Application of artificial neural network for the ionizing radiation particle identification by the plastic scintillation detector response
The separation of ionizing radiation particles is an important and challenging task, especially regarding neutrons and gamma rays. The separation of neutron and gamma radiation is necessary for safeguard purposes and control of nuclear reactions. Standard mathematical models of pulse analysis work well in the presence of large energy transfer (>1 MeV) from the particle to the detector. However, the quality of the separation decreases as the amount of transferred energy lessens, making it impossible to determine the exact type of particle at a sufficiently low-energy level. In this work, an artificial neural network model was used to solve the problem of separation at low-energy levels. The supervised machine learning (ML) model was used to analyse pulses received from the polyethylene naphthalate (PEN) scintillation detector. Several data sets after the PEN exposure to neutron/gamma (combined 239PuBe and 238PuBe source), alpha (238Pu) and beta (90Sr/90Y) sources were used to train the models. The information obtained from the separation of neutrons and gamma particles was compared with the information obtained using standard pulses delayed fluorescence analysis methods. The obtained results showed that the model was able to separate particles in the fields of low- and high-energy transfer.