期刊
ANNUAL REVIEW OF NUCLEAR AND PARTICLE SCIENCE, VOL 68
卷 68, 期 -, 页码 161-181出版社
ANNUAL REVIEWS
DOI: 10.1146/annurev-nucl-101917-021019
关键词
deep learning; LHC; machine learning; particle physics
Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis arc explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LIIC data, and discussion of future prospects and concerns.
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