An online fast multi-track locating algorithm for high-resolution single-event effect test platform

To improve the efficiency and accuracy of single-event effect(SEE)research at the Heavy Ion Research Facility at Lanzhou,Hi’Beam-SEE must precisely localize the position at which each heavy ion hitting the integrated circuit(IC)causes SEE.In this study,we propose a fast multi-track location(FML)method based on deep learning to locate the position of each particle track with high speed and accuracy.FML can process a vast amount of data supplied by Hi’Beam-SEE online,revealing sensitive areas in real time.FML is a slot-based object-centric encoder-decoder structure in which each slot can learn the location information of each track in the image.To make the method more accurate for real data,we designed an algorithm to generate a simulated dataset with a distribution similar to that of the real data,which was then used to train the model.Extensive comparison experiments demonstrated that the FML method,which has the best performance on simulated datasets,has high accuracy on real datasets as well.In particular,FML can reach 238 fps and a standard error of 1.6237μm.This study discusses the design and performance of FML.