Digital liquid-scintillation counting and effective pulse-shape discrimination with artificial neural networks

A typical problem in low-level liquid scintillation (LS) counting is the identification of alpha particles in the presence of a high background of beta and gamma particles. Especially the occurrence of beta-beta and beta-gamma pile-ups may prevent the unambiguous identification of an alpha signal by commonly used analog electronics. In this case, pulse-shape discrimination (PSD) and pile-up rejection (PUR) units show an insufficient performance. This problem was also observed in own earlier experiments on the chemical behaviour of transactinide elements using the liquid-liquid extraction system SISAK in combination with LS counting alpha-particle signals from the decay of the transactinides could not be unambiguously assigned. However, the availability of instruments for the digital recording of LS pulses changes the situation and provides possibilities for new approaches in the treatment of LS pulse shapes. In a SISAK experiment performed at PSI, Villigen, a fast transient recorder, a PC card with oscilloscope characteristics and a sampling rate of 1 giga samples s(-1) (1 ns per point), was used for the first time to record LS signals. It turned out, that the recorded signals were predominantly alpha, beta-beta and beta-gamma pile up, and fission events. This paper describes the subsequent development and use of artificial neural networks (ANN) based on the method of "back-propagation of errors" to automatically distinguish between different pulse shapes. Such networks can "learn" pulse shapes and classify hitherto unknown pulses correctly after a learning period. The results show that ANN in combination with fast digital recording of pulse shapes can be a powerful tool in LS spectrometry even at high background count rates