Implementation of dynamic bias for neutron–photon pulse shape discrimination by using neural network classifiers

In order to accurately determine dose equivalent in radiation fields that include both neutrons and photons, it is necessary to measure the relative number of neutrons to photons and to characterize the energy dependence of the neutrons. The relationship between dose and dose equivalent begins to increase rapidly at about 100 keV; thus, it is necessary to separate neutrons from photons for neutron energies as low as about 100 keV in order to measure dose equivalent in a mixed radiation field that includes both neutrons and photons. Preceptron and back propagation neural networks that use pulse amplitude and pulse rise time information obtain separation of neutron and photons with about 5% error for neutrons with energies as low as 100 keV, and this is accomplished for neutrons with energies that range from 100 keV to several MeV. If the ratio of neutrons to photons is changed by a factor of 10, the classification error increases to about 15% for the neural networks tested. A technique that uses the output from the preceptron as a priori for a Bayesian classifier is more robust to changes in the relative number of neutrons to photons, and it obtains a 5% classification error when this ratio is changed by a factor of ten. Results from this research demonstrate that it is feasible to use commercially available instrumentation in combination with artificial intelligence techniques to develop a practical detector that will accurately measure dose equivalent in mixed neutron–photon radiation fields.