Particles Detection System with CR-39 Based on Deep Learning

Abstract

We present a novel method that we call FAINE, fast artificial intelligence neutron detection system. FAINE automatically classifies tracks of fast neutrons on CR-39 detectors using a deep learning model. This method was demonstrated using a LANDAUER Neutrak® fast neutron dosimetry system, which is installed in the External Dosimetry Laboratory (EDL) at Soreq Nuclear Research Center (SNRC). In modern fast neutron dosimetry systems, after the preliminary stages of etching and imaging of the CR-39 detectors, the third stage uses various types of computer vision systems combined with a manual revision to count the CR-39 tracks and then convert them to a dose in mSv units. Our method enhances these modern systems by introducing an innovative algorithm, which uses deep learning to classify all CR-39 tracks as either real neutron tracks or any other sign such as dirt, scratches, or even cleaning remainders. This new algorithm makes the third stage of manual CR-39 tracks revision superfluous and provides a completely repeatable and accurate way of measuring either neutrons flux or dose. The experimental results show a total accuracy rate of 96.7% for the true positive tracks and true negative tracks detected by our new algorithm against the current method, which uses computer vision followed by manual revision. This algorithm is now in the process of calibration for both alpha-particles detection and fast neutron spectrometry classification and is expected to be very useful in analyzing results of proton-boron11 fusion experiments. Being fully automatic, the new algorithm will enhance the quality assurance and effectiveness of external dosimetry, will lower the uncertainty for the reported dose measurements, and might also enable lowering the system’s detection threshold.