Deep Learning and its Application to LHC Physics

2018/06/29 by Dan Guest, Kyle Cranmer, Daniel Whiteson · 4 citations
Physics and Astronomy · #hep-ex #hep-ph #physics.comp-ph #physics.data-an

paper · pdf · doi:10.1146/annurev-nucl-101917-021019

Posted with permission from the Annual Review of Nuclear and Particle Science, Volume 68. (c) 2018 by Annual Reviews, http://www.annualreviews.org

arxiv created 2018/06/29 · arxiv updated 2018/11/14

Abstract

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 are 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 LHC data, and discussion of future prospects and concerns.

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