Deep Learning and Its Application to LHC Physics
By Daniel Guest, Kyle Cranmer, Daniel Whiteson. Annual Review of Nuclear and Particle Science, 2018
438 citations recorded by the source corpus.
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.
Subfields: Nuclear and High Energy Physics
View Paper: https://www.semanticscholar.org/paper/1f135e98e867ffcde5b359e7b817bbe21f80cfce
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In Nuclear and High Energy Physics
- I. Moult
- Benjamin Nachman
- A. Larkoski
- Jonathan Shlomi
- David Shih
- Peter W. Battaglia
- Biagio Lucini
- Y. Nakai
Citations in the corpus
- Machine learning and the physical sciences (2019, neutral): “(Guest et al., 2018; Radovic et al., 2018)).”
- Searching for new physics with deep autoencoders (2018, background): “Applications of deep learning in high energy physics do not stop at classification tasks: pile-up removal [32], generative models [33] and many others (for a review and more references, we refer to [34]) have all been studied.”
- Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “The challenges here are manifold (Guest et al., 2018).”
- Graph neural networks in particle physics (2020, background): “[3] Dan Guest, Kyle Cranmer, and Daniel Whiteson.”
- Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, methodology): “For the reader interested in more specialized techniques, these can be supplemented by reviews and texts on QCD relevant for jet substructure calculations [10, 11, 12, 13, 14, 15, 16, 17], as well as several standard texts and reviews on deep learning [18, 19, 20] (for particle physics [21, 22]), an”