Linear and nonlinear frequency-domain parametric models trained on charge-normalized CLYC pulses match or beat existing neutron/gamma discriminators, especially at low sampling rates and under noise, when judged on adversarial hard examples.
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Towards energy-insensitive and robust neutron/gamma classification: A learning-based frequency-domain parametric approach
Linear and nonlinear frequency-domain parametric models trained on charge-normalized CLYC pulses match or beat existing neutron/gamma discriminators, especially at low sampling rates and under noise, when judged on adversarial hard examples.