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

作　　者：Yu-Xiao Hu Hai-Bo Yang Hong-Lin Zhang Jian-Wei Liao Fa-Tai Mai Cheng-Xin Zhao 

机构地区：[1]Institute of modern physics,Chinese Academy of Sciences,Lanzhou 730000,China [2]Advanced Energy Science and Technology Guangdong Laboratory,Huizhou 516003,China [3]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Nuclear Science and Techniques》2023年第5期86-100,共15页核技术（英文）

基　　金：supported by the National Natural Science Foundation of China (Nos.U2032209,11975292,12222512);the National Key Research and Development Program of China (2021YFA1601300);the CAS“Light of West China”Program;the CAS Pioneer Hundred Talent Program;the Guangdong Major Project of Basic and Applied Basic Research (No.2020B0301030008)。

摘　　要：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.

关 键 词：Beam tracks Multi-track location Rapid location High accuracy Synthetic data Deep neural network Single-event effects Silicon pixel sensors HIRFL 

分 类 号：O572.2[理学—粒子物理与原子核物理]