A Variational Autoencoder for Minimally-Supervised Pulse Shape Discrimination

We propose a novel approach based on variational autoencoder with Gaussian mixture latent space (GMVAE) to address the challenging problem of pulse shape discrimination (PSD) in organic scintillators, in the presence of pile up. Unlike deterministic charge integration, which is very sensitive to pulse-processing parameters, the GMVAE performances are robust against variations of the hyperparameters. When compared to other supervised machine learning methods, GMVAE requires the fewest training pulses (100) to achieve a classification accuracy within 2% of its optimum performance, i.e., 98.3% accuracy. GMVAE exhibited excellent classification despite the difference in energy spectra between the training and test data sets, which were 14.1 MeV neutron pulses and 239PuBe pulses, respectively. While requiring minimum supervision, GMVAE showed superior PSD performances compared to both deterministic and supervised machine learning approaches. GMVAE is hence particularly suitable for real-time pulse classification, where expert labeling is unavailable and fine tuning of the discrimination parameters impractical.