nuclear fragmentation reactions. Object detection based on deep learning has been applied to the merged image to identify nuclear fragmentation events for measurement of the total charge changing cross-section based on the number of incident particles (Nin) and the number of particles that passed through target without any nuclear reaction (Nout). We verified the accuracy (correct answer rate) of algorithms for extracting the two patterns of etch pit in merged images which corresponds to Nin and Nout using the learning curves expressed as a function of the number of trainings. Accuracy of Nin and Nout were found to be 97.3 ± 4.0% and 98.0 ± 4.0%, respectively. These results show that the object detection algorithm based on the deep learning can be a strong tool for CR-39 etch pit analysis.