Labeling strategy to improve neutron/gamma discrimination with organic scintillator

Abstract

Organic scintillators are widely used for neutron/gamma detection. Pulse shape discrimination algorithms have been commonly used to discriminate the detected radiations. These algorithms have several limits, in particular with plastic scintillator which has lower discrimination ability, compared to liquid scintillator. Recently, machine learning (ML) models have been explored to enhance discrimination performance. Nevertheless, obtaining an accurate ML model or evaluating any discrimination approach requires a reference neutron dataset. The preparation of this is challenging because neutron sources are also gamma-ray emitters. Therefore, this paper proposes a pipeline to prepare clean labeled neutron/gamma datasets acquired by an organic scintillator. The method is mainly based on a Time of Flight setup and Tail-to-Total integral ratio (TTTratio) discrimination algorithm. In the presented case, EJ276 plastic scintillator and 252Cf source were used to implement the acquisition chain. The results showed that this process can identify and remove mislabeled samples in the entire ToF spectrum, including those that contribute to peak values. Furthermore, the process cleans ToF dataset from pile-up events, which can significantly impact experimental results and the conclusions extracted from them.

Keywords

• Neutron gamma discrimination
• Time-of-flight
• Labeling approach
• Plastic scintillator
• Organic scintillator
• Machine learning