Application of convolutional neural networks for pile up correction in single particle counting

An exploration into the application of machine learning (ML) approaches to identify pile-ups and correct them in single particle counters at the GSI Helmholtz Centre for Heavy Ion Research in presented. About 100000 particle pulse data from various spills were manually labelled and a convolutional neural network (CNN) was developed to accurately count the number of particles without domain-specific knowledge. This contribution represents proof-of-work for a fast error free automated particle counting system. The identified algorithm was developed with a perspective of implementation into an FPGA.