Data and computing for high energy physics experiments

被引:0
|
作者
Chen Gang [1 ]
机构
[1] Chinese Acad Sci, Inst High Energy Phys, Beijing 100049, Peoples R China
关键词
data analytics; big data; grid computing; high energy physics experiment; offline data;
D O I
10.1360/SSPMA-2021-0108
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
High energy physics (HEP), which is also known as particle physics, is the study of the fundamental building blocks of nature, the interactions of elementary particles, and the origin of the Universe. Large-scale HEP experiments can produce hundreds of petabytes of real and simulated data yearly. This capability has made HEP a major explorer of scientific computing in the last few decades. In this work, three computing tasks, i.e., event reconstruction, physics simulation, and data analysis, are introduced. Subsequently, data and computing challenges are demonstrated with different examples of HEP experiments. To address the challenges, the HEP community has been focusing on developing computing models in the last thirty years. The HEP-computing model has evolved from local centers with mainframes and workstations to distributed clusters and worldwide computing grids. Some technologies generated through HEP computing, such as WWW, have made a considerable impact on the world. In the concluding part of this work, the perspective of computing technologies in HEP is discussed.
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页数:10
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