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ReviewsNo Access

Machine and deep learning applications in particle physics

Dimitri Bourilkov

Dimitri Bourilkov

http://orcid.org/0000-0003-0260-4935

Physics Department, University of Florida, P.O. Box 118440, Gainesville, Florida 32611, USA

E-mail Address: dimi@ufl.edu

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https://doi.org/10.1142/S0217751X19300199Cited by:109 (Source: Crossref)

Abstract

The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced, and cutting-edge applications in the experimental and theoretical/phenomenological domains are highlighted. After describing the challenges in the application of these novel analysis techniques, the review concludes by discussing the interactions between physics and machine learning as a two-way street enriching both disciplines and helping to meet the present and future challenges of data-intensive science at the energy and intensity frontiers.

Keywords:

PACS: 07.05.Mh

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Vol. 34, No. 35

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History

Received 26 October 2019

Revised 10 December 2019

Accepted 16 December 2019

Published: 28 January 2020

Keywords