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

Abstract

A typical problem in low-level liquid scintillation (LS) counting is the identification of particles in the presence of a high background of and particles. Especially the occurrence of -and -pile-ups may prevent the unambiguous identification of an signal by commonly used analog electronics. In this case, pulseshape 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 liquidliquid extraction system SISAK in combination with LS counting. -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 , -and -pile up, and fission events. This paper describes the subsequent development and use of artificial neural networks (ANN) based on the method of "backpropagation of errors" to automatically distinguish between different pulse shapes. Such networks can "learn" pulse shapes and classify hitherto unknown pulses cor- rectly 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.

English Grammar Discrimination Training Network Model and Search Filtering

“…However, there are few empirical studies on the teaching and learning of English grammar. Foreign language major core journals in China on the knowledge of English grammar teaching and learning found that although some research, respectively, discusses the English grammar knowledge to the predictive power of pragmatic knowledge [8,9] ability to distinguish between training and development trend, English grammar [10], college English teachers and students grammar teaching and learning belief [11], and English grammar knowledge and English and English language skills such as the potential relationship [12], due to adopting the concept of English grammar knowledge or ability and measuring tools, testing the level of task difficulty and subjects of English language levels vary. e result is not the same.…”