Laser and Particle Beams, Volume. 2022, Issue 2, 3820671(2022)

Particles Detection System with CR-39 Based on Deep Learning

Gal Amit1,2, Idan Mosseri3, Ofir Even-Hen1, Nadav Schneider3, Elad Fisher3,4, Hanan Datz1, Eliahu Cohen2, and Noaz Nissim5
Author Affiliations
  • 1Radiation Safety Department Soreq Nuclear Research Center Yavne Israel
  • 2Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials Bar Ilan University Ramat Gan 5290002 Israel
  • 3Technology Division Soreq Nuclear Research Center Yavne Israel
  • 4Maritime Policy & Strategy Research Center (HMS) Hatter Department of Marine Technologies University of Haifa Haifa 3498838 Israel
  • 5Applied Physics Department Soreq Nuclear Research Center Yavne Israel
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    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.

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    Gal Amit, Idan Mosseri, Ofir Even-Hen, Nadav Schneider, Elad Fisher, Hanan Datz, Eliahu Cohen, Noaz Nissim. Particles Detection System with CR-39 Based on Deep Learning[J]. Laser and Particle Beams, 2022, 2022(2): 3820671

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    Paper Information

    Category: Research Articles

    Received: May. 10, 2022

    Accepted: Jun. 14, 2022

    Published Online: Sep. 15, 2022

    The Author Email:

    DOI:10.1155/2022/3820671

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